From d28af3b02a0c82cf82a2136448734df3f719ed10 Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Mon, 27 Oct 2025 14:00:15 +0100
Subject: [PATCH 01/57] refactoring the code and cleaning up

---
 .../ERA5__lwt_rel_occurrence_1958-2014.png    |  Bin 0 -> 101971 bytes
 .../correlation_matrix_E-OBS_1958-2014.png    |  Bin 0 -> 131123 bytes
 .../lwt_1_ERA5__psl_mean_1958-2014.png        |  Bin 0 -> 111271 bytes
 ..._1_TaiESM1_r1i1p1f1_psl_mean_1950-2014.png |  Bin 0 -> 109468 bytes
 .../slwt_EOBS_4_ERA5__psl_mean_1958-2014.png  |  Bin 0 -> 102843 bytes
 doc/sphinx/source/recipes/index.rst           |    1 +
 .../source/recipes/recipe_weathertyping.rst   |  114 ++
 esmvaltool/config-references.yml              |   13 +
 .../diag_scripts/weathertyping/calc_utils.py  | 1074 +++++++++++++++++
 .../diag_scripts/weathertyping/plot_utils.py  |  408 +++++++
 .../weathertyping/weathertyping.py            |  315 +++++
 .../diag_scripts/weathertyping/wt_utils.py    |  538 +++++++++
 .../recipes/recipe_weathertyping_CMIP6.yml    |  310 +++++
 13 files changed, 2773 insertions(+)
 create mode 100755 doc/sphinx/source/recipes/figures/weathertyping/ERA5__lwt_rel_occurrence_1958-2014.png
 create mode 100755 doc/sphinx/source/recipes/figures/weathertyping/correlation_matrix_E-OBS_1958-2014.png
 create mode 100755 doc/sphinx/source/recipes/figures/weathertyping/lwt_1_ERA5__psl_mean_1958-2014.png
 create mode 100755 doc/sphinx/source/recipes/figures/weathertyping/lwt_1_TaiESM1_r1i1p1f1_psl_mean_1950-2014.png
 create mode 100755 doc/sphinx/source/recipes/figures/weathertyping/slwt_EOBS_4_ERA5__psl_mean_1958-2014.png
 create mode 100755 doc/sphinx/source/recipes/recipe_weathertyping.rst
 create mode 100644 esmvaltool/diag_scripts/weathertyping/calc_utils.py
 create mode 100644 esmvaltool/diag_scripts/weathertyping/plot_utils.py
 create mode 100644 esmvaltool/diag_scripts/weathertyping/weathertyping.py
 create mode 100644 esmvaltool/diag_scripts/weathertyping/wt_utils.py
 create mode 100644 esmvaltool/recipes/recipe_weathertyping_CMIP6.yml

diff --git a/doc/sphinx/source/recipes/figures/weathertyping/ERA5__lwt_rel_occurrence_1958-2014.png b/doc/sphinx/source/recipes/figures/weathertyping/ERA5__lwt_rel_occurrence_1958-2014.png
new file mode 100755
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HcmV?d00001

diff --git a/doc/sphinx/source/recipes/index.rst b/doc/sphinx/source/recipes/index.rst
index 77a9815107..2a6afd8124 100644
--- a/doc/sphinx/source/recipes/index.rst
+++ b/doc/sphinx/source/recipes/index.rst
@@ -67,6 +67,7 @@ Atmosphere
    recipe_radiation_budget
    recipe_aod_aeronet_assess
    recipe_surface_trace_gas
+   recipe_weathertyping
 
 Climate metrics
 ^^^^^^^^^^^^^^^
diff --git a/doc/sphinx/source/recipes/recipe_weathertyping.rst b/doc/sphinx/source/recipes/recipe_weathertyping.rst
new file mode 100755
index 0000000000..e36f2afff6
--- /dev/null
+++ b/doc/sphinx/source/recipes/recipe_weathertyping.rst
@@ -0,0 +1,114 @@
+.. _recipes_weathertyping:
+
+Lamb Weathertypes
+===================
+
+Overview
+--------
+
+A diagnostic to calculate Lamb weathertypes over a given region. Furthermore,
+correlations between weathertypes and precipitation patterns over a given area can be calculated
+and 'combined' or 'simplified' weathertypes can be derived. Additionally, mean fields, as well as
+anomalies and standard deviations can be plotted.
+
+
+Available recipes and diagnostics
+---------------------------------
+
+Recipes are stored in esmvaltool/recipes/
+
+    * recipe_weathertyping.yml
+
+Diagnostics are stored in esmvaltool/diag_scripts/weathertyping/
+
+    * weathertyping.py: calculate lamb and simplified WT, plot mean, anomalies and std for each WT for psl, tas, pr
+
+
+User settings in recipe
+-----------------------
+
+#. weathertyping.py
+
+   *Required settings for script*
+
+   *Optional settings for script*
+
+   * correlation_threshold: correlation threshold for selecting similar WT pairs, only needed if automatic_slwt==True and predefined_slwt==False. default=0.9
+   * rmse_threshold: rmse threshold for selecting similar WT pairs, only needed if automatic_slwt==True and predefined_slwt==False. default=0.002
+   * plotting: if true, create plots of means, anomalies and std for psl, tas, prcp
+   * automatic_slwt: if true, automatically combine WT with similar precipitation patterns over specified area (via thresholds of correlation and rmse OR via predefined_slwt)
+   * predefined_slwt: dictionary of mappings between weathertypes
+
+.. note::
+
+  predefined_slwt can be a dictionary where keys are slwt and the values are arrays of lwt OR where keys are lwt and values are slwt
+
+   *Required settings for variables*
+
+   *Optional settings for variables*
+
+   *Required settings for preprocessor*
+
+   *Optional settings for preprocessor*
+
+   *Color tables*
+
+
+Variables
+---------
+
+* psl (atmos, day, time longitude latitude)
+* tas (atmos, day, time longitude latitude)
+* tp (atmos, day, time longitude latitude)
+* pr (atmos, day, time longitude latitude)
+
+
+Observations and reformat scripts
+---------------------------------
+
+*Note: (1) obs4MIPs data can be used directly without any preprocessing;
+(2) see headers of reformat scripts for non-obs4MIPs data for download
+instructions.*
+
+* E-OBS: European Climate Assessment & Dataset gridded daily precipitation sum
+* ERA5: ECMWF reanalysis
+
+References
+----------
+
+* Maraun, D., Truhetz, H., & Schaffer, A. (2021). Regional climate model biases, their dependence on synoptic circulation biases and the potential for bias adjustment: A process-oriented evaluation of the Austrian regional climate projections. Journal of Geophysical Research: Atmospheres, 126, e2020JD032824. https://doi.org/10.1029/2020JD032824
+
+* Jones, P.D., Hulme, M. and Briffa, K.R. (1993), A comparison of Lamb circulation types with an objective classification scheme. Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
+
+Example plots
+-------------
+
+.. _fig_weathertyping_1:
+.. figure::  /recipes/figures/weathertyping/lwt_1_ERA5__psl_mean_1958-2014.png
+   :align:   center
+
+   PSL mean map of Lamb WT 1 for ERA5.
+
+.. _fig_weathertyping_2:
+.. figure::  /recipes/figures/weathertyping/lwt_1_TaiESM1_r1i1p1f1_psl_mean_1950-2014.png
+   :align:   center
+
+   PSL mean map of Lamb WT 1 for TaiESM1.
+
+.. _fig_weathertyping_3:
+.. figure::  /recipes/figures/weathertyping/slwt_EOBS_4_ERA5__psl_mean_1958-2014.png
+   :align:   center
+
+   PSL mean map of slwt_EOBS 4 for ERA5 (in this case combined Lamb WT 24 and 23).
+
+.. _fig_weathertyping_4:
+.. figure::  /recipes/figures/weathertyping/correlation_matrix_E-OBS_1958-2014.png
+   :align:   center
+
+   Heatmap of correlation values for Lamb WTs 1-27.
+
+.. _fig_weathertyping_5:
+.. figure::  /recipes/figures/weathertyping/ERA5__lwt_rel_occurrence_1958-2014.png
+   :align:   center
+
+   Stackplot of seasonal relative occureences of each WT for ERA5.
diff --git a/esmvaltool/config-references.yml b/esmvaltool/config-references.yml
index cda65ff656..1710804fcf 100644
--- a/esmvaltool/config-references.yml
+++ b/esmvaltool/config-references.yml
@@ -322,6 +322,11 @@ authors:
     name: Juckes, Martin
     institute: BADC, UK
     orcid:
+  jury_martin:
+    name: Jury, Martin
+    institute: WEGC, Austria
+    e-mail: martin.jury@uni-graz.at
+    orcid: https://orcid.org/0000-0003-0590-7843
   kadygrov_nikolay:
     name: Kadygrov, Nikolay
     institute: IPSL, France
@@ -355,6 +360,12 @@ authors:
     name: Krasting, John
     institute: NOAA, USA
     orcid: https://orcid.org/0000-0002-4650-9844
+  kroissenbrunner_thomas:
+    name: Kroissenbrunner, Thomas
+    institute: WEGC, Austria
+    email: thomas.kroissenbrunner@uni-graz.at
+    github: thomaskroi1996
+    orcid:
   kuehbacher_birgit:
     name: Kuehbacher, Birgit
     institute: DLR, Germany
@@ -881,6 +892,7 @@ projects:
   ipcc_ar6: IPCC AR6 WG1 contributions
   isenes3: EU H2020 Infrastructure for the European Network for Earth System Modelling - Phase 3
   primavera: EU H2020 project PRIMAVERA
+  preval: PREVAL ÖKS
   qa4ecv: QA4ECV
   russell_project: US Clivar, Ocean Carbon Biogeochemistry, ESGF, NOAA, NSF, NASA, US Antarctic program
   trr181: DFG Project TRR-181, Energy transfers in Atmosphere and Ocean
@@ -888,6 +900,7 @@ projects:
   usmile: ERC Synergy Grant USMILE
 
 
+
 realms:
   aerosol: aerosol
   atmos: atmosphere
diff --git a/esmvaltool/diag_scripts/weathertyping/calc_utils.py b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
new file mode 100644
index 0000000000..de55dfeee2
--- /dev/null
+++ b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
@@ -0,0 +1,1074 @@
+"""Utility functions for calculations."""
+
+# operating system manipulations (e.g. path constructions)
+import json
+import logging
+import os
+import warnings
+
+# to manipulate iris cubes
+import iris
+import iris.analysis.cartography
+
+# general imports
+import numpy as np
+import pandas as pd
+
+# local imports
+from plot_utils import plot_corr_rmse_heatmaps, plot_maps
+from wt_utils import (
+    check_mapping_dict_format,
+    get_mapping_dict,
+    get_provenance_record,
+    log_provenance,
+    map_lwt_to_slwt,
+    reverse_convert_dict,
+    write_corr_rmse_to_csv,
+    write_mapping_dict,
+)
+
+iris.FUTURE.datum_support = True
+
+logger = logging.getLogger(os.path.basename(__file__))
+
+# Ignoring a warning that is produced when selecting timesteps of a weathertype
+warnings.filterwarnings("ignore", ".*Collapsing a non-contiguous coordinate*")
+
+
+def calc_slwt_obs(
+    cfg: dict,
+    lwt: np.array,
+    cube: iris.cube.Cube,
+    dataset: str,
+    ancestors: list,
+    timerange: str,
+) -> np.array:
+    """Calculate simplified weathertypes for observation datasets based on \
+        precipitation patterns over specified area.
+
+    Args:
+        cfg (dict): Configuration dictionary from recipe
+        lwt (np.array): Array of Lamb WT
+        cube (iris.cube.Cube): preprocessor cube to keep time coordinate
+        dataset (str): Name of dataset
+        correlation_thresold (float): correlation_threshold
+        rmse_threshold (float): rsme_threshold
+        ancestors (list): list of ancestors
+
+    Returns
+        np.array: _description_
+    """
+    logger.info("Calculating simplified Lamb Weathertypes for %s", dataset)
+
+    work_dir = cfg.get("work_dir")
+    correlation_threshold = cfg.get("correlation_threshold")
+    rmse_threshold = cfg.get("rmse_threshold")
+    tcoord = cube.coord("time")
+
+    wt_data_prcp = []
+    for wt_ in range(1, 28):
+        target_indices = np.where(lwt == wt_)
+        if len(target_indices[0]) < 1:
+            logger.info(
+                "calc_slwt_obs - CAUTION: Skipped wt %s \
+                for dataset %s!",
+                wt_,
+                dataset,
+            )
+            continue
+        dates = [
+            tcoord.units.num2date(tcoord.points[i]) for i in target_indices
+        ]
+        if dataset == "E-OBS":
+            extracted_cube = cube[target_indices]
+        else:
+            extracted_cube = cube.extract(
+                iris.Constraint(time=lambda t, d=dates: t.point in d[0])
+            )
+        wt_cube_mean = extracted_cube.collapsed("time", iris.analysis.MEAN)
+        wt_data_prcp.append(wt_cube_mean.data.compressed())
+    selected_pairs = process_prcp_mean(
+        cfg,
+        wt_data_prcp,
+        correlation_threshold,
+        rmse_threshold,
+        dataset,
+        timerange,
+    )
+
+    with open(
+        f"{work_dir}/wt_selected_pairs_{dataset}.json", "w", encoding="utf-8"
+    ) as file:
+        json.dump(selected_pairs, file)
+
+    mapping_dict = get_mapping_dict(selected_pairs)
+
+    write_mapping_dict(work_dir, dataset, mapping_dict)
+
+    provenance_record = get_provenance_record(
+        "Lamb Weathertypes", ancestors, ["Lamb Weathertypes"], False, False
+    )
+
+    log_provenance(
+        f"{work_dir}/wt_selected_pairs_{dataset}", cfg, provenance_record
+    )
+
+    return map_lwt_to_slwt(lwt, mapping_dict)
+
+
+def calc_const():
+    """Calculate constants for weathertyping algorithm.
+
+    Eq. taken from: Jones, P.D., Hulme, M. and Briffa, K.R. (1993),
+    A comparison of Lamb circulation types with an objective classification
+    scheme.
+    Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
+
+    Returns
+        tuple: The four constants needed for WT calculation.
+    """
+    const1 = 1 / np.cos(45 * np.pi / 180)
+    const2 = np.sin(45 * np.pi / 180) / np.sin(40 * np.pi / 180)
+    const3 = np.sin(45 * np.pi / 180) / np.sin(50 * np.pi / 180)
+    const4 = 1 / (2 * np.cos(45 * np.pi / 180) ** 2)
+
+    return const1, const2, const3, const4
+
+
+def calc_westerly_flow(cube: iris.cube.Cube) -> np.array:
+    """Calculate the westerly flow over area.
+
+    Eq. taken from: Jones, P.D., Hulme, M. and Briffa, K.R. (1993),
+    A comparison of Lamb circulation types with an objective classification
+    scheme.
+    Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
+
+    Args:
+        cube (iris.cube.Cube): Cube of psl data.
+
+    Returns
+        np.array: westerly flow
+    """
+    return 1 / 2 * (cube.data[:, 1, 2] + cube.data[:, 1, 4]) - 1 / 2 * (
+        cube.data[:, 3, 2] + cube.data[:, 3, 4]
+    )
+
+
+def calc_southerly_flow(cube: iris.cube.Cube, const1: float) -> np.array:
+    """Calculate the southerly flow over area.
+
+    Eq. taken from: Jones, P.D., Hulme, M. and Briffa, K.R. (1993),
+    A comparison of Lamb circulation types with an objective classification
+    scheme.
+    Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
+
+    Args:
+        cube (iris.cube.Cube): Cube of psl data.
+        const1 (float): const1
+
+    Returns
+        np.array: southerly flow
+    """
+    return const1 * (
+        1
+        / 4
+        * (cube.data[:, 3, 4] + 2 * cube.data[:, 2, 4] + cube.data[:, 1, 4])
+        - 1
+        / 4
+        * (cube.data[:, 3, 2] + 2 * cube.data[:, 2, 2] + cube.data[:, 1, 2])
+    )
+
+
+def calc_resultant_flow(
+    westerly_flow: np.array, southerly_flow: np.array
+) -> np.array:
+    """Calculate the resultant flow.
+
+    Eq. taken from: Jones, P.D., Hulme, M. and Briffa, K.R. (1993),
+    A comparison of Lamb circulation types with an objective classification
+    scheme.
+    Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
+
+    Args:
+        westerly_flow (np.array): westerly flow.
+        southerly_flow (np.array): southerly flow
+
+    Returns
+        np.array: resultant flow
+    """
+    return (southerly_flow**2 + westerly_flow**2) ** (1 / 2)
+
+
+def calc_westerly_shear_velocity(
+    cube: iris.cube.Cube, const2: float, const3: float
+) -> np.array:
+    """Calculate westerly shear velocity.
+
+    Eq. taken from: Jones, P.D., Hulme, M. and Briffa, K.R. (1993),
+    A comparison of Lamb circulation types with an objective classification
+    scheme.
+    Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
+
+    Args:
+        cube (iris.cube.Cube): cube of psl data
+        const2 (float): const2
+        const3 (float): const3
+
+    Returns
+        np.array: westerly shear velocity
+    """
+    return const2 * (
+        1 / 2 * (cube.data[:, 0, 2] + cube.data[:, 0, 4])
+        - 1 / 2 * (cube.data[:, 2, 2] + cube.data[:, 2, 4])
+    ) - const3 * (
+        1 / 2 * (cube.data[:, 2, 2] + cube.data[:, 2, 4])
+        - 1 / 2 * (cube.data[:, 4, 2] + cube.data[:, 4, 4])
+    )
+
+
+def calc_southerly_shear_velocity(
+    cube: iris.cube.Cube, const4: float
+) -> np.array:
+    """Calculate southerly shear velocity.
+
+    Eq. taken from: Jones, P.D., Hulme, M. and Briffa, K.R. (1993),
+    A comparison of Lamb circulation types with an objective classification
+    scheme.
+    Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
+
+    Args:
+        cube (iris.cube.Cube): cube of psl data
+        const4 (float): const4
+
+    Returns
+        np.array: southerly shear velocity
+    """
+    return const4 * (
+        1
+        / 4
+        * (cube.data[:, 3, 6] + 2 * cube.data[:, 2, 6] + cube.data[:, 1, 6])
+        - 1
+        / 4
+        * (cube.data[:, 3, 4] + 2 * cube.data[:, 2, 4] + cube.data[:, 1, 4])
+        - 1
+        / 4
+        * (cube.data[:, 3, 2] + 2 * cube.data[:, 2, 2] + cube.data[:, 1, 2])
+        + 1
+        / 4
+        * (cube.data[:, 3, 0] + 2 * cube.data[:, 2, 0] + cube.data[:, 1, 0])
+    )
+
+
+def calc_total_shear_velocity(
+    westerly_shear_velocity: np.array, southerly_shear_velocity: np.array
+) -> np.array:
+    """Calculate total shear velocity.
+
+    Eq. taken from: Jones, P.D., Hulme, M. and Briffa, K.R. (1993),
+    A comparison of Lamb circulation types with an objective classification
+    scheme.
+    Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
+
+    Args:
+        westerly_shear_velocity (np.array): westerly shear velocity
+        southerly_shear_velocity (np.array): southerly shear velocity
+
+    Returns
+        np.array: total shear velocity
+    """
+    return westerly_shear_velocity + southerly_shear_velocity
+
+
+def wt_algorithm(cube: iris.cube.Cube, dataset: str) -> np.array:
+    """Algorithm to calculate Lamb weathertypes.
+
+    Eq. taken from: Jones, P.D., Hulme, M. and Briffa, K.R. (1993),
+    A comparison of Lamb circulation types with an objective classification
+    scheme.
+    Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
+
+    Args:
+        cube (iris.cube.Cube): PSL field of dataset
+        dataset (str): Name of dataset
+
+    Returns
+        np.array: Array of Lamb WT for each day
+    """
+    # lats and lons corresponding to datapoints
+    # 55, 5 -> 1
+    # 55, 15 -> 2
+    # 50, -5 -> 3
+    # 50, 5 -> 4
+    # 50, 15 -> 5
+    # 50, 25 -> 6
+    # 45, -5 -> 7
+    # 45, 5 -> 8
+    # 45, 15 -> 9
+    # 45, 25 -> 10
+    # 40, -5 -> 11
+    # 40, 5 -> 12
+    # 40, 15 -> 13
+    # 40, 25 -> 14
+    # 35, 5 -> 15
+    # 35, 15 -> 16
+
+    # lons: -5, 0, 5, 10, 15, 20, 25
+    # lats: 35, 40, 45, 50, 55
+
+    logger.info("Calculating Lamb Weathertypes for %s", dataset)
+
+    const1, const2, const3, const4 = calc_const()
+
+    # westerly flow
+    westerly_flow = calc_westerly_flow(cube)
+    # southerly flow
+    southerly_flow = calc_southerly_flow(cube, const1)
+    # resultant flow
+    total_flow = calc_resultant_flow(westerly_flow, southerly_flow)
+    # westerly shear vorticity
+    westerly_shear_velocity = calc_westerly_shear_velocity(
+        cube, const2, const3
+    )
+    # southerly shear vorticity
+    southerly_shear_velocity = calc_southerly_shear_velocity(cube, const4)
+    # total shear vorticity
+    total_shear_velocity = calc_total_shear_velocity(
+        westerly_shear_velocity, southerly_shear_velocity
+    )
+
+    weathertypes = np.zeros(len(total_shear_velocity))
+
+    for i, z_i in enumerate(total_shear_velocity):
+        direction = (
+            np.arctan(westerly_flow[i] / southerly_flow[i]) * 180 / np.pi
+        )  # deg
+        if southerly_flow[i] >= 0:
+            direction += 180  # deg
+
+        if direction < 0:
+            direction += 360  # deg
+
+        # Lamb pure directional type
+        if abs(z_i) < total_flow[i]:
+            if 337.5 <= direction or direction < 22.5:
+                weathertypes[i] = 1
+            elif 22.5 <= direction < 67.5:
+                weathertypes[i] = 2
+            elif 67.5 <= direction < 112.5:
+                weathertypes[i] = 3
+            elif 112.5 <= direction < 157.5:
+                weathertypes[i] = 4
+            elif 157.5 <= direction < 202.5:
+                weathertypes[i] = 5
+            elif 202.5 <= direction < 247.5:
+                weathertypes[i] = 6
+            elif 247.5 <= direction < 292.5:
+                weathertypes[i] = 7
+            elif 292.5 <= direction < 337.5:
+                weathertypes[i] = 8
+        # Lamb’s pure cyclonic and anticyclonic type
+        elif (2 * total_flow[i]) < abs(z_i):
+            if z_i > 0:
+                weathertypes[i] = 9
+
+            elif z_i < 0:
+                weathertypes[i] = 10
+        # Lambs’s synoptic/direction hybrid types
+        elif total_flow[i] < abs(z_i) < (2 * total_flow[i]):
+            if z_i > 0:
+                if 337.5 <= direction or direction < 22.5:
+                    weathertypes[i] = 11
+                elif 22.5 <= direction < 67.5:
+                    weathertypes[i] = 12
+                elif 67.5 <= direction < 112.5:
+                    weathertypes[i] = 13
+                elif 112.5 <= direction < 157.5:
+                    weathertypes[i] = 14
+                elif 157.5 <= direction < 202.5:
+                    weathertypes[i] = 15
+                elif 202.5 <= direction < 247.5:
+                    weathertypes[i] = 16
+                elif 247.5 <= direction < 292.5:
+                    weathertypes[i] = 17
+                elif 292.5 <= direction < 337.5:
+                    weathertypes[i] = 18
+
+            elif z_i < 0:
+                if 337.5 <= direction or direction < 22.5:
+                    weathertypes[i] = 19
+                elif 22.5 <= direction < 67.5:
+                    weathertypes[i] = 20
+                elif 67.5 <= direction < 112.5:
+                    weathertypes[i] = 21
+                elif 112.5 <= direction < 157.5:
+                    weathertypes[i] = 22
+                elif 157.5 <= direction < 202.5:
+                    weathertypes[i] = 23
+                elif 202.5 <= direction < 247.5:
+                    weathertypes[i] = 24
+                elif 247.5 <= direction < 292.5:
+                    weathertypes[i] = 25
+                elif 292.5 <= direction < 337.5:
+                    weathertypes[i] = 26
+        # light indeterminate flow, corresponding to Lamb’s unclassified type U
+        elif abs(z_i) < 6 and total_flow[i] < 6:
+            weathertypes[i] = 27
+
+    return weathertypes
+
+
+def calc_lwt_slwt_model(
+    cfg: dict,
+    cube: iris.cube.Cube,
+    data_info: dict,
+    predefined_slwt: bool | dict,
+):
+    """Calculate Lamb WT and simplified WT for model data.
+
+    Args:
+        cfg (dict): Configuration dicitonary from recipe
+        cube (iris.cube.Cube): PSL field of dataset
+        data_info (dict): Dictionary with info to dataset
+        predefined_slwt (bool | dict): If False, automatic_slwt will be used.
+        If dict, this mapping dict will be used.
+        (see recipe option predefined_slwt)
+    """
+    work_dir = cfg.get("work_dir")
+    dataset = data_info.get("dataset")
+    preproc_path = data_info.get("preproc_path")
+    output_file_path = data_info.get("output_file_path")
+    ensemble = data_info.get("ensemble", "")
+    timerange = data_info.get("timerange")
+    driver = data_info.get("driver", "")
+    if driver != "":
+        driver = f"_{driver}"
+
+    if not os.path.exists(f"{work_dir}/{output_file_path}"):
+        os.makedirs(f"{work_dir}/{output_file_path}")
+
+    lwt = wt_algorithm(cube, dataset)
+
+    tcoord = cube.coord("time")
+    time_points = tcoord.units.num2date(tcoord.points)
+
+    logger.info(
+        "Calculating simplified Lamb Weathertypes for %s %s", dataset, ensemble
+    )
+
+    if not predefined_slwt:
+        with open(
+            f"{work_dir}/wt_mapping_dict_ERA5.json", encoding="utf-8"
+        ) as file:
+            mapping_dict_era5_f = json.load(file)
+
+        with open(
+            f"{work_dir}/wt_mapping_dict_E-OBS.json", encoding="utf-8"
+        ) as file:
+            mapping_dict_eobs_f = json.load(file)
+        mapping_dict_era5 = reverse_convert_dict(mapping_dict_era5_f)
+        mapping_dict_eobs = reverse_convert_dict(mapping_dict_eobs_f)
+
+        slwt_era5 = map_lwt_to_slwt(lwt, mapping_dict_era5)
+        slwt_eobs = map_lwt_to_slwt(lwt, mapping_dict_eobs)
+    else:
+        predefined_slwt = check_mapping_dict_format(predefined_slwt)
+        write_mapping_dict(work_dir, "ERA5", predefined_slwt)
+        write_mapping_dict(work_dir, "E-OBS", predefined_slwt)
+        slwt_era5 = map_lwt_to_slwt(lwt, predefined_slwt)
+        slwt_eobs = map_lwt_to_slwt(lwt, predefined_slwt)
+
+    wt_cube = iris.cube.CubeList()
+    wt_cube.append(iris.cube.Cube(lwt, long_name="lwt"))
+    wt_cube.append(iris.cube.Cube(slwt_era5, long_name="slwt_era5"))
+    wt_cube.append(iris.cube.Cube(slwt_eobs, long_name="slwt_eobs"))
+
+    wt_cube[0].add_dim_coord(tcoord, 0)
+    wt_cube[1].add_dim_coord(tcoord, 0)
+    wt_cube[2].add_dim_coord(tcoord, 0)
+
+    iris.save(
+        wt_cube,
+        f"{work_dir}/{output_file_path}/{dataset}{driver}_"
+        f"{ensemble}_{timerange}.nc",
+    )
+
+    # write to csv file
+    d = {
+        "date": time_points[:],
+        "lwt": np.int8(lwt),
+        "slwt_ERA5": np.int8(slwt_era5),
+        "slwt_EOBS": np.int8(slwt_eobs),
+    }
+    df = pd.DataFrame(data=d)
+    df.to_csv(
+        f"{work_dir}/{output_file_path}/{dataset}{driver}_{ensemble}_"
+        f"{timerange}.csv",
+        index=False,
+    )
+
+    ancestors = [
+        preproc_path,
+        f"{work_dir}/wt_mapping_dict_ERA5.json",
+        f"{work_dir}/wt_mapping_dict_E-OBS.json",
+    ]
+    provenance_record = get_provenance_record(
+        "Lamb Weathertypes", ancestors, ["Lamb Weathertypes"], False, False
+    )
+
+    log_provenance(
+        f"{work_dir}/{output_file_path}/{dataset}_{ensemble}",
+        cfg,
+        provenance_record,
+    )
+
+
+def rmse(subarray1: np.array, subarray2: np.array) -> np.array:
+    """Calculate rsme.
+
+    Args:
+        subarray1 (np.array): array1
+        subarray2 (np.array): array2
+
+    Returns
+        np.array: rsme array
+    """
+    return np.sqrt(np.mean((subarray1 - subarray2) ** 2))
+
+
+def process_prcp_mean(
+    cfg: dict,
+    data: np.array,
+    correlation_threshold: float,
+    rmse_threshold: float,
+    dataset: str,
+    timerange: str,
+) -> list:
+    """Process precipitation fields.
+
+    Specified area to get a list of selected pairs of weathertypes
+    with the highest correlation (higher than correlation_threshold)
+    and smallest RSME (smaller than rsme_threshold) for
+    further processing and simplifying the WT.
+
+    Args:
+        cfg (dict): Configuration dictionary from recipe
+        data (np.array): Precipitation data
+        correlation_threshold (float): Correlation threshold
+        rmse_threshold (float): RMSE threshold
+        dataset (str): Name of dataset
+
+    Returns
+        list: Selected pairs of WT. This is passed to get_mapping_dict
+    """
+    logger.info("Calculating corr and rsme matrices for %s", dataset)
+
+    selected_pairs = []
+    pattern_correlation_matrix = np.ma.corrcoef(data)
+    n = len(data)
+    rmse_matrix = np.zeros((n, n))
+
+    for i in range(n):
+        for j in range(i + 1, n):
+            rmse_matrix[i][j] = rmse(data[i], data[j])
+            rmse_matrix[j][i] = rmse_matrix[i][j]
+            if (
+                pattern_correlation_matrix[i][j] >= correlation_threshold
+                and rmse_matrix[i][j] <= rmse_threshold
+            ):
+                selected_pairs.append(
+                    (
+                        (i + 1, j + 1),
+                        pattern_correlation_matrix[i][j],
+                        rmse_matrix[i][j],
+                    )
+                )
+
+    # write matrices to csv
+    write_corr_rmse_to_csv(
+        cfg, pattern_correlation_matrix, rmse_matrix, dataset
+    )
+    # plot heatmaps for matrices
+    plot_corr_rmse_heatmaps(
+        cfg, pattern_correlation_matrix, rmse_matrix, dataset, timerange
+    )
+
+    return selected_pairs
+
+
+def calc_wt_means(
+    cfg: dict,
+    cube: iris.cube.Cube,
+    wt_cubes: iris.cube.CubeList,
+    data_info: dict,
+):
+    """Calculate means of fields of each weathertype.
+
+    Args:
+        cfg (dict): Configuration dictionary from recipe
+        cube (iris.cube.Cube): Cube with variable data
+        wt_cubes (iris.cube.CubeList): List of cubes of lwt, slwt_ERA5
+                                        and slwt_EOBS
+        data_info (dict): Dictionary with info to dataset
+        preproc_path (str): Ancestor path
+    """
+    dataset = data_info.get("dataset")
+    var_name = data_info.get("var")
+    wt_string = data_info.get("wt_string")
+    preproc_path = data_info.get("preproc_path")
+    ensemble = data_info.get("ensemble")
+    timerange = data_info.get("timerange")
+    driver = data_info.get("driver", "")
+    if driver != "":
+        driver = f"_{driver}"
+
+    logger.info("Calculating %s %s means for %s", dataset, var_name, wt_string)
+
+    work_dir = cfg.get("work_dir")
+
+    num_slwt = 0
+    target_indices = []
+    lwt, slwt_eobs, slwt_era5 = [], [], []
+
+    if wt_string == "slwt_ERA5":
+        slwt_era5_cube = wt_cubes[1]
+        tcoord = slwt_era5_cube.coord("time")
+        slwt_era5 = slwt_era5_cube.data[:]
+        num_slwt = len(np.unique(slwt_era5))
+    elif wt_string == "slwt_EOBS":
+        slwt_eobs_cube = wt_cubes[2]
+        tcoord = slwt_eobs_cube.coord("time")
+        slwt_eobs = slwt_eobs_cube.data[:]
+        num_slwt = len(np.unique(slwt_eobs))
+    elif wt_string == "lwt":
+        lwt_cube = wt_cubes[0]
+        tcoord = lwt_cube.coord("time")
+        lwt = lwt_cube.data[:]
+
+    if "slwt" in wt_string:
+        for wt in range(1, num_slwt + 1):
+            if wt_string == "slwt_ERA5":
+                target_indices = np.where(slwt_era5 == wt)
+            elif wt_string == "slwt_EOBS":
+                target_indices = np.where(slwt_eobs == wt)
+            else:
+                logger.info("WT_STRING not supported!")
+            if len(target_indices[0]) < 1:
+                logger.info(
+                    "calc_wt_means - CAUTION: Skipped %s %s \
+                    for dataset %s!",
+                    wt_string,
+                    wt,
+                    dataset,
+                )
+                continue
+            dates = [
+                tcoord.units.num2date(tcoord.points[i]) for i in target_indices
+            ]
+            extracted_cube = cube.extract(
+                iris.Constraint(time=lambda t, d=dates: t.point in d[0])
+            )
+            wt_cube_mean = extracted_cube.collapsed("time", iris.analysis.MEAN)
+            plot_maps(wt, cfg, wt_cube_mean, data_info, "mean")
+    elif wt_string == "lwt":
+        for wt in range(1, 28):
+            target_indices = np.where(lwt == wt)
+            if len(target_indices[0]) < 1:
+                logger.info(
+                    "calc_wt_means - CAUTION: Skipped lwt %s \
+                    for dataset %s!",
+                    wt,
+                    dataset,
+                )
+                continue
+            dates = [
+                tcoord.units.num2date(tcoord.points[i]) for i in target_indices
+            ]
+            extracted_cube = cube.extract(
+                iris.Constraint(time=lambda t, d=dates: t.point in d[0])
+            )
+            wt_cube_mean = extracted_cube.collapsed("time", iris.analysis.MEAN)
+            plot_maps(wt, cfg, wt_cube_mean, data_info, "mean")
+    else:
+        logger.info("WT_STRING NOT SUPPORTED.")
+
+    ancestors = [f"{preproc_path}", f"{work_dir}/ERA5.nc"]
+    provenance_record = get_provenance_record(
+        f"{var_name} means for \
+                                              {wt_string}",
+        ancestors,
+        [var_name],
+        ["map"],
+        ["mean"],
+    )
+
+    local_path = f"{cfg.get('plot_dir')}/mean"
+
+    log_provenance(
+        f"{local_path}/{wt_string}_{wt}{driver}_"
+        f"{dataset}_{ensemble}"
+        f"_{var_name}_mean_{timerange}",
+        cfg,
+        provenance_record,
+    )
+
+
+def calc_wt_anomalies(
+    cfg: dict,
+    cube: iris.cube.Cube,
+    wt_cubes: iris.cube.CubeList,
+    data_info: dict,
+):
+    """Calculate anomalies of fields of each weathertype.
+
+    Args:
+        cfg (dict): Configuration dictionary from recipe
+        cube (iris.cube.Cube): Cube with variable data
+        wt_cubes (iris.cube.CubeList): List of cubes of lwt, slwt_ERA5
+                                        and slwt_EOBS
+        data_info (dict): Dictionary with info to dataset
+        preproc_path (str): Ancestor path
+    """
+    work_dir = cfg.get("work_dir")
+    dataset = data_info.get("dataset")
+    var_name = data_info.get("var_name")
+    wt_string = data_info.get("wt_string")
+    preproc_path = data_info.get("preproc_path")
+    ensemble = data_info.get("ensemble")
+    timerange = data_info.get("timerange")
+
+    logger.info(
+        "Calculating %s %s anomalies for %s", dataset, var_name, wt_string
+    )
+
+    target_indices = []
+    lwt, slwt_eobs, slwt_era5 = [], [], []
+
+    if wt_string == "slwt_ERA5":
+        slwt_era5_cube = wt_cubes[1]
+        tcoord = slwt_era5_cube.coord("time")
+        slwt_era5 = slwt_era5_cube.data[:]
+    elif wt_string == "slwt_EOBS":
+        slwt_eobs_cube = wt_cubes[2]
+        tcoord = slwt_eobs_cube.coord("time")
+        slwt_eobs = slwt_eobs_cube.data[:]
+    elif wt_string == "lwt":
+        lwt_cube = wt_cubes[0]
+        tcoord = lwt_cube.coord("time")
+        lwt = lwt_cube.data[:]
+
+    num_slwt_era5 = len(np.unique(slwt_era5))
+    num_slwt_eobs = len(np.unique(slwt_eobs))
+
+    if num_slwt_eobs != num_slwt_era5:
+        logger.info(
+            "calc_wt_anomalies - CAUTION: unequal number of \
+                    slwt_era5 (%s) and slwt_eobs (%s)!",
+            num_slwt_era5,
+            num_slwt_eobs,
+        )
+
+    if "slwt" in wt_string:
+        for wt in range(1, max(num_slwt_era5, num_slwt_eobs)):
+            if wt_string == "slwt_ERA5":
+                target_indices = np.where(slwt_era5 == wt)
+            elif wt_string == "slwt_EOBS":
+                target_indices = np.where(slwt_eobs == wt)
+            else:
+                logger.info("WT_STRING not supported!")
+            if len(target_indices[0]) < 1:
+                logger.info(
+                    "calc_wt_anomalies - CAUTION: Skipped wt %s \
+                    for dataset %s!",
+                    wt,
+                    dataset,
+                )
+                continue
+            dates = [
+                tcoord.units.num2date(tcoord.points[i]) for i in target_indices
+            ]
+            extracted_cube = cube.extract(
+                iris.Constraint(time=lambda t, d=dates: t.point in d[0])
+            )
+            wt_cube_mean = extracted_cube.collapsed("time", iris.analysis.MEAN)
+            plot_maps(
+                wt,
+                cfg,
+                cube.collapsed("time", iris.analysis.MEAN) - wt_cube_mean,
+                data_info,
+                "anomaly",
+            )
+    elif wt_string == "lwt":
+        for wt in range(1, 28):
+            target_indices = np.where(lwt == wt)
+            if len(target_indices[0]) < 1:
+                logger.info(
+                    "calc_wt_anomalies - CAUTION: Skipped wt %s \
+                    for dataset %s!",
+                    wt,
+                    dataset,
+                )
+                continue
+            dates = [
+                tcoord.units.num2date(tcoord.points[i]) for i in target_indices
+            ]
+            extracted_cube = cube.extract(
+                iris.Constraint(time=lambda t, d=dates: t.point in d[0])
+            )
+            wt_cube_mean = extracted_cube.collapsed("time", iris.analysis.MEAN)
+            plot_maps(
+                wt,
+                cfg,
+                cube.collapsed("time", iris.analysis.MEAN) - wt_cube_mean,
+                data_info,
+                "anomaly",
+            )
+    else:
+        logger.info("WT_STRING NOT SUPPORTED.")
+
+    ancestors = [f"{preproc_path}", f"{work_dir}/ERA5.nc"]
+    provenance_record = get_provenance_record(
+        f"{var_name} anomaly for \
+                                              {wt_string}",
+        ancestors,
+        [var_name],
+        ["map"],
+        ["anomaly"],
+    )
+
+    local_path = f"{cfg.get('plot_dir')}/anomaly"
+
+    log_provenance(
+        f"{local_path}/{wt_string}_{wt}_{dataset}_{ensemble}"
+        f"_{var_name}_anomaly__{timerange}",
+        cfg,
+        provenance_record,
+    )
+
+
+def calc_wt_std(
+    cfg: dict,
+    cube: iris.cube.Cube,
+    wt_cubes: iris.cube.CubeList,
+    data_info: dict,
+):
+    """Calculate standard deviation of fields of each weathertype.
+
+    Args:
+        cfg (dict): Configuration dictionary from recipe
+        cube (iris.cube.Cube): Cube with variable data
+        wt_cubes (iris.cube.CubeList): List of cubes of lwt, slwt_ERA5
+                                        and slwt_EOBS
+        data_info (dict): Dictionary with info to dataset
+        preproc_path (str): Ancestor path
+    """
+    work_dir = cfg.get("work_dir")
+    dataset = data_info.get("dataset")
+    var_name = data_info.get("var_name")
+    wt_string = data_info.get("wt_string")
+    preproc_path = data_info.get("preproc_path")
+    ensemble = data_info.get("ensemble")
+    timerange = data_info.get("timerange")
+
+    logger.info(
+        "Calculating %s %s standard deviation for %s",
+        dataset,
+        var_name,
+        wt_string,
+    )
+
+    target_indices = []
+    lwt, slwt_eobs, slwt_era5 = [], [], []
+
+    if wt_string == "slwt_ERA5":
+        slwt_era5_cube = wt_cubes[1]
+        tcoord = slwt_era5_cube.coord("time")
+        slwt_era5 = slwt_era5_cube.data[:]
+    elif wt_string == "slwt_EOBS":
+        slwt_eobs_cube = wt_cubes[2]
+        tcoord = slwt_eobs_cube.coord("time")
+        slwt_eobs = slwt_eobs_cube.data[:]
+    elif wt_string == "lwt":
+        lwt_cube = wt_cubes[0]
+        tcoord = lwt_cube.coord("time")
+        lwt = lwt_cube.data[:]
+
+    num_slwt_era5 = len(np.unique(slwt_era5))
+    num_slwt_eobs = len(np.unique(slwt_eobs))
+
+    if num_slwt_eobs != num_slwt_era5:
+        logger.info(
+            "calc_wt_std - CAUTION: unequal number of \
+                    slwt_era5 (%s) and slwt_eobs (%s)!",
+            num_slwt_era5,
+            num_slwt_eobs,
+        )
+
+    if "slwt" in wt_string:
+        for wt in range(1, max(num_slwt_era5, num_slwt_eobs)):
+            if wt_string == "slwt_ERA5":
+                target_indices = np.where(slwt_era5 == wt)
+            elif wt_string == "slwt_EOBS":
+                target_indices = np.where(slwt_eobs == wt)
+            else:
+                logger.info("WT_STRING not supported!")
+            if len(target_indices[0]) < 1:
+                logger.info(
+                    "calc_slwt_obs - CAUTION: Skipped wt %s \
+                    for dataset %s!",
+                    wt,
+                    dataset,
+                )
+                continue
+            dates = [
+                tcoord.units.num2date(tcoord.points[i]) for i in target_indices
+            ]
+            extracted_cube = cube.extract(
+                iris.Constraint(time=lambda t, d=dates: t.point in d[0])
+            )
+            wt_cube_std = extracted_cube.collapsed(
+                "time", iris.analysis.STD_DEV
+            )
+            plot_maps(wt, cfg, wt_cube_std, data_info, "stddev")
+    elif wt_string == "lwt":
+        for wt in range(1, 28):
+            target_indices = np.where(lwt == wt)
+            if len(target_indices[0]) < 1:
+                logger.info(
+                    "calc_wt_std - CAUTION: Skipped wt %s \
+                    for dataset %s!",
+                    wt,
+                    dataset,
+                )
+                continue
+            dates = [
+                tcoord.units.num2date(tcoord.points[i]) for i in target_indices
+            ]
+            extracted_cube = cube.extract(
+                iris.Constraint(time=lambda t, d=dates: t.point in d[0])
+            )
+            wt_cube_std = extracted_cube.collapsed(
+                "time", iris.analysis.STD_DEV
+            )
+            plot_maps(wt, cfg, wt_cube_std, data_info, "stddev")
+    else:
+        logger.info("WT_STRING NOT SUPPORTED.")
+
+    ancestors = [f"{preproc_path}", f"{work_dir}/ERA5.nc"]
+    provenance_record = get_provenance_record(
+        f"{var_name} standard \
+                                              deviation for \
+                                              {wt_string}",
+        ancestors,
+        [var_name],
+        ["map"],
+        ["stddev"],
+    )
+
+    local_path = f"{cfg.get('plot_dir')}/stddev"
+
+    log_provenance(
+        f"{local_path}/{wt_string}_{wt}_{dataset}_{ensemble}"
+        f"_{var_name}_stddev_{timerange}",
+        cfg,
+        provenance_record,
+    )
+
+
+def calc_lwt_model(
+    cfg: dict, cube: iris.cube.Cube, dataset: str, data_info: dict
+):
+    """Calculate lwt for model data.
+
+    Args:
+        cfg (dict): Configuration dictionary from recipe
+        cube (iris.cube.Cube): Cube to keep time coordinate
+        dataset (str): Name of dataset
+        data_info (dict): Dictionary with info to dataset
+    """
+    work_dir = cfg.get("work_dir")
+    dataset = data_info.get("dataset")
+    output_file_path = data_info.get("output_file_path")
+    ensemble = data_info.get("ensemble", "")
+    timerange = data_info.get("timerange")
+    driver = data_info.get("driver", "")
+    if driver != "":
+        driver = f"_{driver}"
+
+    if not os.path.exists(f"{work_dir}/{output_file_path}"):
+        os.makedirs(f"{work_dir}/{output_file_path}")
+
+    wt = wt_algorithm(cube, dataset)
+
+    tcoord = cube.coord("time")
+    time_points = tcoord.units.num2date(tcoord.points)
+
+    wt_cube = iris.cube.CubeList()
+    wt_cube.append(iris.cube.Cube(wt, long_name="lwt"))
+
+    logger.info(
+        "Writing Lamb Weathertype for %s \
+                to file %s.nc",
+        dataset,
+        dataset,
+    )
+
+    wt_cube[0].add_dim_coord(tcoord, 0)
+
+    iris.save(
+        wt_cube,
+        f"{work_dir}/{output_file_path}/{dataset}{driver}"
+        f"_{ensemble}_{timerange}.nc",
+    )
+
+    # write to csv file
+    d = {"date": time_points[:], "lwt": np.int8(wt)}
+    df = pd.DataFrame(data=d)
+    df.to_csv(
+        f"{work_dir}/{output_file_path}/{dataset}{driver}_{ensemble}_"
+        f"{timerange}.csv",
+        index=False,
+    )
+
+    ancestors = [
+        f"{work_dir}/wt_mapping_dict_ERA5.json",
+        f"{work_dir}/wt_mapping_dict_E-OBS.json",
+    ]
+    provenance_record = get_provenance_record(
+        "Lamb Weathertypes", ancestors, ["Lamb Weathertypes"], False, False
+    )
+
+    log_provenance(
+        f"{work_dir}/{output_file_path}/{dataset}_{ensemble}",
+        cfg,
+        provenance_record,
+    )
+
+
+def plot_means(
+    cfg: dict,
+    preproc_var: np.array,
+    wt_cubes: iris.cube.Cube,
+    data_info: dict,
+    only_lwt=False,
+):
+    """Wrapper function to plot various means/std/anomalies.
+
+    Args:
+        cfg (dict): cfg dictionary provided by recipe
+        preproc_var (np.array): variable to be plotted
+        wt_cubes (iris.cube.Cube): list of wt cubes
+        data_info (dict): dictionary with info to dataset
+        only_lwt (bool, optional): If True,
+        only Lamb weathertypes will be loaded. Defaults to False.
+        (useful for automatic_slwt = False)
+    """
+    if not only_lwt:
+        data_info["wt_string"] = "lwt"
+        calc_wt_means(cfg, preproc_var, wt_cubes, data_info)
+        data_info["wt_string"] = "slwt_ERA5"
+        calc_wt_means(cfg, preproc_var, wt_cubes, data_info)
+        data_info["wt_string"] = "slwt_EOBS"
+        calc_wt_means(cfg, preproc_var, wt_cubes, data_info)
+    else:
+        data_info["wt_string"] = "lwt"
+        calc_wt_means(cfg, preproc_var, wt_cubes, data_info)
diff --git a/esmvaltool/diag_scripts/weathertyping/plot_utils.py b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
new file mode 100644
index 0000000000..bbad9c8c24
--- /dev/null
+++ b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
@@ -0,0 +1,408 @@
+"""Utility functions for plotting."""
+
+# operating system manipulations (e.g. path constructions)
+import logging
+import os
+import warnings
+
+# plotting imports
+import cartopy.crs as ccrs
+import cartopy.feature as cfeature
+
+# to manipulate iris cubes
+import iris
+import iris.analysis.cartography
+import iris.plot as iplt
+import matplotlib.pyplot as plt
+import matplotlib.ticker as mticker
+
+# general imports
+import numpy as np
+import seaborn as sns
+
+# local imports
+from cartopy.mpl.gridliner import LATITUDE_FORMATTER, LONGITUDE_FORMATTER
+from matplotlib.colors import ListedColormap
+
+iris.FUTURE.datum_support = True
+
+logger = logging.getLogger(os.path.basename(__file__))
+
+# Ignoring a warning that is produced when selecting timesteps of a weathertype
+warnings.filterwarnings("ignore", ".*Collapsing a non-contiguous coordinate*")
+
+
+def generate_grayscale_hex_values(x):
+    """Generate grayscale values for plotting seasonal occurrence.
+
+    Args:
+        x (int): number of weathertypes
+
+    Returns
+        np.list: array with grescale values as hex
+    """
+    grayscale_values = np.linspace(0, 1, x)
+    grayscale_hex = [
+        f"#{int(value * 255):02x}{int(value * 255):02x}{int(value * 255):02x}"
+        for value in grayscale_values
+    ]
+
+    return grayscale_hex
+
+
+def plot_seasonal_occurrence(
+    cfg: dict, wt_cubes: iris.cube.Cube, data_info: dict
+):
+    """Plot relative monthly/seasonal occurrence of weathertypes.
+
+    Args:
+        cfg (dict): Configuration dict from recipe
+        wt_cubes (iris.cube.Cube): Cube with weathertypes
+        data_info (dict): Dictionary with relevant info to dataset
+    """
+    dataset_name = data_info.get("dataset")
+    timerange = data_info.get("timerange")
+    ensemble = data_info.get("ensemble", "")
+    driver = data_info.get("driver", "")
+
+    output_path = f"{cfg['plot_dir']}/seasonal_occurrence"
+
+    if not os.path.exists(f"{output_path}"):
+        os.makedirs(f"{output_path}")
+
+    month_list = [
+        "Jan",
+        "Feb",
+        "Mar",
+        "Apr",
+        "May",
+        "Jun",
+        "Jul",
+        "Aug",
+        "Sep",
+        "Oct",
+        "Nov",
+        "Dec",
+    ]
+
+    relative_occurrences = {}
+    # {wt_string: {month: {wt1: rel_occurrence, wt2: rel_occurrence, ....}}}
+    # first do absolute occurrence, then relative occurrence
+    for cube in wt_cubes:
+        month_dict = {}
+        for month in range(1, 13):
+            month_constraint = iris.Constraint(
+                time=iris.time.PartialDateTime(month=month)
+            )
+            array = cube.extract(month_constraint).data
+            unique, counts = np.unique(array, return_counts=True)
+            count_dict = dict(zip(unique, counts / sum(counts), strict=False))
+            month_dict[month] = count_dict
+        relative_occurrences[cube.long_name] = month_dict
+
+    x = month_list
+
+    for wt_string in relative_occurrences:
+        wt_numbers = max(
+            len(value)
+            for value in relative_occurrences.get(wt_string).values()
+        )
+        colors = generate_grayscale_hex_values(wt_numbers)
+        wt_stack = np.zeros((wt_numbers, 12))
+        for month, month_value in relative_occurrences.get(wt_string).items():
+            print(month_value)
+            for wt in month_value.keys():
+                print(month_value.get(wt))
+                wt_stack[np.int8(wt - 1), month - 1] = month_value.get(wt)
+
+        y = np.vstack(list(wt_stack))
+
+        # plot
+        _, ax_ = plt.subplots(figsize=(10, 10))
+
+        ax_.set_title(
+            f"Seasonal occurence of {wt_string}, \
+            {dataset_name}, {timerange}"
+        )
+
+        ax_.stackplot(x, y, colors=colors)
+
+        ax_.legend(
+            loc="upper center",
+            bbox_to_anchor=(0.5, -0.05),
+            fancybox=True,
+            shadow=True,
+            ncol=9,
+            labels=tuple(f"WT {i + 1}" for i in range(0, wt_numbers)),
+        )
+
+        ax_.set(
+            xlim=(0, 11),
+            xticks=np.arange(0, 12),
+            ylim=(0, 1),
+            yticks=np.arange(0, 1.1, 0.1),
+        )
+        ax_.set_xlabel("Month")
+        ax_.set_ylabel("Cumulative Relative occurrence")
+
+        plt.savefig(
+            f"{output_path}/{driver}{dataset_name}_{ensemble}_"
+            f"{wt_string}_rel_occurrence_{timerange}.png"
+        )
+        plt.savefig(
+            f"{output_path}/{driver}{dataset_name}_{ensemble}_"
+            f"{wt_string}_rel_occurrence_{timerange}.pdf"
+        )
+        plt.close()
+
+
+def plot_maps(
+    wt: np.array, cfg: dict, cube: iris.cube.Cube, data_info: dict, mode: str
+):
+    """Plot maps.
+
+    Args:
+        wt (np.array): WT for which statistic was calculated
+        cfg (dict): Configuration dicitonary from recipe
+        cube (iris.cube.Cube): Data to be plotted
+        data_info (dict): Dictionary with info to dataset
+        mode (str): Statistics that is used
+    """
+    dataset = data_info.get("dataset")
+    var_name = data_info.get("var")
+    wt_string = data_info.get("wt_string")
+    ensemble = data_info.get("ensemble", "")
+    timerange = data_info.get("timerange")
+    driver = data_info.get("driver", "")
+    if driver != "":
+        driver = f"_{driver}"
+
+    logger.info(
+        "Plotting %s %s %s for %s %s", dataset, var_name, mode, wt_string, wt
+    )
+
+    local_path = f"{cfg.get('plot_dir')}/{mode}"
+
+    if not os.path.exists(f"{local_path}"):
+        os.makedirs(f"{local_path}")
+
+    ax = plt.axes(projection=ccrs.PlateCarree())
+
+    if var_name == "psl":
+        psl_cmap = get_colormap("psl")
+        plt.title(
+            f"{dataset} {ensemble}, {var_name} {mode}\n"
+            + f"{timerange}, wt: {wt}"
+        )
+        unit = "[hPa]"
+        im = iplt.contourf(cube / 100, cmap=psl_cmap)
+        cb = plt.colorbar(im)
+        cb.ax.tick_params(labelsize=8)
+        cb.set_label(label=f"{var_name} {mode} {unit}")
+    elif var_name == "pr":
+        prcp_cmap = get_colormap("prcp")
+        if dataset == "ERA5":
+            unit = "[m]"
+            plt.title(
+                f"{dataset} {ensemble}, total {var_name} {mode}\n"
+                + f"{timerange}, wt: {wt}"
+            )
+        else:
+            unit = "[kg m-2 s-1]"
+            plt.title(
+                f"{dataset} {ensemble}, {var_name} flux {mode}\n"
+                + f"{timerange}, wt: {wt}"
+            )
+        im = iplt.contourf(cube, cmap=prcp_cmap)
+        cb = plt.colorbar(im)
+        cb.ax.tick_params(labelsize=8)
+        cb.set_label(label=f"{var_name} {mode} {unit}")
+    elif var_name == "tas":
+        temp_cmap = get_colormap("temp")
+        unit = "[K]"
+        plt.title(
+            f"{dataset} {ensemble}, 1000 hPa {var_name} {mode}\n"
+            + f"{timerange}, wt: {wt}"
+        )
+        im = iplt.contourf(cube, cmap=temp_cmap)
+        cb = plt.colorbar(im)
+        cb.ax.tick_params(labelsize=8)
+        cb.set_label(label=f"{var_name} {mode} {unit}")
+
+    gl = ax.gridlines(
+        crs=ccrs.PlateCarree(),
+        draw_labels=True,
+        linewidth=0.5,
+        color="gray",
+        alpha=0.5,
+        linestyle="--",
+    )
+    gl.left_labels = True
+    gl.bottom_labels = True
+    gl.top_labels = False
+    gl.right_labels = False
+    gl.xlines = True
+    gl.ylocator = mticker.FixedLocator(np.arange(20, 70, 5))
+    gl.xlocator = mticker.FixedLocator([-10, -5, 0, 5, 10, 15])
+    gl.xformatter = LONGITUDE_FORMATTER
+    gl.yformatter = LATITUDE_FORMATTER
+    gl.xlabel_style = {"size": 8, "color": "black"}
+    gl.ylabel_style = {"color": "black", "size": 8}
+
+    ax.set_extent([-15, 20, 27.5, 62.5])
+
+    ax.coastlines()
+    ax.add_feature(cfeature.BORDERS, linestyle=":")
+
+    plt.savefig(
+        f"{local_path}/{wt_string}_{wt}{driver}_{dataset}_{ensemble}"
+        f"_{var_name}_{mode}_{timerange}.png"
+    )
+    plt.savefig(
+        f"{local_path}/{wt_string}_{wt}{driver}_{dataset}_{ensemble}_"
+        f"{var_name}_{mode}_{timerange}.pdf"
+    )
+    plt.close()
+
+
+def plot_corr_rmse_heatmaps(
+    cfg: dict,
+    pattern_correlation_matrix: np.array,
+    rmse_matrix: np.array,
+    dataset: str,
+    timerange: str,
+):
+    """Plot heatmaps for correlation and rmse matrices.
+
+    Args:
+        cfg (dict): cfg dict from recipe
+        pattern_correlation_matrix (np.array): pattern correlation matrix
+        rmse_matrix (np.array): rmse matrix
+        dataset (str): string of dataset
+        timerange (str): string of timerange
+    """
+    output_path = f"{cfg.get('plot_dir')}/heatmaps"
+
+    if not os.path.exists(f"{output_path}"):
+        os.makedirs(f"{output_path}")
+
+    labels = np.arange(1, 28)
+
+    mask = np.zeros_like(pattern_correlation_matrix)
+    mask[np.triu_indices_from(mask)] = True
+    with sns.axes_style("white"):
+        plt.figure(figsize=(10, 10))
+        plt.title(f"Correlation Matrix , {dataset}, {timerange}")
+        levels = np.linspace(
+            np.min(pattern_correlation_matrix),
+            np.max(pattern_correlation_matrix),
+            9,
+        )
+        ax = sns.heatmap(
+            pattern_correlation_matrix,
+            mask=mask,
+            square=True,
+            annot=True,
+            annot_kws={"size": 6},
+            cmap="seismic",
+            xticklabels=labels,
+            yticklabels=labels,
+            cbar_kws={"ticks": levels, "shrink": 0.8, "format": "%.2f"},
+        )
+        ax.set_xlabel("lwt", fontsize=8)
+        ax.set_xticklabels(ax.get_xticklabels(), rotation=0)
+        ax.set_yticklabels(ax.get_yticklabels(), rotation=0)
+        ax.set_ylabel("lwt", fontsize=8)
+        plt.tight_layout()
+        plt.savefig(
+            f"{output_path}/correlation_matrix_{dataset}_{timerange}.png"
+        )
+        plt.savefig(
+            f"{output_path}/correlation_matrix_{dataset}_{timerange}.pdf"
+        )
+        plt.close()
+
+    mask = np.zeros_like(rmse_matrix)
+    mask[np.triu_indices_from(mask)] = True
+    with sns.axes_style("white"):
+        plt.figure(figsize=(10, 10))
+        plt.title(f"RMSE Matrix, {dataset}, {timerange}")
+        levels = np.linspace(np.min(rmse_matrix), np.max(rmse_matrix), 9)
+        ax = sns.heatmap(
+            rmse_matrix,
+            mask=mask,
+            square=True,
+            annot=True,
+            annot_kws={"size": 5},
+            cmap="seismic",
+            xticklabels=labels,
+            yticklabels=labels,
+            cbar_kws={"ticks": levels, "shrink": 0.8, "format": "%.2f"},
+        )
+        ax.set_xlabel("lwt", fontsize=8)
+        ax.set_xticklabels(ax.get_xticklabels(), rotation=0)
+        ax.set_yticklabels(ax.get_yticklabels(), rotation=0)
+        ax.set_ylabel("lwt", fontsize=8)
+        plt.tight_layout()
+        plt.savefig(f"{output_path}/rmse_matrix_{dataset}_{timerange}.png")
+        plt.savefig(f"{output_path}/rmse_matrix_{dataset}_{timerange}.pdf")
+        plt.close()
+
+
+def get_colormap(colormap_string: str) -> ListedColormap:
+    """Get colormaps based on string.
+
+    Args:
+        colormap_string (str): String to get Colormaps for either
+                                psl, tas or precipitation.
+
+    Returns
+        ListedColormap: Choosen Colormap
+    """
+    misc_seq_2_disc = [
+        (230 / 255, 240 / 255, 240 / 255),
+        (182 / 255, 217 / 255, 228 / 255),
+        (142 / 255, 192 / 255, 226 / 255),
+        (118 / 255, 163 / 255, 228 / 255),
+        (116 / 255, 130 / 255, 222 / 255),
+        (121 / 255, 97 / 255, 199 / 255),
+        (118 / 255, 66 / 255, 164 / 255),
+        (107 / 255, 40 / 255, 121 / 255),
+        (86 / 255, 22 / 255, 75 / 255),
+        (54 / 255, 14 / 255, 36 / 255),
+    ]
+
+    temp_seq_disc = [
+        (254 / 255, 254 / 255, 203 / 255),
+        (251 / 255, 235 / 255, 153 / 255),
+        (244 / 255, 204 / 255, 104 / 255),
+        (235 / 255, 167 / 255, 84 / 255),
+        (228 / 255, 134 / 255, 80 / 255),
+        (209 / 255, 98 / 255, 76 / 255),
+        (164 / 255, 70 / 255, 66 / 255),
+        (114 / 255, 55 / 255, 46 / 255),
+        (66 / 255, 40 / 255, 24 / 255),
+        (25 / 255, 25 / 255, 0 / 255),
+    ]
+
+    prec_seq_disc = [
+        (255 / 255, 255 / 255, 229 / 255),
+        (217 / 255, 235 / 255, 213 / 255),
+        (180 / 255, 216 / 255, 197 / 255),
+        (142 / 255, 197 / 255, 181 / 255),
+        (105 / 255, 177 / 255, 165 / 255),
+        (67 / 255, 158 / 255, 149 / 255),
+        (44 / 255, 135 / 255, 127 / 255),
+        (29 / 255, 110 / 255, 100 / 255),
+        (14 / 255, 85 / 255, 74 / 255),
+        (0 / 255, 60 / 255, 48 / 255),
+    ]
+
+    if colormap_string == "psl":
+        return ListedColormap(misc_seq_2_disc)
+    if colormap_string == "prcp":
+        return ListedColormap(prec_seq_disc)
+    if colormap_string == "temp":
+        return ListedColormap(temp_seq_disc)
+
+    return None
diff --git a/esmvaltool/diag_scripts/weathertyping/weathertyping.py b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
new file mode 100644
index 0000000000..6ea5138932
--- /dev/null
+++ b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
@@ -0,0 +1,315 @@
+"""Script for calculating Lamb weathertypes.
+
+It also plots the means and seasonal occurrence of the
+weathertypes, and offers the option to calculate simplified
+weathertypes based on precipitation patterns.
+"""
+
+import iris
+
+# import internal esmvaltool modules here
+from esmvaltool.diag_scripts.shared import run_diagnostic
+from esmvaltool.diag_scripts.weathertyping.calc_utils import (
+    calc_lwt_model,
+    calc_lwt_slwt_model,
+    calc_slwt_obs,
+    wt_algorithm,
+)
+from esmvaltool.diag_scripts.weathertyping.plot_utils import (
+    plot_means,
+    plot_seasonal_occurrence,
+)
+from esmvaltool.diag_scripts.weathertyping.wt_utils import (
+    add_dict_entry,
+    combine_wt_to_file,
+    get_ancestors_era5_eobs,
+    get_cfg_vars,
+    get_looping_dict,
+    get_model_output_filepath,
+    get_preproc_lists_ensemble,
+    get_provenance_record,
+    load_wt_files,
+    load_wt_preprocessors,
+    log_provenance,
+    run_predefined_slwt,
+    write_lwt_to_file,
+)
+
+
+def run_automatic_slwt(cfg: dict):
+    """Run the automated calculation for simplified weathertypes \
+    and write to file, and plot the means and seasonal occurrence \
+    of the weathertypes.
+
+    Args:
+        cfg (dict): Nested dictionary of metadata
+    """
+    preproc_variables_dict, _, _, work_dir, plotting, _, predefined_slwt = (
+        get_cfg_vars(cfg)
+    )
+    for dataset_name, dataset_vars in preproc_variables_dict.items():
+        data_info = {
+            "timerange": dataset_vars[0].get("timerange").replace("/", "-"),
+            "dataset": dataset_name,
+        }
+        if dataset_name == "ERA5":
+            wt_preproc, wt_preproc_prcp, wt_preproc_prcp_eobs = (
+                load_wt_preprocessors(dataset_name, preproc_variables_dict)
+            )
+
+            # calculate lwt
+            lwt = wt_algorithm(wt_preproc, dataset_name)
+
+            era5_ancestors, eobs_ancestors = get_ancestors_era5_eobs(
+                dataset_name, preproc_variables_dict
+            )
+
+            # calculate simplified lwt based on precipitation
+            # patterns or use predefined_slwt
+            if not predefined_slwt:
+                slwt_era5 = calc_slwt_obs(
+                    cfg,
+                    lwt,
+                    wt_preproc_prcp,
+                    dataset_name,
+                    era5_ancestors,
+                    data_info["timerange"],
+                )
+                slwt_eobs = calc_slwt_obs(
+                    cfg,
+                    lwt,
+                    wt_preproc_prcp_eobs,
+                    "E-OBS",
+                    eobs_ancestors,
+                    data_info["timerange"],
+                )
+            else:
+                slwt_era5, slwt_eobs = run_predefined_slwt(
+                    work_dir, dataset_name, lwt, predefined_slwt
+                )
+
+            # write to file
+            wt_list = [lwt, slwt_era5, slwt_eobs]
+            combine_wt_to_file(cfg, wt_list, wt_preproc, dataset_name)
+
+            # load weathertype files as cubes
+            wt_cubes = load_wt_files(f"{work_dir}/{dataset_name}.nc")
+
+            var_dict = get_looping_dict(
+                dataset_vars
+            )  # dataset_vars is list of variables for dataset dataset_name
+            if plotting:
+                # plot means
+                for var_name, var_data in var_dict.items():
+                    add_dict_entry(data_info, "var", var_name)
+                    add_dict_entry(data_info, "preproc_path", var_data[1])
+
+                    plot_means(cfg, var_data[0], wt_cubes, data_info)
+                plot_seasonal_occurrence(cfg, wt_cubes, data_info)
+        else:
+            if dataset_name == "E-OBS":
+                continue
+            for ensemble_var in dataset_vars:
+                if ensemble_var.get("preprocessor") == "weathertype_preproc":
+                    add_dict_entry(
+                        data_info, "ensemble", ensemble_var.get("ensemble", "")
+                    )
+                    add_dict_entry(
+                        data_info, "driver", ensemble_var.get("driver", "")
+                    )
+                    if data_info["driver"] != "":
+                        data_info["driver"] = "_" + {data_info["driver"]}
+
+                    wt_preproc = iris.load_cube(ensemble_var.get("filename"))
+
+                    output_file_path, preproc_path = get_model_output_filepath(
+                        dataset_name, ensemble_var
+                    )
+
+                    data_info["output_file_path"] = output_file_path
+                    data_info["preproc_path"] = preproc_path
+
+                    # calculate weathertypes
+                    calc_lwt_slwt_model(
+                        cfg, wt_preproc, data_info, predefined_slwt
+                    )
+
+                    # load wt files
+                    wt_cubes = load_wt_files(
+                        f"{work_dir}/{output_file_path}"
+                        f"/{dataset_name}"
+                        f"{data_info['driver']}_"
+                        f"{data_info['ensemble']}_"
+                        f"{data_info['timerange']}.nc"
+                    )
+
+                    var_dict = {
+                        f"{ensemble_var.get('short_name')}": get_preproc_lists_ensemble(
+                            ensemble_var
+                        )
+                    }
+
+                    # plot means
+                    if plotting:
+                        for var_name, var_data in var_dict.items():
+                            add_dict_entry(data_info, "var", var_name)
+                            add_dict_entry(
+                                data_info, "preproc_path", var_data[1]
+                            )
+
+                            plot_means(cfg, var_data[0], wt_cubes, data_info)
+                        plot_seasonal_occurrence(cfg, wt_cubes, data_info)
+
+
+def run_lwt(cfg: dict):
+    """Run calculation of weathertypes and write to file, and plot the means \
+    and seasonal occurrence of the weathertypes.
+
+    Args:
+        cfg (dict): Nested dictionary of metadata
+
+    """
+    preproc_variables_dict, _, _, work_dir, plotting, _, _ = get_cfg_vars(cfg)
+    for dataset_name, dataset_vars in preproc_variables_dict.items():
+        data_info = {
+            "timerange": dataset_vars[0].get("timerange").replace("/", "-"),
+            "dataset": dataset_name,
+        }
+
+        if dataset_name == "ERA5":
+            wt_preproc, _, _ = load_wt_preprocessors(
+                dataset_name, preproc_variables_dict
+            )
+
+            # calculate lwt
+            lwt = wt_algorithm(wt_preproc, dataset_name)
+
+            era5_ancestors, eobs_ancestors = get_ancestors_era5_eobs(
+                dataset_name, preproc_variables_dict
+            )
+
+            ancestors = [era5_ancestors, eobs_ancestors]
+
+            provenance_record = get_provenance_record(
+                "Lamb Weathertypes",
+                ancestors,
+                ["Lamb Weathertypes"],
+                False,
+                False,
+            )
+
+            log_provenance(f"{work_dir}/lwt_era5", cfg, provenance_record)
+
+            # write only lwt to file
+            write_lwt_to_file(cfg, lwt, wt_preproc, dataset_name)
+
+            # load wt files
+            wt_cubes = load_wt_files(
+                f"{work_dir}/{dataset_name}.nc", only_lwt=True
+            )
+
+            var_dict = get_looping_dict(
+                dataset_vars
+            )  # dataset_vars is list of variables for dataset dataset_name
+
+            if plotting:
+                # plot means
+                for var_name, var_data in var_dict.items():
+                    add_dict_entry(data_info, "var", var_name)
+                    add_dict_entry(data_info, "preproc_path", var_data[1])
+
+                    plot_means(
+                        cfg, var_data[0], wt_cubes, data_info, only_lwt=True
+                    )
+                plot_seasonal_occurrence(cfg, wt_cubes, data_info)
+        else:
+            if dataset_name == "E-OBS":
+                continue
+            for ensemble_var in dataset_vars:
+                if ensemble_var.get("preprocessor") == "weathertype_preproc":
+                    add_dict_entry(
+                        data_info, "ensemble", ensemble_var.get("ensemble", "")
+                    )
+                    add_dict_entry(
+                        data_info, "driver", ensemble_var.get("driver", "")
+                    )
+                    if data_info["driver"] != "":
+                        data_info["driver"] = "_" + {data_info["driver"]}
+
+                    wt_preproc = iris.load_cube(
+                        dataset_vars[0].get("filename")
+                    )
+
+                    output_file_path, preproc_path = get_model_output_filepath(
+                        dataset_name, dataset_vars
+                    )
+
+                    # calculate weathertypes
+                    data_info["output_file_path"] = output_file_path
+                    data_info["preproc_path"] = preproc_path
+
+                    calc_lwt_model(cfg, wt_preproc, dataset_name, data_info)
+
+                    # load wt files
+                    wt_cubes = load_wt_files(
+                        f"{work_dir}/{output_file_path}"
+                        f"/{dataset_name}"
+                        f"{data_info['driver']}"
+                        f"_{data_info['ensemble']}_"
+                        f"{data_info['timerange']}.nc",
+                        only_lwt=True,
+                    )
+
+                    var_dict = get_looping_dict(
+                        dataset_vars
+                    )  # dataset_vars is list of variables for dataset_name
+
+                    if plotting:
+                        # plot means
+                        for var_name, var_data in var_dict.items():
+                            add_dict_entry(data_info, "var", var_name)
+                            add_dict_entry(
+                                data_info, "preproc_path", var_data[1]
+                            )
+
+                            plot_means(
+                                cfg,
+                                var_data[0],
+                                wt_cubes,
+                                data_info,
+                                only_lwt=True,
+                            )
+                        plot_seasonal_occurrence(cfg, wt_cubes, data_info)
+
+
+def run_my_diagnostic(cfg: dict):
+    """Run the weathertyping diagnostic.
+
+    Arguments:
+        cfg - nested dictionary of metadata
+
+    Returns
+        string; runs the user diagnostic
+
+    """
+    # assemble the data dictionary keyed by dataset name
+    # this makes use of the handy group_metadata function that
+    # orders the data by 'dataset'; the resulting dictionary is
+    # keyed on datasets e.g. dict = {'MPI-ESM-LR': [var1, var2...]}
+    # where var1, var2 are dicts holding all needed information per variable
+    automatic_slwt = cfg.get("automatic_slwt")
+
+    if automatic_slwt:
+        run_automatic_slwt(cfg)
+    # if automatic_slwt is false, and predefined_slwt is false,
+    # just write selected pairs to file
+    elif not automatic_slwt:
+        run_lwt(cfg)
+
+
+if __name__ == "__main__":
+    # always use run_diagnostic() to get the config (the preprocessor
+    # nested dictionary holding all the needed information)
+    with run_diagnostic() as config:
+        # list here the functions that need to run
+        run_my_diagnostic(config)
diff --git a/esmvaltool/diag_scripts/weathertyping/wt_utils.py b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
new file mode 100644
index 0000000000..53dddfe4e2
--- /dev/null
+++ b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
@@ -0,0 +1,538 @@
+"""Utility functions for weathertyping script."""
+
+# operating system manipulations (e.g. path constructions)
+import json
+import logging
+import os
+import warnings
+
+# to manipulate iris cubes
+import iris
+import iris.analysis.cartography
+
+# general imports
+import numpy as np
+import pandas as pd
+
+# import internal esmvaltool modules here
+from esmvaltool.diag_scripts.shared import ProvenanceLogger, group_metadata
+
+iris.FUTURE.datum_support = True
+
+logger = logging.getLogger(os.path.basename(__file__))
+
+# Ignoring a warning that is produced when selecting timesteps of a weathertype
+warnings.filterwarnings("ignore", ".*Collapsing a non-contiguous coordinate*")
+
+
+def add_dict_entry(dict_: dict, key: str, value):
+    """Add entry to dictionary.
+
+    Args:
+        dict (dict): dictionary to add entry to
+        key (str): key of entry
+        value: value of entry
+
+    Returns
+        dict: updated dictionary
+    """
+    dict_[key] = value
+    return dict_
+
+
+def get_cfg_vars(cfg: dict):
+    """Get list of vars from configuration dict.
+
+    Args:
+        cfg (dict): Configuration dict from recipe.
+
+    Returns
+        tuple: cfg vars
+    """
+    preproc_variables_dict = group_metadata(
+        cfg.get("input_data").values(), "dataset"
+    )
+
+    return (
+        preproc_variables_dict,
+        cfg.get("correlation_threshold"),
+        cfg.get("rmse_threshold"),
+        cfg.get("work_dir"),
+        cfg.get("plotting", False),
+        cfg.get("automatic_slwt", True),
+        cfg.get("predefined_slwt", False),
+    )
+
+
+def load_wt_preprocessors(dataset: str, preproc_variables_dict: dict):
+    """Load preprocessor cubes for calculating Lamb weathertypes.
+
+    Args:
+        dataset (str): dataset name
+        preproc_variables_dict (dict): dictionary of preprocessor variables
+
+    Returns
+        _type_: list of preprocessor vars for weathertyping
+    """
+    wt_preproc = iris.load_cube(
+        preproc_variables_dict.get(dataset)[0].get("filename")
+    )
+    wt_preproc_prcp = iris.load_cube(
+        preproc_variables_dict.get(dataset)[1].get("filename")
+    )
+    wt_preproc_prcp_eobs = iris.load_cube(
+        preproc_variables_dict.get("E-OBS")[0].get("filename")
+    )
+
+    return wt_preproc, wt_preproc_prcp, wt_preproc_prcp_eobs
+
+
+def get_ancestors_era5_eobs(dataset: str, preproc_variables_dict: dict):
+    """Get ancestors for ERA5/E-OBS.
+
+    Args:
+        dataset (str): dataset name
+        preproc_variables_dict (dict): dictionary of preprocessor variables
+
+    Returns
+        _type_: lists of ERA5/E-OBS ancestors
+    """
+    era5_ancestors = [
+        preproc_variables_dict.get(dataset)[0].get("filename"),
+        preproc_variables_dict.get(dataset)[1].get("filename"),
+    ]
+    eobs_ancestors = [
+        preproc_variables_dict.get(dataset)[0].get("filename"),
+        preproc_variables_dict.get("E-OBS")[0].get("filename"),
+    ]
+    return era5_ancestors, eobs_ancestors
+
+
+def get_model_output_filepath(dataset: str, value: list):
+    """Generate output filepath for model data.
+
+    Args:
+        dataset (str): Model name
+        value (list): Model variables
+
+    Returns
+        _type_: Output filepath and preprocessor path for
+        future referencing.
+    """
+    timerange = value.get("timerange").replace("/", "-")
+    experiment = value.get("exp")
+    ensemble = value.get("ensemble")
+    driver = value.get("driver", "")
+
+    out_path = f"{dataset}/{driver}/{experiment}/{ensemble}/{timerange}"
+    preproc_path = value.get("filename")
+
+    return out_path, preproc_path
+
+
+def get_preproc_lists(preproc_vars: list):
+    """Put preprocessor variables and paths into list for further use.
+
+    Args:
+        preproc_vars (list): List of variables for specific
+        dataset.
+
+    Returns
+        _type_: List of preprocessor cubes for mean calculations
+        as well as path to files.
+    """
+    preproc_path_psl = preproc_vars[-3].get("filename")
+    preproc_path_prcp = preproc_vars[-2].get("filename")
+    preproc_path_tas = preproc_vars[-1].get("filename")
+
+    mean_preproc_psl = iris.load_cube(preproc_vars[-3].get("filename"))
+    mean_preproc_prcp = iris.load_cube(preproc_vars[-2].get("filename"))
+    mean_preproc_tas = iris.load_cube(preproc_vars[-1].get("filename"))
+
+    preproc_list = [mean_preproc_psl, mean_preproc_prcp, mean_preproc_tas]
+    preproc_path_list = [preproc_path_psl, preproc_path_prcp, preproc_path_tas]
+
+    return preproc_list, preproc_path_list
+
+
+def get_preproc_lists_ensemble(preproc_vars: list):
+    """Put preprocessor variables and paths into list for further use.
+
+    Args:
+        preproc_vars (list): Variable for specific ensemble member.
+
+    Returns
+        _type_: Preprocessor cube for mean calculations
+        as well as path to files.
+    """
+    preproc_path = preproc_vars.get("filename")
+
+    preproc = iris.load_cube(preproc_vars.get("filename"))
+
+    # preproc_list = [mean_preproc]
+    # preproc_path_list = [preproc_path_psl]
+
+    return preproc, preproc_path
+
+
+def get_looping_dict(preproc_vars: list):
+    """Put cubes into dictionary for further use.
+
+    Args:
+        preproc_vars (list): Values of dataset dictionary
+
+    Returns
+        _type_: Dictionary of the form {'var': [preprov_var, preproc_path]}
+    """
+    preproc, preproc_path = get_preproc_lists(preproc_vars)
+    dict_ = {
+        "psl": [preproc[0], preproc_path[0]],
+        "pr": [preproc[1], preproc_path[1]],
+        "tas": [preproc[2], preproc_path[2]],
+    }
+    return dict_
+
+
+def load_wt_files(path: str, only_lwt=False):
+    """Load *.nc files of weathertype data.
+
+    If only_lwt is true, only Lamb
+    weathertypes will be loaded. (useful for automatic_slwt = False)
+
+    Args:
+        path (str): Path to weathertype data.
+        only_lwt (bool, optional): If True,
+        only Lamb weathertypes will be loaded. Defaults to False.
+        (useful for automatic_slwt = False)
+
+    Returns
+        _type_: List of weathertype cubes.
+    """
+    if not only_lwt:
+        lwt_cube = iris.load_cube(path, "lwt")
+        slwt_era5_cube = iris.load_cube(path, "slwt_era5")
+        slwt_eobs_cube = iris.load_cube(path, "slwt_eobs")
+        wt_cubes = [lwt_cube, slwt_era5_cube, slwt_eobs_cube]
+    else:
+        lwt_cube = iris.load_cube(path, "lwt")
+        wt_cubes = [lwt_cube]
+
+    return wt_cubes
+
+
+def get_provenance_record(
+    caption: str,
+    ancestors: list,
+    long_names: list,
+    plot_types: bool | list,
+    statistics: bool | list,
+) -> dict:
+    """Get provenance record.
+
+    Args:
+        caption (str): Caption for plots
+        ancestors (list): List of ancestors
+        long_names (list): Variable long names
+        plot_types (bool | list): Type of plot. Can be false
+        if output is not a plot.
+        statistics (bool | list): Types of statistics used.
+
+    Returns
+        dict: Provenance dictionary.
+    """
+    record = {
+        "caption": caption,
+        "domains": ["reg"],
+        "authors": ["jury_martin", "kroissenbrunner_thomas"],
+        "references": ["maraun21jgr", "jones93ijc"],
+        "projects": ["preval"],
+        "long_names": long_names,
+        "ancestors": ancestors,
+    }
+    if plot_types is not False:
+        record["plot_types"] = plot_types
+    if statistics is not False:
+        record["statistics"] = statistics
+    return record
+
+
+def log_provenance(filename: str, cfg: dict, provenance_record: dict):
+    """Log provenance. Produces xml file provenance info.
+
+    Args:
+        caption (str): Caption of plots.
+        filename (str): Output name of provenance.
+        cfg (dict): Configuration dictionary provided by recipe.
+        provenance_record (dict): Provenance record dictionary.
+    """
+    with ProvenanceLogger(cfg) as provenance_logger:
+        provenance_logger.log(filename, provenance_record)
+
+    logger.info("Output stored as %s", filename)
+
+
+def turn_list_to_mapping_dict(list_: list) -> dict:
+    """Turn list of combined WT into a dictionary for further processing.
+
+    Args:
+        list_ (list): List where entries are lists with related WT
+
+    Returns
+        dict: Mapping dicitonary keys are simplified WT, values are Lamb WT
+    """
+    result_dict = {}
+
+    for i, s in enumerate(list_):
+        for elem in s:
+            if elem not in result_dict:
+                result_dict[elem] = i + 1
+            else:
+                result_dict[elem].append(i)
+
+    return result_dict
+
+
+def get_mapping_dict(selected_pairs: list) -> dict:
+    """Get mapping dictionary from list of selected pairs.
+
+    Args:
+        selected_pairs (list): Selected pairs of WTs based on
+        precipitation patterns over specified area and
+        correlation and RSME thresholds defined in recipe.S
+
+    Returns
+        dict: Mapping dicitonary keys are simplified WT, values are Lamb WT
+    """
+    mapping_array = []
+
+    for elem in selected_pairs:
+        mapping_array.append(elem[0])
+
+    s = [set(i) for i in mapping_array if i]
+
+    def find_intersection(m_list: list) -> list:
+        for i, v in enumerate(m_list):
+            for j, k in enumerate(m_list[i + 1 :], i + 1):
+                if v & k:
+                    s[i] = v.union(m_list.pop(j))
+                    return find_intersection(m_list)
+        return m_list
+
+    merged_tuples = find_intersection(s)
+    mapping_dict = turn_list_to_mapping_dict(merged_tuples)
+
+    return mapping_dict
+
+
+def write_mapping_dict(work_dir: str, dataset: str, mapping_dict: dict):
+    """Write mapping dictionary to file.
+
+    Args:
+        work_dir (str): Working directory
+        dataset (str): Name of dataset
+        mapping_dict (dict): Mapping dictionary
+    """
+    mapping_dict_reformat = convert_dict(mapping_dict)
+
+    with open(
+        f"{work_dir}/wt_mapping_dict_{dataset}.json", "w", encoding="utf-8"
+    ) as file:
+        json.dump(mapping_dict_reformat, file)
+
+
+def convert_dict(dict_: dict) -> dict:
+    """Convert mapping dictionary.
+
+    From {lwt: slwt, ...} format to {slwt: [lwt1, lwt2], ...}.
+
+    Args:
+        dict_ (dict): Dict in the {lwt: slwt, ...} format
+
+    Returns
+        dict: Dict in the {slwt: [lwt1, lwt2], ...} format
+    """
+    new_dict = {}
+    for dataset, value in dict_.items():
+        if value not in new_dict:
+            new_dict[value] = []
+        new_dict[value].append(dataset)
+    return new_dict
+
+
+def reverse_convert_dict(dict_: dict) -> dict:
+    """Convert mapping dictionary.
+
+    From {slwt: [lwt1, lwt2], ...} format to {lwt: slwt, ...}.
+
+    Args:
+        original_dict (dict): Dict in the {slwt: [lwt1, lwt2], ...}format
+
+    Returns
+        dict: Dict in the  format {lwt: slwt, ...}
+    """
+    new_dict = {}
+    for key, value_list in dict_.items():
+        for original_key in value_list:
+            new_dict[original_key] = key
+    return new_dict
+
+
+def write_corr_rmse_to_csv(
+    cfg: dict,
+    pattern_correlation_matrix: np.array,
+    rmse_matrix: np.array,
+    dataset: str,
+):
+    """Write correlation and rsme matrix to csv files.
+
+    Args:
+        cfg (dict): Configuration dictionary from recipe
+        pattern_correlation_matrix (np.array): Correlation matrix
+        rmse_matrix (np.array): RSME matrix
+        dataset (str): Name of dataset
+    """
+    logger.info("Writing corr and rsme matrices for %s", dataset)
+
+    work_dir = cfg.get("work_dir")
+
+    df_corr = pd.DataFrame(pattern_correlation_matrix)
+    df_corr.index = range(1, len(df_corr) + 1)
+    df_corr.columns = range(1, len(df_corr.columns) + 1)
+    df_corr.to_csv(
+        f"{work_dir}/correlation_matrix_{dataset}.csv", index_label="Index"
+    )
+
+    df_rmse = pd.DataFrame(rmse_matrix)
+    df_rmse.index = range(1, len(df_rmse) + 1)
+    df_rmse.columns = range(1, len(df_rmse.columns) + 1)
+    df_rmse.to_csv(
+        f"{work_dir}/rmse_matrix_{dataset}.csv", index_label="Index"
+    )
+
+
+def run_predefined_slwt(
+    work_dir: str, dataset_name: str, lwt: np.array, predefined_slwt: dict
+):
+    """Run predefined slwt mapping.
+
+    Args:
+        work_dir (str): Working directory to save mapping dict
+        dataset_name (str): Name of dataset
+        lwt (np.array): lwt array
+        predefined_slwt (dict): Predefined mapping dict
+
+    Returns
+        np.array: slwt_era5 array
+        np.array: slwt_eobs array
+    """
+    predefined_slwt = check_mapping_dict_format(predefined_slwt)
+    write_mapping_dict(work_dir, dataset_name, predefined_slwt)
+    write_mapping_dict(work_dir, "E-OBS", predefined_slwt)
+    slwt_era5 = map_lwt_to_slwt(lwt, predefined_slwt)
+    slwt_eobs = map_lwt_to_slwt(lwt, predefined_slwt)
+
+    return slwt_era5, slwt_eobs
+
+
+def combine_wt_to_file(
+    cfg: dict, wt_list: list, cube: iris.cube.Cube, file_name: str
+):
+    """Combine lwt and slwt arrays to one file.
+
+    Args:
+        cfg (dict): Configuration dictionary from recipe
+        wt_list (list): List of weathertype arrays
+        cube (iris.cube.Cube): Cube of data to keep time coordinate
+        file_name (str): Name of output file
+    """
+    lwt = wt_list[0]
+    slwt_era5 = wt_list[1]
+    slwt_eobs = wt_list[2]
+
+    logger.info("Writing weathertypes to %s", file_name)
+
+    tcoord = cube.coord("time")
+    time_points = tcoord.units.num2date(tcoord.points)
+
+    write_path = cfg.get("work_dir")
+
+    wt_cube = iris.cube.CubeList()
+    wt_cube.append(iris.cube.Cube(lwt, long_name="lwt"))
+    wt_cube.append(iris.cube.Cube(slwt_era5, long_name="slwt_era5"))
+    wt_cube.append(iris.cube.Cube(slwt_eobs, long_name="slwt_eobs"))
+
+    wt_cube[0].add_dim_coord(tcoord, 0)
+    wt_cube[1].add_dim_coord(tcoord, 0)
+    wt_cube[2].add_dim_coord(tcoord, 0)
+
+    iris.save(wt_cube, f"{write_path}/{file_name}.nc")
+
+    # write to csv file
+    d = {
+        "date": time_points[:],
+        "lwt": np.int8(lwt),
+        "slwt_era5": np.int8(slwt_era5),
+        "slwt_eobs": np.int8(slwt_eobs),
+    }
+    df = pd.DataFrame(data=d)
+    df.to_csv(write_path + f"/{file_name}.csv", index=False)
+
+
+def write_lwt_to_file(
+    cfg: dict, lwt: np.array, cube: iris.cube.Cube, file_name: str
+):
+    """Write only lwt to file.
+
+    Args:
+        cfg (dict): Configuration dictionary from recipe
+        lwt (np.array): lwt array
+        cube (iris.cube.Cube): Cube to keep time coordinate
+        file_name (str): Output filename
+    """
+    logger.info("Writing Lamb Weathertype to %s", file_name)
+
+    tcoord = cube.coord("time")
+    time_points = tcoord.units.num2date(tcoord.points)
+
+    write_path = cfg.get("work_dir")
+
+    wt_cube = iris.cube.CubeList()
+    wt_cube.append(iris.cube.Cube(np.int8(lwt), long_name="lwt"))
+
+    wt_cube[0].add_dim_coord(tcoord, 0)
+    iris.save(wt_cube, f"{write_path}/{file_name}.nc")
+
+    # write to csv file
+    d = {"date": time_points[:], "lwt": np.int8(lwt)}
+    df = pd.DataFrame(data=d)
+    df.to_csv(write_path + f"/{file_name}.csv", index=False)
+
+
+def map_lwt_to_slwt(lwt: np.array, mapping_dict: dict) -> np.array:
+    """Map lwt array to slwt array.
+
+    Args:
+        lwt (np.array): array of lwt
+        mapping_dict (dict): mapping dictionary in {lwt: slwt, ...} format
+
+    Returns
+        np.array: array of slwt
+    """
+    return np.array([np.int8(mapping_dict.get(value, 0)) for value in lwt])
+
+
+def check_mapping_dict_format(mapping_dict: dict) -> dict:
+    """Check format of mapping dict and return in {lwt: slwt, ...} format.
+
+    Args:
+        mapping_dict (dict): mapping dict in any format
+
+    Returns
+        dict: mapping dict in {lwt: slwt, ...} format
+    """
+    if isinstance(mapping_dict.get(list(mapping_dict.keys())[0]), list):
+        dict_ = reverse_convert_dict(mapping_dict)
+    else:
+        dict_ = mapping_dict
+
+    return dict_
diff --git a/esmvaltool/recipes/recipe_weathertyping_CMIP6.yml b/esmvaltool/recipes/recipe_weathertyping_CMIP6.yml
new file mode 100644
index 0000000000..737a00e16b
--- /dev/null
+++ b/esmvaltool/recipes/recipe_weathertyping_CMIP6.yml
@@ -0,0 +1,310 @@
+documentation:
+  title: Weathertyping algorithm
+
+  description: |
+    A diagnostic to calculate Lamb weathertypes over a given region. Furthermore,
+    correlations between weathertypes and precipitation patterns over a given area can be calculated
+    and 'combined' or 'simplified' weathertypes can be derived. Additionally, mean fields, as well as
+    anomalies and standard deviations can be plotted.
+
+  authors:
+    - jury_martin
+    - kroissenbrunner_thomas
+
+  maintainer:
+    - kroissenbrunner_thomas
+
+  projects:
+    - preval
+
+  references:
+    - maraun21jgr
+    - jones93ijc
+
+datasets_psl_CMIP6: &datasets_psl_cmip6
+  - {institute: AS-RCEC, dataset: TaiESM1, ensemble: r1i1p1f1, grid: gn, esgf_version: v20200626}
+  - {institute: AWI, dataset: AWI-ESM-1-1-LR, ensemble: r1i1p1f1, grid: gn, esgf_version: v20200212}
+  - {institute: BCC, dataset: BCC-CSM2-MR, ensemble: r1i1p1f1, grid: gn, esgf_version: v20181126}
+  - {institute: BCC, dataset: BCC-ESM1, ensemble: r1i1p1f1, grid: gn, esgf_version: v20181220}
+  - {institute: CAS, dataset: FGOALS-f3-L, ensemble: r1i1p1f1, grid: gr, esgf_version: v20191019}
+  - {institute: CAS, dataset: FGOALS-g3, ensemble: r1i1p1f1, grid: gn, esgf_version: v20190826}
+  # - {institute: CCCR-IITM, dataset: IITM-ESM, ensemble: r1i1p1f1, grid: gn, esgf_version: v20210203} #discontinous longitude warning
+  - {institute: CCCma, dataset: CanESM5, ensemble: r1i1p1f1, grid: gn, esgf_version: v20190429}
+  - {institute: CMCC, dataset: CMCC-CM2-HR4, ensemble: r1i1p1f1, grid: gn, esgf_version: v20200904}
+  - {institute: CMCC, dataset: CMCC-CM2-SR5, ensemble: r1i1p1f1, grid: gn, esgf_version: v20200616}
+  - {institute: CMCC, dataset: CMCC-ESM2, ensemble: r1i1p1f1, grid: gn, esgf_version: v20210114}
+  - {institute: CNRM-CERFACS, dataset: CNRM-CM6-1, ensemble: r1i1p1f2, grid: gr, esgf_version: v20180917}
+  - {institute: CSIRO-ARCCSS, dataset: ACCESS-CM2, ensemble: r1i1p1f1, grid: gn, esgf_version: v20191108}
+  - {institute: CSIRO, dataset: ACCESS-ESM1-5, ensemble: r1i1p1f1, grid: gn, esgf_version: v20191115}
+  - {institute: EC-Earth-Consortium, dataset: EC-Earth3-AerChem, ensemble: r1i1p1f1, grid: gr, esgf_version: v20200624}
+  - {institute: EC-Earth-Consortium, dataset: EC-Earth3-CC, ensemble: r1i1p1f1, grid: gr, esgf_version: v20210113}
+  - {institute: EC-Earth-Consortium, dataset: EC-Earth3-Veg-LR, ensemble: r1i1p1f1, grid: gr, esgf_version: v20200217}
+  - {institute: EC-Earth-Consortium, dataset: EC-Earth3-Veg, ensemble: r1i1p1f1, grid: gr, esgf_version: v20211207}
+  - {institute: EC-Earth-Consortium, dataset: EC-Earth3, ensemble: r1i1p1f1, grid: gr, esgf_version: v20200310}
+  - {institute: HAMMOZ-Consortium, dataset: MPI-ESM-1-2-HAM, ensemble: r1i1p1f1, grid: gn, esgf_version: v20190627}
+  - {institute: INM, dataset: INM-CM4-8, ensemble: r1i1p1f1, grid: gr1, esgf_version: v20190530}
+  - {institute: INM, dataset: INM-CM5-0, ensemble: r1i1p1f1, grid: gr1, esgf_version: v20190610}
+  - {institute: IPSL, dataset: IPSL-CM5A2-INCA, ensemble: r1i1p1f1, grid: gr, esgf_version: v20200729}
+  # - {institute: IPSL, dataset: IPSL-CM6A-LR-INCA, ensemble: r1i1p1f1, grid: gr, esgf_version: v20210216} #???
+  - {institute: IPSL, dataset: IPSL-CM6A-LR, ensemble: r1i1p1f1, grid: gr, esgf_version: v20180803}
+  - {institute: KIOST, dataset: KIOST-ESM, ensemble: r1i1p1f1, grid: gr1, esgf_version: v20210601}
+  - {institute: MIROC, dataset: MIROC-ES2L, ensemble: r1i1p1f2, grid: gn, esgf_version: v20191129}
+  - {institute: MIROC, dataset: MIROC6, ensemble: r1i1p1f1, grid: gn, esgf_version: v20191016}
+  # - {institute: MPI-M, dataset: ICON-ESM-LR, ensemble: r1i1p1f1, grid: gn, esgf_version: v20210215} #boundary values not 0 or 2
+  - {institute: MPI-M, dataset: MPI-ESM1-2-HR, ensemble: r1i1p1f1, grid: gn, esgf_version: v20190710}
+  - {institute: MPI-M, dataset: MPI-ESM1-2-LR, ensemble: r1i1p1f1, grid: gn, esgf_version: v20190710}
+  - {institute: MRI, dataset: MRI-ESM2-0, ensemble: r1i1p1f1, grid: gn, esgf_version: v20190603}
+  - {institute: NASA-GISS, dataset: GISS-E2-2-G, ensemble: r1i1p1f1, grid: gn, esgf_version: v20191120, start_year: 1970}
+  - {institute: NCAR, dataset: CESM2-FV2, ensemble: r1i1p1f1, grid: gn, esgf_version: v20191120}
+  - {institute: NCAR, dataset: CESM2-WACCM-FV2, ensemble: r1i1p1f1, grid: gn, esgf_version: v20191120}
+  # - {institute: NCAR, dataset: CESM2-WACCM, ensemble: r1i1p1f1, grid: gn, esgf_version: v20190227} #concatenate error
+  - {institute: NCAR, dataset: CESM2, ensemble: r1i1p1f1, grid: gn, esgf_version: v20190308}
+  - {institute: NCC, dataset: NorCPM1, ensemble: r1i1p1f1, grid: gn, esgf_version: v20200724}
+  - {institute: NCC, dataset: NorESM2-LM, ensemble: r1i1p1f1, grid: gn, esgf_version: v20190815}
+  - {institute: NCC, dataset: NorESM2-MM, ensemble: r1i1p1f1, grid: gn, esgf_version: v20191108}
+  # - {institute: NIMS-KMA, dataset: KACE-1-0-G, ensemble: r1i1p1f1, grid: gr, esgf_version: v20190911} #discontinous longitude coordinate warning
+  - {institute: NOAA-GFDL, dataset: GFDL-CM4, ensemble: r1i1p1f1, grid: gr1, esgf_version: v20180701}
+  - {institute: NOAA-GFDL, dataset: GFDL-CM4, ensemble: r1i1p1f1, grid: gr2, esgf_version: v20180701}
+  - {institute: NOAA-GFDL, dataset: GFDL-ESM4, ensemble: r1i1p1f1, grid: gr1, esgf_version: v20190726}
+  - {institute: NUIST, dataset: NESM3, ensemble: r1i1p1f1, grid: gn, esgf_version: v20190812}
+  - {institute: SNU, dataset: SAM0-UNICON, ensemble: r1i1p1f1, grid: gn, esgf_version: v20190323}
+
+datasets_ERA5: &datasets_era5
+  - {project: OBS6, dataset: ERA5, type: reanaly, frequency: day, latestversion: v1, mip: day, tier: 3, start_year: 1958, end_year: 2014}
+
+datasets_EOBS: &datasets_EOBS
+  - {dataset: E-OBS, project: OBS, version: v29.0e-0.25, type: ground, tier: 2, start_year: 1958, end_year: 2014, mip: day}
+
+preprocessors:
+  weathertype_preproc:
+    extract_time: &time
+      start_year: 1950
+      start_month: 1
+      start_day: 1
+      end_year: 2014
+      end_month: 12
+      end_day: 31
+    regrid:
+      # This is the region for which the Lamb weathertypes will be calculated.
+      # The objective classification scheme of Jones et al. 1993 is used,
+      # these grid points are the same ones as used in the paper.
+      target_grid: &grid
+        start_longitude: -5
+        end_longitude: 25
+        step_longitude: 5
+        start_latitude: 35
+        end_latitude: 55
+        step_latitude: 5
+      scheme: linear
+
+  weathertype_preproc_pr:
+    extract_time:
+      *time
+    extract_region:
+      # This is the region for which precipitation data can be used to
+      # calculate correlations between weathertypes and this said region
+      # to further be able to combine them into 'simplified' weathertypes.
+      # Combined weathertypes correspond to a specific precipitation
+      # pattern over this region. You can change this to a region of your liking
+      # to see which types of weathertypes are associated with which precipitation
+      # patterns.
+      start_longitude: 9.5
+      end_longitude: 17.25
+      start_latitude: 46.25
+      end_latitude: 49
+
+  mean_preproc:
+    extract_time:
+      *time
+    extract_region: &reg
+      start_longitude: -15
+      end_longitude: 35
+      start_latitude: 25
+      end_latitude: 65
+
+diagnostics:
+  weathertyping:
+    description: calculate weathertypes and plot means
+    variables:
+      era5_msl_wt: &era5_msl_wt
+        project: OBS6
+        dataset: ERA51
+        type: reanaly
+        frequency: day
+        mip: day
+        latestversion: v1
+        tier: 3
+        start_year: 1958
+        end_year: 2014
+        short_name: psl
+        preprocessor: weathertype_preproc
+        additional_datasets:
+          *datasets_era5
+
+      era5_pr_wt: &era5_pr_wt
+        project: OBS6
+        dataset: ERA51
+        type: reanaly
+        frequency: day
+        mip: day
+        latestversion: v1
+        tier: 3
+        start_year: 1958
+        end_year: 2014
+        short_name: pr
+        preprocessor: weathertype_preproc_pr
+        additional_datasets:
+          *datasets_era5
+
+      eobs_pr_wt: &eobs_pr_wt
+        project: OBS
+        dataset: E-OBS
+        type: ground
+        mip: day
+        version: v29.0e-0.25
+        tier: 2
+        start_year: 1958
+        end_year: 2014
+        short_name: pr
+        preprocessor: weathertype_preproc_pr
+        additional_datasets:
+          *datasets_EOBS
+
+      era5_msl_mean: &era5_msl
+        project: OBS6
+        dataset: ERA5
+        type: reanaly
+        frequency: day
+        mip: day
+        latestversion: v1
+        tier: 3
+        start_year: 1958
+        end_year: 2014
+        short_name: psl
+        preprocessor: mean_preproc
+        additional_datasets:
+          *datasets_era5
+
+      era5_tp_mean: &era5_prcp
+        project: OBS6
+        dataset: ERA5
+        type: reanaly
+        frequency: day
+        mip: day
+        latestversion: v1
+        tier: 3
+        start_year: 1958
+        end_year: 2014
+        short_name: pr
+        preprocessor: mean_preproc
+        additional_datasets:
+          *datasets_era5
+
+      era5_tas_mean: &era5_temp
+        project: OBS6
+        dataset: ERA5
+        type: reanaly
+        frequency: day
+        mip: day
+        latestversion: v1
+        tier: 3
+        start_year: 1958
+        end_year: 2014
+        short_name: tas
+        preprocessor: mean_preproc
+        additional_datasets:
+          *datasets_era5
+
+      cmip6_historical_psl_day_wt: &cmip6_historical_wt
+        project: CMIP6
+        activity: CMIP
+        exp: historical
+        mip: day
+        short_name: psl
+        preprocessor: weathertype_preproc
+        start_year: 1950
+        end_year: 2014
+        additional_datasets:
+          *datasets_psl_cmip6
+
+      cmip6_historical_psl_day_mean: &cmip6_historical_psl
+        project: CMIP6
+        activity: CMIP
+        exp: historical
+        mip: day
+        short_name: psl
+        preprocessor: mean_preproc
+        start_year: 1950
+        end_year: 2014
+        additional_datasets:
+          *datasets_psl_cmip6
+
+      cmip6_historical_prcp_day_mean: &cmip6_historical_prcp
+        project: CMIP6
+        activity: CMIP
+        exp: historical
+        mip: day
+        short_name: pr
+        preprocessor: mean_preproc
+        start_year: 1950
+        end_year: 2014
+        additional_datasets:
+          *datasets_psl_cmip6
+
+      cmip6_historical_temp_day_mean: &cmip6_historical_ta
+        project: CMIP6
+        activity: CMIP
+        exp: historical
+        mip: day
+        short_name: tas
+        preprocessor: mean_preproc
+        start_year: 1950
+        end_year: 2014
+        additional_datasets:
+          *datasets_psl_cmip6
+
+    scripts:
+      weathertyping:
+        correlation_threshold: 0.9
+        rmse_threshold: 0.002
+        automatic_slwt: true
+        predefined_slwt:
+          1:
+            - 1
+            - 2
+            - 19
+          2:
+            - 3
+            - 4
+            - 22
+            - 21
+          3:
+            - 5
+            - 6
+            - 15
+            - 16
+          4:
+            - 7
+            - 8
+            - 11
+            - 18
+          5:
+            - 9
+            - 17
+          6:
+            - 10
+            - 20
+          7:
+            - 12
+            - 13
+            - 14
+          8:
+            - 23
+            - 24
+          9:
+            - 25
+            - 26
+          0:
+            - 27
+        plotting: true
+        script: weathertyping/weathertyping.py

From 0c39a5a79a9663c4119a500d59f34d1cfdafe62f Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Mon, 27 Oct 2025 15:49:01 +0100
Subject: [PATCH 02/57] add reference

---
 esmvaltool/references/maraun21jgr.bibtex | 15 +++++++++++++++
 1 file changed, 15 insertions(+)
 create mode 100644 esmvaltool/references/maraun21jgr.bibtex

diff --git a/esmvaltool/references/maraun21jgr.bibtex b/esmvaltool/references/maraun21jgr.bibtex
new file mode 100644
index 0000000000..613e5d1bb9
--- /dev/null
+++ b/esmvaltool/references/maraun21jgr.bibtex
@@ -0,0 +1,15 @@
+@article{https://doi.org/10.1029/2020JD032824,
+    author = {Maraun, Douglas and Truhetz, Heimo and Schaffer, Armin},
+    title = {Regional Climate Model Biases, Their Dependence on Synoptic Circulation Biases and the Potential for Bias Adjustment: A Process-Oriented Evaluation of the Austrian Regional Climate Projections},
+    journal = {Journal of Geophysical Research: Atmospheres},
+    volume = {126},
+    number = {6},
+    pages = {e2020JD032824},
+    keywords = {Austria, bias adjustment, climate model evaluation, large-scale circulation errors, regional climate projections},
+    doi = {https://doi.org/10.1029/2020JD032824},
+    url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020JD032824},
+    eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2020JD032824},
+    note = {e2020JD032824 2020JD032824},
+    abstract = {Abstract The Austrian regional climate projections are based on an ensemble of bias adjusted regional climate model simulations. Bias adjustment (BA) improves the usability of climate projections for impact studies, but cannot mitigate fundamental model errors. This argument holds in particular for biases in the temporal dependence, which is strongly influenced by the large-scale circulation. Global climate models (GCMs), underlying regional climate projections, suffer from substantial circulation errors. We therefore, conduct a process-based evaluation of the Austrian climate projections focusing on large-scale circulation errors, their regional imprints and the potential for BA. First, we define nine synoptic weather types and assess how well the considered climate models represent their occurrence and persistence. Second, we assess biases in the overall dry and hot day probabilities, as well as conditional on synoptic weather type occurrence; and biases in the duration of dry and hot spells. Third, we investigate how these biases depend on biases in the occurrence and persistence of relevant synoptic weather types. And fourth, we study how much an overall BA improves these biases. Many GCMs misrepresent the occurrence and persistence of relevant synoptic weather types. These biases have a clear imprint on biases in dry and hot day occurrence and spell durations. BA in many cases helps to greatly reduce biases even in the presence of circulation biases, but may in some cases amplify conditional biases. Persistence biases are especially relevant for the representation of meteorological drought. Biases in the duration of dry spells cannot fully be mitigated by BA.},
+    year = {2021}
+}

From 7bf3acd3ada3cddb5c2c8676d6eb3c74423e586a Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Tue, 28 Oct 2025 07:55:44 +0100
Subject: [PATCH 03/57] add jones93ijc.bibtex

---
 esmvaltool/references/jones93ijc.bibtex | 14 ++++++++++++++
 1 file changed, 14 insertions(+)
 create mode 100644 esmvaltool/references/jones93ijc.bibtex

diff --git a/esmvaltool/references/jones93ijc.bibtex b/esmvaltool/references/jones93ijc.bibtex
new file mode 100644
index 0000000000..360850ffdf
--- /dev/null
+++ b/esmvaltool/references/jones93ijc.bibtex
@@ -0,0 +1,14 @@
+@article{https://doi.org/10.1002/joc.3370130606,
+    author = {Jones, P. D. and Hulme, M. and Briffa, K. R.},
+    title = {A comparison of Lamb circulation types with an objective classification scheme},
+    journal = {International Journal of Climatology},
+    volume = {13},
+    number = {6},
+    pages = {655-663},
+    keywords = {Weather types, Temperature, Precipitation, British, Isles},
+    doi = {https://doi.org/10.1002/joc.3370130606},
+    url = {https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.3370130606},
+    eprint = {https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.3370130606},
+    abstract = {Abstract An objective scheme, initially developed by Jenkinson and Collison, is used to classify daily circulation types over the British Isles, along the lines of the subjective method devised by Lamb. The scheme uses daily grid-point mean sea-level pressure data for the region. The results of the analysis over the period 1881–1989 are compared with ‘true’ Lamb weather types. The frequencies of objectively developed types are highly correlated with traditional Lamb types, especially so for synoptic (cyclonic and anticyclonic) types, although still good for wind directional types. Comparison of the two classification schemes reveals negligible differences between the correlations of the counts of circulation types and regional temperature and rainfall. The major difference between the two classification schemes is that the decline of the westerlies since 1940 is less evident with the objective scheme.},
+    year = {1993}
+}

From 0c2f5f9595b162bfb9a9a9bf6f18c9a038f90380 Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Tue, 28 Oct 2025 10:37:48 +0100
Subject: [PATCH 04/57] added dashed unerline

---
 .../diag_scripts/weathertyping/calc_utils.py  | 18 +++++++++
 .../diag_scripts/weathertyping/plot_utils.py  |  9 ++++-
 .../weathertyping/weathertyping.py            |  4 +-
 .../diag_scripts/weathertyping/wt_utils.py    | 39 +++++++++++++++++++
 4 files changed, 68 insertions(+), 2 deletions(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/calc_utils.py b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
index de55dfeee2..7d31bc94e5 100644
--- a/esmvaltool/diag_scripts/weathertyping/calc_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
@@ -47,6 +47,7 @@ def calc_slwt_obs(
         precipitation patterns over specified area.
 
     Args:
+    ----
         cfg (dict): Configuration dictionary from recipe
         lwt (np.array): Array of Lamb WT
         cube (iris.cube.Cube): preprocessor cube to keep time coordinate
@@ -56,6 +57,7 @@ def calc_slwt_obs(
         ancestors (list): list of ancestors
 
     Returns
+    -------
         np.array: _description_
     """
     logger.info("Calculating simplified Lamb Weathertypes for %s", dataset)
@@ -125,6 +127,7 @@ def calc_const():
     Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
 
     Returns
+    -------
         tuple: The four constants needed for WT calculation.
     """
     const1 = 1 / np.cos(45 * np.pi / 180)
@@ -144,9 +147,11 @@ def calc_westerly_flow(cube: iris.cube.Cube) -> np.array:
     Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
 
     Args:
+    ----
         cube (iris.cube.Cube): Cube of psl data.
 
     Returns
+    -------
         np.array: westerly flow
     """
     return 1 / 2 * (cube.data[:, 1, 2] + cube.data[:, 1, 4]) - 1 / 2 * (
@@ -163,10 +168,12 @@ def calc_southerly_flow(cube: iris.cube.Cube, const1: float) -> np.array:
     Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
 
     Args:
+    ----
         cube (iris.cube.Cube): Cube of psl data.
         const1 (float): const1
 
     Returns
+    -------
         np.array: southerly flow
     """
     return const1 * (
@@ -190,10 +197,12 @@ def calc_resultant_flow(
     Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
 
     Args:
+    ----
         westerly_flow (np.array): westerly flow.
         southerly_flow (np.array): southerly flow
 
     Returns
+    -------
         np.array: resultant flow
     """
     return (southerly_flow**2 + westerly_flow**2) ** (1 / 2)
@@ -210,11 +219,13 @@ def calc_westerly_shear_velocity(
     Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
 
     Args:
+    ----
         cube (iris.cube.Cube): cube of psl data
         const2 (float): const2
         const3 (float): const3
 
     Returns
+    -------
         np.array: westerly shear velocity
     """
     return const2 * (
@@ -237,10 +248,12 @@ def calc_southerly_shear_velocity(
     Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
 
     Args:
+    ----
         cube (iris.cube.Cube): cube of psl data
         const4 (float): const4
 
     Returns
+    -------
         np.array: southerly shear velocity
     """
     return const4 * (
@@ -270,10 +283,12 @@ def calc_total_shear_velocity(
     Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
 
     Args:
+    ----
         westerly_shear_velocity (np.array): westerly shear velocity
         southerly_shear_velocity (np.array): southerly shear velocity
 
     Returns
+    -------
         np.array: total shear velocity
     """
     return westerly_shear_velocity + southerly_shear_velocity
@@ -292,6 +307,7 @@ def wt_algorithm(cube: iris.cube.Cube, dataset: str) -> np.array:
         dataset (str): Name of dataset
 
     Returns
+    -------
         np.array: Array of Lamb WT for each day
     """
     # lats and lons corresponding to datapoints
@@ -530,6 +546,7 @@ def rmse(subarray1: np.array, subarray2: np.array) -> np.array:
         subarray2 (np.array): array2
 
     Returns
+    -------
         np.array: rsme array
     """
     return np.sqrt(np.mean((subarray1 - subarray2) ** 2))
@@ -558,6 +575,7 @@ def process_prcp_mean(
         dataset (str): Name of dataset
 
     Returns
+    -------
         list: Selected pairs of WT. This is passed to get_mapping_dict
     """
     logger.info("Calculating corr and rsme matrices for %s", dataset)
diff --git a/esmvaltool/diag_scripts/weathertyping/plot_utils.py b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
index bbad9c8c24..2601e3f23b 100644
--- a/esmvaltool/diag_scripts/weathertyping/plot_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
@@ -36,10 +36,12 @@ def generate_grayscale_hex_values(x):
     """Generate grayscale values for plotting seasonal occurrence.
 
     Args:
+    ----
         x (int): number of weathertypes
 
     Returns
-        np.list: array with grescale values as hex
+    -------
+        np.list: array with grayscale values as hex
     """
     grayscale_values = np.linspace(0, 1, x)
     grayscale_hex = [
@@ -56,6 +58,7 @@ def plot_seasonal_occurrence(
     """Plot relative monthly/seasonal occurrence of weathertypes.
 
     Args:
+    ----
         cfg (dict): Configuration dict from recipe
         wt_cubes (iris.cube.Cube): Cube with weathertypes
         data_info (dict): Dictionary with relevant info to dataset
@@ -162,6 +165,7 @@ def plot_maps(
     """Plot maps.
 
     Args:
+    ----
         wt (np.array): WT for which statistic was calculated
         cfg (dict): Configuration dicitonary from recipe
         cube (iris.cube.Cube): Data to be plotted
@@ -275,6 +279,7 @@ def plot_corr_rmse_heatmaps(
     """Plot heatmaps for correlation and rmse matrices.
 
     Args:
+    ----
         cfg (dict): cfg dict from recipe
         pattern_correlation_matrix (np.array): pattern correlation matrix
         rmse_matrix (np.array): rmse matrix
@@ -353,10 +358,12 @@ def get_colormap(colormap_string: str) -> ListedColormap:
     """Get colormaps based on string.
 
     Args:
+    ----
         colormap_string (str): String to get Colormaps for either
                                 psl, tas or precipitation.
 
     Returns
+    -------
         ListedColormap: Choosen Colormap
     """
     misc_seq_2_disc = [
diff --git a/esmvaltool/diag_scripts/weathertyping/weathertyping.py b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
index 6ea5138932..51d937a296 100644
--- a/esmvaltool/diag_scripts/weathertyping/weathertyping.py
+++ b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
@@ -13,10 +13,10 @@
     calc_lwt_model,
     calc_lwt_slwt_model,
     calc_slwt_obs,
+    plot_means,
     wt_algorithm,
 )
 from esmvaltool.diag_scripts.weathertyping.plot_utils import (
-    plot_means,
     plot_seasonal_occurrence,
 )
 from esmvaltool.diag_scripts.weathertyping.wt_utils import (
@@ -42,6 +42,7 @@ def run_automatic_slwt(cfg: dict):
     of the weathertypes.
 
     Args:
+    ----
         cfg (dict): Nested dictionary of metadata
     """
     preproc_variables_dict, _, _, work_dir, plotting, _, predefined_slwt = (
@@ -166,6 +167,7 @@ def run_lwt(cfg: dict):
     and seasonal occurrence of the weathertypes.
 
     Args:
+    ----
         cfg (dict): Nested dictionary of metadata
 
     """
diff --git a/esmvaltool/diag_scripts/weathertyping/wt_utils.py b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
index 53dddfe4e2..eda11a8cbc 100644
--- a/esmvaltool/diag_scripts/weathertyping/wt_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
@@ -29,11 +29,13 @@ def add_dict_entry(dict_: dict, key: str, value):
     """Add entry to dictionary.
 
     Args:
+    ----
         dict (dict): dictionary to add entry to
         key (str): key of entry
         value: value of entry
 
     Returns
+    -------
         dict: updated dictionary
     """
     dict_[key] = value
@@ -44,9 +46,11 @@ def get_cfg_vars(cfg: dict):
     """Get list of vars from configuration dict.
 
     Args:
+    ----
         cfg (dict): Configuration dict from recipe.
 
     Returns
+    -------
         tuple: cfg vars
     """
     preproc_variables_dict = group_metadata(
@@ -68,10 +72,12 @@ def load_wt_preprocessors(dataset: str, preproc_variables_dict: dict):
     """Load preprocessor cubes for calculating Lamb weathertypes.
 
     Args:
+    ----
         dataset (str): dataset name
         preproc_variables_dict (dict): dictionary of preprocessor variables
 
     Returns
+    -------
         _type_: list of preprocessor vars for weathertyping
     """
     wt_preproc = iris.load_cube(
@@ -91,10 +97,12 @@ def get_ancestors_era5_eobs(dataset: str, preproc_variables_dict: dict):
     """Get ancestors for ERA5/E-OBS.
 
     Args:
+    ----
         dataset (str): dataset name
         preproc_variables_dict (dict): dictionary of preprocessor variables
 
     Returns
+    -------
         _type_: lists of ERA5/E-OBS ancestors
     """
     era5_ancestors = [
@@ -112,10 +120,12 @@ def get_model_output_filepath(dataset: str, value: list):
     """Generate output filepath for model data.
 
     Args:
+    ----
         dataset (str): Model name
         value (list): Model variables
 
     Returns
+    -------
         _type_: Output filepath and preprocessor path for
         future referencing.
     """
@@ -134,10 +144,12 @@ def get_preproc_lists(preproc_vars: list):
     """Put preprocessor variables and paths into list for further use.
 
     Args:
+    ----
         preproc_vars (list): List of variables for specific
         dataset.
 
     Returns
+    -------
         _type_: List of preprocessor cubes for mean calculations
         as well as path to files.
     """
@@ -159,9 +171,11 @@ def get_preproc_lists_ensemble(preproc_vars: list):
     """Put preprocessor variables and paths into list for further use.
 
     Args:
+    ----
         preproc_vars (list): Variable for specific ensemble member.
 
     Returns
+    -------
         _type_: Preprocessor cube for mean calculations
         as well as path to files.
     """
@@ -179,9 +193,11 @@ def get_looping_dict(preproc_vars: list):
     """Put cubes into dictionary for further use.
 
     Args:
+    ----
         preproc_vars (list): Values of dataset dictionary
 
     Returns
+    -------
         _type_: Dictionary of the form {'var': [preprov_var, preproc_path]}
     """
     preproc, preproc_path = get_preproc_lists(preproc_vars)
@@ -200,12 +216,14 @@ def load_wt_files(path: str, only_lwt=False):
     weathertypes will be loaded. (useful for automatic_slwt = False)
 
     Args:
+    ----
         path (str): Path to weathertype data.
         only_lwt (bool, optional): If True,
         only Lamb weathertypes will be loaded. Defaults to False.
         (useful for automatic_slwt = False)
 
     Returns
+    -------
         _type_: List of weathertype cubes.
     """
     if not only_lwt:
@@ -230,6 +248,7 @@ def get_provenance_record(
     """Get provenance record.
 
     Args:
+    ----
         caption (str): Caption for plots
         ancestors (list): List of ancestors
         long_names (list): Variable long names
@@ -238,6 +257,7 @@ def get_provenance_record(
         statistics (bool | list): Types of statistics used.
 
     Returns
+    -------
         dict: Provenance dictionary.
     """
     record = {
@@ -260,6 +280,7 @@ def log_provenance(filename: str, cfg: dict, provenance_record: dict):
     """Log provenance. Produces xml file provenance info.
 
     Args:
+    ----
         caption (str): Caption of plots.
         filename (str): Output name of provenance.
         cfg (dict): Configuration dictionary provided by recipe.
@@ -275,9 +296,11 @@ def turn_list_to_mapping_dict(list_: list) -> dict:
     """Turn list of combined WT into a dictionary for further processing.
 
     Args:
+    ----
         list_ (list): List where entries are lists with related WT
 
     Returns
+    -------
         dict: Mapping dicitonary keys are simplified WT, values are Lamb WT
     """
     result_dict = {}
@@ -296,11 +319,13 @@ def get_mapping_dict(selected_pairs: list) -> dict:
     """Get mapping dictionary from list of selected pairs.
 
     Args:
+    ----
         selected_pairs (list): Selected pairs of WTs based on
         precipitation patterns over specified area and
         correlation and RSME thresholds defined in recipe.S
 
     Returns
+    -------
         dict: Mapping dicitonary keys are simplified WT, values are Lamb WT
     """
     mapping_array = []
@@ -328,6 +353,7 @@ def write_mapping_dict(work_dir: str, dataset: str, mapping_dict: dict):
     """Write mapping dictionary to file.
 
     Args:
+    ----
         work_dir (str): Working directory
         dataset (str): Name of dataset
         mapping_dict (dict): Mapping dictionary
@@ -346,9 +372,11 @@ def convert_dict(dict_: dict) -> dict:
     From {lwt: slwt, ...} format to {slwt: [lwt1, lwt2], ...}.
 
     Args:
+    ----
         dict_ (dict): Dict in the {lwt: slwt, ...} format
 
     Returns
+    -------
         dict: Dict in the {slwt: [lwt1, lwt2], ...} format
     """
     new_dict = {}
@@ -365,9 +393,11 @@ def reverse_convert_dict(dict_: dict) -> dict:
     From {slwt: [lwt1, lwt2], ...} format to {lwt: slwt, ...}.
 
     Args:
+    ----
         original_dict (dict): Dict in the {slwt: [lwt1, lwt2], ...}format
 
     Returns
+    -------
         dict: Dict in the  format {lwt: slwt, ...}
     """
     new_dict = {}
@@ -386,6 +416,7 @@ def write_corr_rmse_to_csv(
     """Write correlation and rsme matrix to csv files.
 
     Args:
+    ----
         cfg (dict): Configuration dictionary from recipe
         pattern_correlation_matrix (np.array): Correlation matrix
         rmse_matrix (np.array): RSME matrix
@@ -416,12 +447,14 @@ def run_predefined_slwt(
     """Run predefined slwt mapping.
 
     Args:
+    ----
         work_dir (str): Working directory to save mapping dict
         dataset_name (str): Name of dataset
         lwt (np.array): lwt array
         predefined_slwt (dict): Predefined mapping dict
 
     Returns
+    -------
         np.array: slwt_era5 array
         np.array: slwt_eobs array
     """
@@ -440,6 +473,7 @@ def combine_wt_to_file(
     """Combine lwt and slwt arrays to one file.
 
     Args:
+    ----
         cfg (dict): Configuration dictionary from recipe
         wt_list (list): List of weathertype arrays
         cube (iris.cube.Cube): Cube of data to keep time coordinate
@@ -484,6 +518,7 @@ def write_lwt_to_file(
     """Write only lwt to file.
 
     Args:
+    ----
         cfg (dict): Configuration dictionary from recipe
         lwt (np.array): lwt array
         cube (iris.cube.Cube): Cube to keep time coordinate
@@ -512,10 +547,12 @@ def map_lwt_to_slwt(lwt: np.array, mapping_dict: dict) -> np.array:
     """Map lwt array to slwt array.
 
     Args:
+    ----
         lwt (np.array): array of lwt
         mapping_dict (dict): mapping dictionary in {lwt: slwt, ...} format
 
     Returns
+    -------
         np.array: array of slwt
     """
     return np.array([np.int8(mapping_dict.get(value, 0)) for value in lwt])
@@ -525,9 +562,11 @@ def check_mapping_dict_format(mapping_dict: dict) -> dict:
     """Check format of mapping dict and return in {lwt: slwt, ...} format.
 
     Args:
+    ----
         mapping_dict (dict): mapping dict in any format
 
     Returns
+    -------
         dict: mapping dict in {lwt: slwt, ...} format
     """
     if isinstance(mapping_dict.get(list(mapping_dict.keys())[0]), list):

From 8fecb2c2564af871f57a1b71a506e984f17111ff Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Tue, 28 Oct 2025 11:15:38 +0100
Subject: [PATCH 05/57] changed docstring format

---
 .../diag_scripts/weathertyping/calc_utils.py  | 209 +++++++++++------
 .../diag_scripts/weathertyping/plot_utils.py  |  53 +++--
 .../weathertyping/weathertyping.py            |  53 ++---
 .../diag_scripts/weathertyping/wt_utils.py    | 222 +++++++++++-------
 4 files changed, 324 insertions(+), 213 deletions(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/calc_utils.py b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
index 7d31bc94e5..724ef9baa5 100644
--- a/esmvaltool/diag_scripts/weathertyping/calc_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
@@ -48,17 +48,23 @@ def calc_slwt_obs(
 
     Args:
     ----
-        cfg (dict): Configuration dictionary from recipe
-        lwt (np.array): Array of Lamb WT
-        cube (iris.cube.Cube): preprocessor cube to keep time coordinate
-        dataset (str): Name of dataset
-        correlation_thresold (float): correlation_threshold
-        rmse_threshold (float): rsme_threshold
-        ancestors (list): list of ancestors
+        cfg : dict
+            Configuration dictionary from recipe
+        lwt : np.array
+            Array of Lamb WT
+        cube : iris.cube.Cube
+            Cube of psl data
+        dataset : str
+            Name of dataset
+        ancestors : list
+            List of ancestors
+        timerange : str
+            Time range for the calculation
 
     Returns
     -------
-        np.array: _description_
+        np.array
+            Simplified Lamb Weathertypes
     """
     logger.info("Calculating simplified Lamb Weathertypes for %s", dataset)
 
@@ -128,7 +134,8 @@ def calc_const():
 
     Returns
     -------
-        tuple: The four constants needed for WT calculation.
+        tuple
+            The four constants needed for WT calculation.
     """
     const1 = 1 / np.cos(45 * np.pi / 180)
     const2 = np.sin(45 * np.pi / 180) / np.sin(40 * np.pi / 180)
@@ -148,11 +155,13 @@ def calc_westerly_flow(cube: iris.cube.Cube) -> np.array:
 
     Args:
     ----
-        cube (iris.cube.Cube): Cube of psl data.
+        cube : iris.cube.Cube
+            Cube of psl data.
 
     Returns
     -------
-        np.array: westerly flow
+        np.array
+            westerly flow
     """
     return 1 / 2 * (cube.data[:, 1, 2] + cube.data[:, 1, 4]) - 1 / 2 * (
         cube.data[:, 3, 2] + cube.data[:, 3, 4]
@@ -169,12 +178,14 @@ def calc_southerly_flow(cube: iris.cube.Cube, const1: float) -> np.array:
 
     Args:
     ----
-        cube (iris.cube.Cube): Cube of psl data.
-        const1 (float): const1
-
+        cube : iris.cube.Cube
+            cube of psl data
+        const1 : float
+            const1
     Returns
     -------
-        np.array: southerly flow
+        np.array
+            southerly flow
     """
     return const1 * (
         1
@@ -198,12 +209,15 @@ def calc_resultant_flow(
 
     Args:
     ----
-        westerly_flow (np.array): westerly flow.
-        southerly_flow (np.array): southerly flow
+        westerly_flow : np.array
+            westerly flow
+        southerly_flow : np.array
+            southerly flow
 
     Returns
     -------
-        np.array: resultant flow
+        np.array
+            resultant flow
     """
     return (southerly_flow**2 + westerly_flow**2) ** (1 / 2)
 
@@ -220,13 +234,17 @@ def calc_westerly_shear_velocity(
 
     Args:
     ----
-        cube (iris.cube.Cube): cube of psl data
-        const2 (float): const2
-        const3 (float): const3
+        cube : iris.cube.Cube
+            cube of psl data
+        const2 : float
+            const2
+        const3 : float
+            const3
 
     Returns
     -------
-        np.array: westerly shear velocity
+        np.array
+            westerly shear velocity
     """
     return const2 * (
         1 / 2 * (cube.data[:, 0, 2] + cube.data[:, 0, 4])
@@ -249,12 +267,15 @@ def calc_southerly_shear_velocity(
 
     Args:
     ----
-        cube (iris.cube.Cube): cube of psl data
-        const4 (float): const4
+        cube : iris.cube.Cube
+            cube of psl data
+        const4 : float
+            const
 
     Returns
     -------
-        np.array: southerly shear velocity
+        np.array
+            southerly shear velocity
     """
     return const4 * (
         1
@@ -284,12 +305,15 @@ def calc_total_shear_velocity(
 
     Args:
     ----
-        westerly_shear_velocity (np.array): westerly shear velocity
-        southerly_shear_velocity (np.array): southerly shear velocity
+        westerly_shear_velocity : np.array
+            westerly shear velocity
+        southerly_shear_velocity : np.array
+            southerly shear velocity
 
     Returns
     -------
-        np.array: total shear velocity
+        np.array
+            total shear velocity
     """
     return westerly_shear_velocity + southerly_shear_velocity
 
@@ -303,12 +327,16 @@ def wt_algorithm(cube: iris.cube.Cube, dataset: str) -> np.array:
     Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
 
     Args:
-        cube (iris.cube.Cube): PSL field of dataset
-        dataset (str): Name of dataset
+    ----
+        cube : iris.cube.Cube
+            Cube of psl data.
+        dataset : str
+            Name of dataset.
 
     Returns
     -------
-        np.array: Array of Lamb WT for each day
+        np.array
+            Array of Lamb WT for each day
     """
     # lats and lons corresponding to datapoints
     # 55, 5 -> 1
@@ -442,12 +470,17 @@ def calc_lwt_slwt_model(
     """Calculate Lamb WT and simplified WT for model data.
 
     Args:
-        cfg (dict): Configuration dicitonary from recipe
-        cube (iris.cube.Cube): PSL field of dataset
-        data_info (dict): Dictionary with info to dataset
-        predefined_slwt (bool | dict): If False, automatic_slwt will be used.
-        If dict, this mapping dict will be used.
-        (see recipe option predefined_slwt)
+    ----
+        cfg : dict
+            Configuration dictionary from recipe
+        cube : iris.cube.Cube
+            PSL field of dataset
+        data_info : dict
+            Dictionary with info to dataset
+        predefined_slwt : bool | dict
+            If False, automatic_slwt will be used.
+            If dict, this mapping dict will be used.
+            (see recipe option predefined_slwt)
     """
     work_dir = cfg.get("work_dir")
     dataset = data_info.get("dataset")
@@ -542,12 +575,15 @@ def rmse(subarray1: np.array, subarray2: np.array) -> np.array:
     """Calculate rsme.
 
     Args:
-        subarray1 (np.array): array1
-        subarray2 (np.array): array2
+        subarray1 : np.array
+            First subarray
+        subarray2 : np.array
+            Second subarray
 
     Returns
     -------
-        np.array: rsme array
+        np.array
+            rsme array
     """
     return np.sqrt(np.mean((subarray1 - subarray2) ** 2))
 
@@ -568,15 +604,23 @@ def process_prcp_mean(
     further processing and simplifying the WT.
 
     Args:
-        cfg (dict): Configuration dictionary from recipe
-        data (np.array): Precipitation data
-        correlation_threshold (float): Correlation threshold
-        rmse_threshold (float): RMSE threshold
-        dataset (str): Name of dataset
+        cfg : dict
+            Configuration dictionary from recipe
+        data : np.array
+            Array of precipitation means for each WT
+        correlation_threshold : float
+            correlation threshold
+        rmse_threshold : float
+            rsme threshold
+        dataset : str
+            Name of dataset
+        timerange : str
+            Time range of dataset
 
     Returns
     -------
-        list: Selected pairs of WT. This is passed to get_mapping_dict
+        list
+            Selected pairs of WT. This is passed to get_mapping_dict
     """
     logger.info("Calculating corr and rsme matrices for %s", dataset)
 
@@ -622,12 +666,14 @@ def calc_wt_means(
     """Calculate means of fields of each weathertype.
 
     Args:
-        cfg (dict): Configuration dictionary from recipe
-        cube (iris.cube.Cube): Cube with variable data
-        wt_cubes (iris.cube.CubeList): List of cubes of lwt, slwt_ERA5
-                                        and slwt_EOBS
-        data_info (dict): Dictionary with info to dataset
-        preproc_path (str): Ancestor path
+        cfg : dict
+            Configuration dictionary from recipe
+        cube : iris.cube.Cube
+            Cube with variable data
+        wt_cubes : iris.cube.CubeList
+            List of cubes of lwt, slwt_ERA5 and slwt_EOBS
+        data_info : dict
+            Dictionary with info to dataset
     """
     dataset = data_info.get("dataset")
     var_name = data_info.get("var")
@@ -739,12 +785,14 @@ def calc_wt_anomalies(
     """Calculate anomalies of fields of each weathertype.
 
     Args:
-        cfg (dict): Configuration dictionary from recipe
-        cube (iris.cube.Cube): Cube with variable data
-        wt_cubes (iris.cube.CubeList): List of cubes of lwt, slwt_ERA5
-                                        and slwt_EOBS
-        data_info (dict): Dictionary with info to dataset
-        preproc_path (str): Ancestor path
+        cfg : dict
+            Configuration dictionary from recipe
+        cube : iris.cube.Cube
+            Cube with variable data
+        wt_cubes : iris.cube.CubeList
+            List of cubes of lwt, slwt_ERA5 and slwt_EOBS
+        data_info : dict
+            Dictionary with info to dataset
     """
     work_dir = cfg.get("work_dir")
     dataset = data_info.get("dataset")
@@ -872,12 +920,14 @@ def calc_wt_std(
     """Calculate standard deviation of fields of each weathertype.
 
     Args:
-        cfg (dict): Configuration dictionary from recipe
-        cube (iris.cube.Cube): Cube with variable data
-        wt_cubes (iris.cube.CubeList): List of cubes of lwt, slwt_ERA5
-                                        and slwt_EOBS
-        data_info (dict): Dictionary with info to dataset
-        preproc_path (str): Ancestor path
+        cfg : dict
+            Configuration dictionary from recipe
+        cube : iris.cube.Cube
+            Cube with variable data
+        wt_cubes : iris.cube.CubeList
+            List of cubes of lwt, slwt_ERA5 and slwt_EOBS
+        data_info : dict
+            Dictionary with info to dataset
     """
     work_dir = cfg.get("work_dir")
     dataset = data_info.get("dataset")
@@ -998,10 +1048,15 @@ def calc_lwt_model(
     """Calculate lwt for model data.
 
     Args:
-        cfg (dict): Configuration dictionary from recipe
-        cube (iris.cube.Cube): Cube to keep time coordinate
-        dataset (str): Name of dataset
-        data_info (dict): Dictionary with info to dataset
+    ----
+        cfg : dict
+            Configuration dictionary from recipe
+        cube : iris.cube.Cube
+            Cube to keep time coordinate
+        dataset : str
+            Name of dataset
+        data_info : dict
+            Dictionary with info to dataset
     """
     work_dir = cfg.get("work_dir")
     dataset = data_info.get("dataset")
@@ -1072,13 +1127,17 @@ def plot_means(
     """Wrapper function to plot various means/std/anomalies.
 
     Args:
-        cfg (dict): cfg dictionary provided by recipe
-        preproc_var (np.array): variable to be plotted
-        wt_cubes (iris.cube.Cube): list of wt cubes
-        data_info (dict): dictionary with info to dataset
-        only_lwt (bool, optional): If True,
-        only Lamb weathertypes will be loaded. Defaults to False.
-        (useful for automatic_slwt = False)
+        cfg : dict
+            Configuration dictionary from recipe
+        preproc_var : np.array
+            Preprocessed variable cube
+        wt_cubes : iris.cube.Cube
+            List of cubes of lwt, slwt_ERA5 and slwt_EOBS
+        data_info : dict
+            Dictionary with info to dataset
+        only_lwt : bool
+            If True, only lwt means are calculated.
+            If False, lwt, slwt_ERA5 and slwt_EOBS means are calculated.
     """
     if not only_lwt:
         data_info["wt_string"] = "lwt"
diff --git a/esmvaltool/diag_scripts/weathertyping/plot_utils.py b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
index 2601e3f23b..7d30f9332b 100644
--- a/esmvaltool/diag_scripts/weathertyping/plot_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
@@ -37,11 +37,13 @@ def generate_grayscale_hex_values(x):
 
     Args:
     ----
-        x (int): number of weathertypes
+        x : int
+            number of weathertypes
 
     Returns
     -------
-        np.list: array with grayscale values as hex
+        np.list
+            array with grayscale values as hex
     """
     grayscale_values = np.linspace(0, 1, x)
     grayscale_hex = [
@@ -59,9 +61,12 @@ def plot_seasonal_occurrence(
 
     Args:
     ----
-        cfg (dict): Configuration dict from recipe
-        wt_cubes (iris.cube.Cube): Cube with weathertypes
-        data_info (dict): Dictionary with relevant info to dataset
+        cfg : dict
+            Configuration dictionary from recipe
+        wt_cubes : iris.cube.Cube
+            Cube with weathertypes
+        data_info : dict
+            Dictionary with relevant info to dataset
     """
     dataset_name = data_info.get("dataset")
     timerange = data_info.get("timerange")
@@ -166,11 +171,16 @@ def plot_maps(
 
     Args:
     ----
-        wt (np.array): WT for which statistic was calculated
-        cfg (dict): Configuration dicitonary from recipe
-        cube (iris.cube.Cube): Data to be plotted
-        data_info (dict): Dictionary with info to dataset
-        mode (str): Statistics that is used
+        wt : np.array
+            weathertype number
+        cfg : dict
+            Configuration dicitonary from recipe
+        cube : iris.cube.Cube
+            Data to be plotted
+        data_info : dict
+            Dictionary with info to dataset
+        mode : str
+            Statistics that is used
     """
     dataset = data_info.get("dataset")
     var_name = data_info.get("var")
@@ -280,11 +290,16 @@ def plot_corr_rmse_heatmaps(
 
     Args:
     ----
-        cfg (dict): cfg dict from recipe
-        pattern_correlation_matrix (np.array): pattern correlation matrix
-        rmse_matrix (np.array): rmse matrix
-        dataset (str): string of dataset
-        timerange (str): string of timerange
+        cfg : dict
+            Configuration dictionary from recipe
+        pattern_correlation_matrix : np.array
+            pattern correlation matrix
+        rmse_matrix : np.array
+            rmse matrix
+        dataset : str
+            string of dataset
+        timerange : str
+            string of timerange
     """
     output_path = f"{cfg.get('plot_dir')}/heatmaps"
 
@@ -359,12 +374,14 @@ def get_colormap(colormap_string: str) -> ListedColormap:
 
     Args:
     ----
-        colormap_string (str): String to get Colormaps for either
-                                psl, tas or precipitation.
+        colormap_string : str
+            String to get Colormaps for either
+            psl, tas or precipitation.
 
     Returns
     -------
-        ListedColormap: Choosen Colormap
+        ListedColormap
+            Choosen Colormap
     """
     misc_seq_2_disc = [
         (230 / 255, 240 / 255, 240 / 255),
diff --git a/esmvaltool/diag_scripts/weathertyping/weathertyping.py b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
index 51d937a296..841b8b9b06 100644
--- a/esmvaltool/diag_scripts/weathertyping/weathertyping.py
+++ b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
@@ -20,7 +20,6 @@
     plot_seasonal_occurrence,
 )
 from esmvaltool.diag_scripts.weathertyping.wt_utils import (
-    add_dict_entry,
     combine_wt_to_file,
     get_ancestors_era5_eobs,
     get_cfg_vars,
@@ -43,7 +42,8 @@ def run_automatic_slwt(cfg: dict):
 
     Args:
     ----
-        cfg (dict): Nested dictionary of metadata
+        cfg : dict
+            Nested dictionary of metadata
     """
     preproc_variables_dict, _, _, work_dir, plotting, _, predefined_slwt = (
         get_cfg_vars(cfg)
@@ -102,8 +102,8 @@ def run_automatic_slwt(cfg: dict):
             if plotting:
                 # plot means
                 for var_name, var_data in var_dict.items():
-                    add_dict_entry(data_info, "var", var_name)
-                    add_dict_entry(data_info, "preproc_path", var_data[1])
+                    data_info["var"] = var_name
+                    data_info["preproc_path"] = var_data[1]
 
                     plot_means(cfg, var_data[0], wt_cubes, data_info)
                 plot_seasonal_occurrence(cfg, wt_cubes, data_info)
@@ -112,14 +112,11 @@ def run_automatic_slwt(cfg: dict):
                 continue
             for ensemble_var in dataset_vars:
                 if ensemble_var.get("preprocessor") == "weathertype_preproc":
-                    add_dict_entry(
-                        data_info, "ensemble", ensemble_var.get("ensemble", "")
-                    )
-                    add_dict_entry(
-                        data_info, "driver", ensemble_var.get("driver", "")
-                    )
+                    data_info["ensemble"] = ensemble_var.get("ensemble", "")
+                    data_info["driver"] = ensemble_var.get("driver", "")
+
                     if data_info["driver"] != "":
-                        data_info["driver"] = "_" + {data_info["driver"]}
+                        data_info["driver"] = "_" + data_info["driver"]
 
                     wt_preproc = iris.load_cube(ensemble_var.get("filename"))
 
@@ -153,10 +150,8 @@ def run_automatic_slwt(cfg: dict):
                     # plot means
                     if plotting:
                         for var_name, var_data in var_dict.items():
-                            add_dict_entry(data_info, "var", var_name)
-                            add_dict_entry(
-                                data_info, "preproc_path", var_data[1]
-                            )
+                            data_info["var"] = var_name
+                            data_info["preproc_path"] = var_data[1]
 
                             plot_means(cfg, var_data[0], wt_cubes, data_info)
                         plot_seasonal_occurrence(cfg, wt_cubes, data_info)
@@ -168,7 +163,8 @@ def run_lwt(cfg: dict):
 
     Args:
     ----
-        cfg (dict): Nested dictionary of metadata
+        cfg : dict
+            Nested dictionary of metadata
 
     """
     preproc_variables_dict, _, _, work_dir, plotting, _, _ = get_cfg_vars(cfg)
@@ -217,8 +213,8 @@ def run_lwt(cfg: dict):
             if plotting:
                 # plot means
                 for var_name, var_data in var_dict.items():
-                    add_dict_entry(data_info, "var", var_name)
-                    add_dict_entry(data_info, "preproc_path", var_data[1])
+                    data_info["var"] = var_name
+                    data_info["preproc_path"] = var_data[1]
 
                     plot_means(
                         cfg, var_data[0], wt_cubes, data_info, only_lwt=True
@@ -229,12 +225,9 @@ def run_lwt(cfg: dict):
                 continue
             for ensemble_var in dataset_vars:
                 if ensemble_var.get("preprocessor") == "weathertype_preproc":
-                    add_dict_entry(
-                        data_info, "ensemble", ensemble_var.get("ensemble", "")
-                    )
-                    add_dict_entry(
-                        data_info, "driver", ensemble_var.get("driver", "")
-                    )
+                    data_info["ensemble"] = ensemble_var.get("ensemble", "")
+                    data_info["driver"] = ensemble_var.get("driver", "")
+
                     if data_info["driver"] != "":
                         data_info["driver"] = "_" + {data_info["driver"]}
 
@@ -269,10 +262,8 @@ def run_lwt(cfg: dict):
                     if plotting:
                         # plot means
                         for var_name, var_data in var_dict.items():
-                            add_dict_entry(data_info, "var", var_name)
-                            add_dict_entry(
-                                data_info, "preproc_path", var_data[1]
-                            )
+                            data_info["var"] = var_name
+                            data_info["preproc_path"] = var_data[1]
 
                             plot_means(
                                 cfg,
@@ -288,10 +279,12 @@ def run_my_diagnostic(cfg: dict):
     """Run the weathertyping diagnostic.
 
     Arguments:
-        cfg - nested dictionary of metadata
+        cfg : dict
+            nested dictionary of metadata
 
     Returns
-        string; runs the user diagnostic
+        string
+            runs the user diagnostic
 
     """
     # assemble the data dictionary keyed by dataset name
diff --git a/esmvaltool/diag_scripts/weathertyping/wt_utils.py b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
index eda11a8cbc..4f905dff88 100644
--- a/esmvaltool/diag_scripts/weathertyping/wt_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
@@ -25,33 +25,18 @@
 warnings.filterwarnings("ignore", ".*Collapsing a non-contiguous coordinate*")
 
 
-def add_dict_entry(dict_: dict, key: str, value):
-    """Add entry to dictionary.
-
-    Args:
-    ----
-        dict (dict): dictionary to add entry to
-        key (str): key of entry
-        value: value of entry
-
-    Returns
-    -------
-        dict: updated dictionary
-    """
-    dict_[key] = value
-    return dict_
-
-
 def get_cfg_vars(cfg: dict):
     """Get list of vars from configuration dict.
 
     Args:
     ----
-        cfg (dict): Configuration dict from recipe.
+        cfg : dict
+            Configuration dict from recipe.
 
     Returns
     -------
-        tuple: cfg vars
+        tuple
+            cfg vars
     """
     preproc_variables_dict = group_metadata(
         cfg.get("input_data").values(), "dataset"
@@ -73,12 +58,15 @@ def load_wt_preprocessors(dataset: str, preproc_variables_dict: dict):
 
     Args:
     ----
-        dataset (str): dataset name
-        preproc_variables_dict (dict): dictionary of preprocessor variables
+        dataset : str
+            dataset name
+        preproc_variables_dict : dict
+            dictionary of preprocessor variables
 
     Returns
     -------
-        _type_: list of preprocessor vars for weathertyping
+        list
+            list of preprocessor vars for weathertyping
     """
     wt_preproc = iris.load_cube(
         preproc_variables_dict.get(dataset)[0].get("filename")
@@ -98,12 +86,15 @@ def get_ancestors_era5_eobs(dataset: str, preproc_variables_dict: dict):
 
     Args:
     ----
-        dataset (str): dataset name
-        preproc_variables_dict (dict): dictionary of preprocessor variables
+       dataset : str
+            dataset name
+        preproc_variables_dict : dict
+            dictionary of preprocessor variables
 
     Returns
     -------
-        _type_: lists of ERA5/E-OBS ancestors
+        tuple(list, list)
+            lists of ERA5/E-OBS ancestors
     """
     era5_ancestors = [
         preproc_variables_dict.get(dataset)[0].get("filename"),
@@ -121,13 +112,16 @@ def get_model_output_filepath(dataset: str, value: list):
 
     Args:
     ----
-        dataset (str): Model name
-        value (list): Model variables
+        dataset : str
+            dataset name
+        value : dict
+            Model variables
 
     Returns
     -------
-        _type_: Output filepath and preprocessor path for
-        future referencing.
+        tuple(str, str)
+            Output filepath and preprocessor path for
+            future referencing.
     """
     timerange = value.get("timerange").replace("/", "-")
     experiment = value.get("exp")
@@ -145,13 +139,15 @@ def get_preproc_lists(preproc_vars: list):
 
     Args:
     ----
-        preproc_vars (list): List of variables for specific
-        dataset.
+        preproc_vars : list
+            List of variables for specific
+            dataset
 
     Returns
     -------
-        _type_: List of preprocessor cubes for mean calculations
-        as well as path to files.
+        tuple(list, list)
+            List of preprocessor cubes for mean calculations
+            as well as path to files.
     """
     preproc_path_psl = preproc_vars[-3].get("filename")
     preproc_path_prcp = preproc_vars[-2].get("filename")
@@ -172,12 +168,14 @@ def get_preproc_lists_ensemble(preproc_vars: list):
 
     Args:
     ----
-        preproc_vars (list): Variable for specific ensemble member.
+        preproc_vars : list
+            Variable for specific ensemble member.
 
     Returns
     -------
-        _type_: Preprocessor cube for mean calculations
-        as well as path to files.
+        tuple(iris.cube.Cube, str)
+            Preprocessor cube for mean calculations
+            as well as path to files.
     """
     preproc_path = preproc_vars.get("filename")
 
@@ -194,11 +192,12 @@ def get_looping_dict(preproc_vars: list):
 
     Args:
     ----
-        preproc_vars (list): Values of dataset dictionary
+        preproc_vars : list
+            Values of dataset dictionary
 
     Returns
     -------
-        _type_: Dictionary of the form {'var': [preprov_var, preproc_path]}
+        Dictionary of the form {'var': [preproc_var, preproc_path]}
     """
     preproc, preproc_path = get_preproc_lists(preproc_vars)
     dict_ = {
@@ -217,14 +216,16 @@ def load_wt_files(path: str, only_lwt=False):
 
     Args:
     ----
-        path (str): Path to weathertype data.
-        only_lwt (bool, optional): If True,
-        only Lamb weathertypes will be loaded. Defaults to False.
-        (useful for automatic_slwt = False)
+        path : str
+            Path to weathertype data.
+        only_lwt : bool, optional
+            If True, only Lamb weathertypes will be loaded.
+            Defaults to False.
 
     Returns
     -------
-        _type_: List of weathertype cubes.
+        list(iris.cube.Cube)
+            List of weathertype cubes.
     """
     if not only_lwt:
         lwt_cube = iris.load_cube(path, "lwt")
@@ -249,16 +250,21 @@ def get_provenance_record(
 
     Args:
     ----
-        caption (str): Caption for plots
-        ancestors (list): List of ancestors
-        long_names (list): Variable long names
-        plot_types (bool | list): Type of plot. Can be false
-        if output is not a plot.
-        statistics (bool | list): Types of statistics used.
+        caption : str
+            Caption for plots
+        ancestors : list
+            List of ancestors
+        long_names : list
+            Variable long names
+        plot_types : bool | list
+            Type of plot. Can be false if output is not a plot.
+        statistics : bool | list
+            Types of statistics used.
 
     Returns
     -------
-        dict: Provenance dictionary.
+        dict
+            Provenance dictionary.
     """
     record = {
         "caption": caption,
@@ -281,10 +287,12 @@ def log_provenance(filename: str, cfg: dict, provenance_record: dict):
 
     Args:
     ----
-        caption (str): Caption of plots.
-        filename (str): Output name of provenance.
-        cfg (dict): Configuration dictionary provided by recipe.
-        provenance_record (dict): Provenance record dictionary.
+        filename : str
+            Output name of provenance.
+        cfg : dict
+            Configuration dictionary provided by recipe.
+        provenance_record : dict
+            Provenance record dictionary.
     """
     with ProvenanceLogger(cfg) as provenance_logger:
         provenance_logger.log(filename, provenance_record)
@@ -297,11 +305,13 @@ def turn_list_to_mapping_dict(list_: list) -> dict:
 
     Args:
     ----
-        list_ (list): List where entries are lists with related WT
+        list_ : list
+            List where entries are lists with related WT
 
     Returns
     -------
-        dict: Mapping dicitonary keys are simplified WT, values are Lamb WT
+        dict
+            Mapping dictionary keys are simplified WT, values are Lamb WT
     """
     result_dict = {}
 
@@ -320,13 +330,15 @@ def get_mapping_dict(selected_pairs: list) -> dict:
 
     Args:
     ----
-        selected_pairs (list): Selected pairs of WTs based on
-        precipitation patterns over specified area and
-        correlation and RSME thresholds defined in recipe.S
+        selected_pairs : list
+            List of selected pairs of related WT based on
+            precipitation patterns over specified area and
+            correlation and RSME thresholds defined in recipe
 
     Returns
     -------
-        dict: Mapping dicitonary keys are simplified WT, values are Lamb WT
+        dict
+            Mapping dicitonary keys are simplified WT, values are Lamb WT
     """
     mapping_array = []
 
@@ -354,9 +366,12 @@ def write_mapping_dict(work_dir: str, dataset: str, mapping_dict: dict):
 
     Args:
     ----
-        work_dir (str): Working directory
-        dataset (str): Name of dataset
-        mapping_dict (dict): Mapping dictionary
+        work_dir : str
+            Working directory to save mapping dict
+        dataset : str
+            Name of dataset
+        mapping_dict : dict
+            Mapping dictionary in {lwt: slwt, ...} format
     """
     mapping_dict_reformat = convert_dict(mapping_dict)
 
@@ -373,11 +388,13 @@ def convert_dict(dict_: dict) -> dict:
 
     Args:
     ----
-        dict_ (dict): Dict in the {lwt: slwt, ...} format
+        dict_ : dict
+            Dict in the {lwt: slwt, ...} format
 
     Returns
     -------
-        dict: Dict in the {slwt: [lwt1, lwt2], ...} format
+        dict
+            Dict in the {slwt: [lwt1, lwt2], ...} format
     """
     new_dict = {}
     for dataset, value in dict_.items():
@@ -394,11 +411,13 @@ def reverse_convert_dict(dict_: dict) -> dict:
 
     Args:
     ----
-        original_dict (dict): Dict in the {slwt: [lwt1, lwt2], ...}format
+        original_dict : dict
+        Dict in the {slwt: [lwt1, lwt2], ...} format
 
     Returns
     -------
-        dict: Dict in the  format {lwt: slwt, ...}
+        dict
+            Dict in the  format {lwt: slwt, ...}
     """
     new_dict = {}
     for key, value_list in dict_.items():
@@ -417,10 +436,14 @@ def write_corr_rmse_to_csv(
 
     Args:
     ----
-        cfg (dict): Configuration dictionary from recipe
-        pattern_correlation_matrix (np.array): Correlation matrix
-        rmse_matrix (np.array): RSME matrix
-        dataset (str): Name of dataset
+        cfg : dict
+            Configuration dictionary from recipe
+        pattern_correlation_matrix : np.array
+            Correlation matrix
+        rmse_matrix : np.array
+            RSME matrix
+        dataset : str
+            Name of dataset
     """
     logger.info("Writing corr and rsme matrices for %s", dataset)
 
@@ -448,15 +471,21 @@ def run_predefined_slwt(
 
     Args:
     ----
-        work_dir (str): Working directory to save mapping dict
-        dataset_name (str): Name of dataset
-        lwt (np.array): lwt array
-        predefined_slwt (dict): Predefined mapping dict
+        work_dir : str
+            Working directory to save mapping dict
+        dataset_name : str
+            Name of dataset
+        lwt : np.array
+            lwt array
+        predefined_slwt : dict
+            Mapping dictionary in {lwt: slwt, ...} format
 
     Returns
     -------
-        np.array: slwt_era5 array
-        np.array: slwt_eobs array
+        np.array
+            slwt_era5 array
+        np.array
+            slwt_eobs array
     """
     predefined_slwt = check_mapping_dict_format(predefined_slwt)
     write_mapping_dict(work_dir, dataset_name, predefined_slwt)
@@ -474,10 +503,14 @@ def combine_wt_to_file(
 
     Args:
     ----
-        cfg (dict): Configuration dictionary from recipe
-        wt_list (list): List of weathertype arrays
-        cube (iris.cube.Cube): Cube of data to keep time coordinate
-        file_name (str): Name of output file
+        cfg : dict
+            Configuration dictionary from recipe
+        wt_list : list
+            List of weathertype arrays
+        cube : iris.cube.Cube
+            Cube of data to keep time coordinate
+        file_name : str
+            Name of output file
     """
     lwt = wt_list[0]
     slwt_era5 = wt_list[1]
@@ -519,10 +552,14 @@ def write_lwt_to_file(
 
     Args:
     ----
-        cfg (dict): Configuration dictionary from recipe
-        lwt (np.array): lwt array
-        cube (iris.cube.Cube): Cube to keep time coordinate
-        file_name (str): Output filename
+        cfg : dict
+            Configuration dictionary from recipe
+        lwt : np.array
+            lwt array
+        cube : iris.cube.Cube
+            Cube of data to keep time coordinate
+        file_name : str
+            Name of output file
     """
     logger.info("Writing Lamb Weathertype to %s", file_name)
 
@@ -548,12 +585,15 @@ def map_lwt_to_slwt(lwt: np.array, mapping_dict: dict) -> np.array:
 
     Args:
     ----
-        lwt (np.array): array of lwt
-        mapping_dict (dict): mapping dictionary in {lwt: slwt, ...} format
+        lwt : np.array
+            lwt array
+        mapping_dict : dict
+            Mapping dictionary in {lwt: slwt, ...} format
 
     Returns
     -------
-        np.array: array of slwt
+        np.array
+            array of slwt
     """
     return np.array([np.int8(mapping_dict.get(value, 0)) for value in lwt])
 
@@ -563,11 +603,13 @@ def check_mapping_dict_format(mapping_dict: dict) -> dict:
 
     Args:
     ----
-        mapping_dict (dict): mapping dict in any format
+        mapping_dict : dict
+            mapping dict in any format
 
     Returns
     -------
-        dict: mapping dict in {lwt: slwt, ...} format
+        dict
+            mapping dict in {lwt: slwt, ...} format
     """
     if isinstance(mapping_dict.get(list(mapping_dict.keys())[0]), list):
         dict_ = reverse_convert_dict(mapping_dict)

From ce833be3bf893767e828e8dd59c6bab18493c824 Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Tue, 4 Nov 2025 09:08:45 +0100
Subject: [PATCH 06/57] using less locals, refactoring weathertyping.py

---
 .../diag_scripts/weathertyping/calc_utils.py  | 195 ++++----
 .../weathertyping/weathertyping.py            | 439 ++++++++++--------
 2 files changed, 348 insertions(+), 286 deletions(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/calc_utils.py b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
index 724ef9baa5..85c5e3c8e9 100644
--- a/esmvaltool/diag_scripts/weathertyping/calc_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
@@ -68,11 +68,6 @@ def calc_slwt_obs(
     """
     logger.info("Calculating simplified Lamb Weathertypes for %s", dataset)
 
-    work_dir = cfg.get("work_dir")
-    correlation_threshold = cfg.get("correlation_threshold")
-    rmse_threshold = cfg.get("rmse_threshold")
-    tcoord = cube.coord("time")
-
     wt_data_prcp = []
     for wt_ in range(1, 28):
         target_indices = np.where(lwt == wt_)
@@ -85,7 +80,8 @@ def calc_slwt_obs(
             )
             continue
         dates = [
-            tcoord.units.num2date(tcoord.points[i]) for i in target_indices
+            cube.coord("time").units.num2date(cube.coord("time").points[i])
+            for i in target_indices
         ]
         if dataset == "E-OBS":
             extracted_cube = cube[target_indices]
@@ -98,27 +94,29 @@ def calc_slwt_obs(
     selected_pairs = process_prcp_mean(
         cfg,
         wt_data_prcp,
-        correlation_threshold,
-        rmse_threshold,
         dataset,
         timerange,
     )
 
     with open(
-        f"{work_dir}/wt_selected_pairs_{dataset}.json", "w", encoding="utf-8"
+        f"{cfg.get('work_dir')}/wt_selected_pairs_{dataset}.json",
+        "w",
+        encoding="utf-8",
     ) as file:
         json.dump(selected_pairs, file)
 
     mapping_dict = get_mapping_dict(selected_pairs)
 
-    write_mapping_dict(work_dir, dataset, mapping_dict)
+    write_mapping_dict(cfg.get("work_dir"), dataset, mapping_dict)
 
     provenance_record = get_provenance_record(
         "Lamb Weathertypes", ancestors, ["Lamb Weathertypes"], False, False
     )
 
     log_provenance(
-        f"{work_dir}/wt_selected_pairs_{dataset}", cfg, provenance_record
+        f"{cfg.get('work_dir')}/wt_selected_pairs_{dataset}",
+        cfg,
+        provenance_record,
     )
 
     return map_lwt_to_slwt(lwt, mapping_dict)
@@ -482,36 +480,37 @@ def calc_lwt_slwt_model(
             If dict, this mapping dict will be used.
             (see recipe option predefined_slwt)
     """
-    work_dir = cfg.get("work_dir")
-    dataset = data_info.get("dataset")
-    preproc_path = data_info.get("preproc_path")
-    output_file_path = data_info.get("output_file_path")
-    ensemble = data_info.get("ensemble", "")
-    timerange = data_info.get("timerange")
     driver = data_info.get("driver", "")
     if driver != "":
         driver = f"_{driver}"
 
-    if not os.path.exists(f"{work_dir}/{output_file_path}"):
-        os.makedirs(f"{work_dir}/{output_file_path}")
+    if not os.path.exists(
+        f"{cfg.get('work_dir')}/{data_info.get('output_file_path')}"
+    ):
+        os.makedirs(
+            f"{cfg.get('work_dir')}/{data_info.get('output_file_path')}"
+        )
 
-    lwt = wt_algorithm(cube, dataset)
+    lwt = wt_algorithm(cube, data_info.get("dataset"))
 
-    tcoord = cube.coord("time")
-    time_points = tcoord.units.num2date(tcoord.points)
+    time_points = cube.coord("time").units.num2date(cube.coord("time").points)
 
     logger.info(
-        "Calculating simplified Lamb Weathertypes for %s %s", dataset, ensemble
+        "Calculating simplified Lamb Weathertypes for %s %s",
+        data_info.get("dataset"),
+        data_info.get("ensemble", ""),
     )
 
     if not predefined_slwt:
         with open(
-            f"{work_dir}/wt_mapping_dict_ERA5.json", encoding="utf-8"
+            f"{cfg.get('work_dir')}/wt_mapping_dict_ERA5.json",
+            encoding="utf-8",
         ) as file:
             mapping_dict_era5_f = json.load(file)
 
         with open(
-            f"{work_dir}/wt_mapping_dict_E-OBS.json", encoding="utf-8"
+            f"{cfg.get('work_dir')}/wt_mapping_dict_E-OBS.json",
+            encoding="utf-8",
         ) as file:
             mapping_dict_eobs_f = json.load(file)
         mapping_dict_era5 = reverse_convert_dict(mapping_dict_era5_f)
@@ -521,8 +520,8 @@ def calc_lwt_slwt_model(
         slwt_eobs = map_lwt_to_slwt(lwt, mapping_dict_eobs)
     else:
         predefined_slwt = check_mapping_dict_format(predefined_slwt)
-        write_mapping_dict(work_dir, "ERA5", predefined_slwt)
-        write_mapping_dict(work_dir, "E-OBS", predefined_slwt)
+        write_mapping_dict(cfg.get("work_dir"), "ERA5", predefined_slwt)
+        write_mapping_dict(cfg.get("work_dir"), "E-OBS", predefined_slwt)
         slwt_era5 = map_lwt_to_slwt(lwt, predefined_slwt)
         slwt_eobs = map_lwt_to_slwt(lwt, predefined_slwt)
 
@@ -531,14 +530,14 @@ def calc_lwt_slwt_model(
     wt_cube.append(iris.cube.Cube(slwt_era5, long_name="slwt_era5"))
     wt_cube.append(iris.cube.Cube(slwt_eobs, long_name="slwt_eobs"))
 
-    wt_cube[0].add_dim_coord(tcoord, 0)
-    wt_cube[1].add_dim_coord(tcoord, 0)
-    wt_cube[2].add_dim_coord(tcoord, 0)
+    wt_cube[0].add_dim_coord(cube.coord("time"), 0)
+    wt_cube[1].add_dim_coord(cube.coord("time"), 0)
+    wt_cube[2].add_dim_coord(cube.coord("time"), 0)
 
     iris.save(
         wt_cube,
-        f"{work_dir}/{output_file_path}/{dataset}{driver}_"
-        f"{ensemble}_{timerange}.nc",
+        f"{cfg.get('work_dir')}/{data_info.get('output_file_path')}/{data_info.get('dataset')}{driver}_"
+        f"{data_info.get('ensemble', '')}_{data_info.get('timerange')}.nc",
     )
 
     # write to csv file
@@ -550,22 +549,22 @@ def calc_lwt_slwt_model(
     }
     df = pd.DataFrame(data=d)
     df.to_csv(
-        f"{work_dir}/{output_file_path}/{dataset}{driver}_{ensemble}_"
-        f"{timerange}.csv",
+        f"{cfg.get('work_dir')}/{data_info.get('output_file_path')}/{data_info.get('dataset')}{driver}_{data_info.get('ensemble', '')}_"
+        f"{data_info.get('timerange')}.csv",
         index=False,
     )
 
     ancestors = [
-        preproc_path,
-        f"{work_dir}/wt_mapping_dict_ERA5.json",
-        f"{work_dir}/wt_mapping_dict_E-OBS.json",
+        data_info.get("preproc_path"),
+        f"{cfg.get('work_dir')}/wt_mapping_dict_ERA5.json",
+        f"{cfg.get('work_dir')}/wt_mapping_dict_E-OBS.json",
     ]
     provenance_record = get_provenance_record(
         "Lamb Weathertypes", ancestors, ["Lamb Weathertypes"], False, False
     )
 
     log_provenance(
-        f"{work_dir}/{output_file_path}/{dataset}_{ensemble}",
+        f"{cfg.get('work_dir')}/{data_info.get('output_file_path')}/{data_info.get('dataset')}_{data_info.get('ensemble', '')}",
         cfg,
         provenance_record,
     )
@@ -591,8 +590,6 @@ def rmse(subarray1: np.array, subarray2: np.array) -> np.array:
 def process_prcp_mean(
     cfg: dict,
     data: np.array,
-    correlation_threshold: float,
-    rmse_threshold: float,
     dataset: str,
     timerange: str,
 ) -> list:
@@ -633,10 +630,9 @@ def process_prcp_mean(
         for j in range(i + 1, n):
             rmse_matrix[i][j] = rmse(data[i], data[j])
             rmse_matrix[j][i] = rmse_matrix[i][j]
-            if (
-                pattern_correlation_matrix[i][j] >= correlation_threshold
-                and rmse_matrix[i][j] <= rmse_threshold
-            ):
+            if pattern_correlation_matrix[i][j] >= cfg.get(
+                "correlation_threshold"
+            ) and rmse_matrix[i][j] <= cfg.get("rmse_threshold"):
                 selected_pairs.append(
                     (
                         (i + 1, j + 1),
@@ -675,19 +671,18 @@ def calc_wt_means(
         data_info : dict
             Dictionary with info to dataset
     """
-    dataset = data_info.get("dataset")
     var_name = data_info.get("var")
     wt_string = data_info.get("wt_string")
-    preproc_path = data_info.get("preproc_path")
-    ensemble = data_info.get("ensemble")
-    timerange = data_info.get("timerange")
     driver = data_info.get("driver", "")
     if driver != "":
         driver = f"_{driver}"
 
-    logger.info("Calculating %s %s means for %s", dataset, var_name, wt_string)
-
-    work_dir = cfg.get("work_dir")
+    logger.info(
+        "Calculating %s %s means for %s",
+        data_info.get("dataset"),
+        var_name,
+        wt_string,
+    )
 
     num_slwt = 0
     target_indices = []
@@ -722,7 +717,7 @@ def calc_wt_means(
                     for dataset %s!",
                     wt_string,
                     wt,
-                    dataset,
+                    data_info.get("dataset"),
                 )
                 continue
             dates = [
@@ -741,7 +736,7 @@ def calc_wt_means(
                     "calc_wt_means - CAUTION: Skipped lwt %s \
                     for dataset %s!",
                     wt,
-                    dataset,
+                    data_info.get("dataset"),
                 )
                 continue
             dates = [
@@ -755,7 +750,10 @@ def calc_wt_means(
     else:
         logger.info("WT_STRING NOT SUPPORTED.")
 
-    ancestors = [f"{preproc_path}", f"{work_dir}/ERA5.nc"]
+    ancestors = [
+        f"{data_info.get('preproc_path')}",
+        f"{cfg.get('work_dir')}/ERA5.nc",
+    ]
     provenance_record = get_provenance_record(
         f"{var_name} means for \
                                               {wt_string}",
@@ -769,8 +767,8 @@ def calc_wt_means(
 
     log_provenance(
         f"{local_path}/{wt_string}_{wt}{driver}_"
-        f"{dataset}_{ensemble}"
-        f"_{var_name}_mean_{timerange}",
+        f"{data_info.get('dataset')}_{data_info.get('ensemble', '')}"
+        f"_{var_name}_mean_{data_info.get('timerange')}",
         cfg,
         provenance_record,
     )
@@ -794,16 +792,14 @@ def calc_wt_anomalies(
         data_info : dict
             Dictionary with info to dataset
     """
-    work_dir = cfg.get("work_dir")
-    dataset = data_info.get("dataset")
     var_name = data_info.get("var_name")
     wt_string = data_info.get("wt_string")
-    preproc_path = data_info.get("preproc_path")
-    ensemble = data_info.get("ensemble")
-    timerange = data_info.get("timerange")
 
     logger.info(
-        "Calculating %s %s anomalies for %s", dataset, var_name, wt_string
+        "Calculating %s %s anomalies for %s",
+        data_info.get("dataset"),
+        var_name,
+        wt_string,
     )
 
     target_indices = []
@@ -846,7 +842,7 @@ def calc_wt_anomalies(
                     "calc_wt_anomalies - CAUTION: Skipped wt %s \
                     for dataset %s!",
                     wt,
-                    dataset,
+                    data_info.get("dataset"),
                 )
                 continue
             dates = [
@@ -871,7 +867,7 @@ def calc_wt_anomalies(
                     "calc_wt_anomalies - CAUTION: Skipped wt %s \
                     for dataset %s!",
                     wt,
-                    dataset,
+                    data_info.get("dataset"),
                 )
                 continue
             dates = [
@@ -891,7 +887,10 @@ def calc_wt_anomalies(
     else:
         logger.info("WT_STRING NOT SUPPORTED.")
 
-    ancestors = [f"{preproc_path}", f"{work_dir}/ERA5.nc"]
+    ancestors = [
+        f"{data_info.get('preproc_path')}",
+        f"{cfg.get('work_dir')}/ERA5.nc",
+    ]
     provenance_record = get_provenance_record(
         f"{var_name} anomaly for \
                                               {wt_string}",
@@ -904,8 +903,8 @@ def calc_wt_anomalies(
     local_path = f"{cfg.get('plot_dir')}/anomaly"
 
     log_provenance(
-        f"{local_path}/{wt_string}_{wt}_{dataset}_{ensemble}"
-        f"_{var_name}_anomaly__{timerange}",
+        f"{local_path}/{wt_string}_{wt}_{data_info.get('dataset')}_{data_info.get('ensemble', '')}"
+        f"_{var_name}_anomaly__{data_info.get('timerange')}",
         cfg,
         provenance_record,
     )
@@ -929,17 +928,12 @@ def calc_wt_std(
         data_info : dict
             Dictionary with info to dataset
     """
-    work_dir = cfg.get("work_dir")
-    dataset = data_info.get("dataset")
     var_name = data_info.get("var_name")
     wt_string = data_info.get("wt_string")
-    preproc_path = data_info.get("preproc_path")
-    ensemble = data_info.get("ensemble")
-    timerange = data_info.get("timerange")
 
     logger.info(
         "Calculating %s %s standard deviation for %s",
-        dataset,
+        data_info.get("dataset"),
         var_name,
         wt_string,
     )
@@ -984,7 +978,7 @@ def calc_wt_std(
                     "calc_slwt_obs - CAUTION: Skipped wt %s \
                     for dataset %s!",
                     wt,
-                    dataset,
+                    data_info.get("dataset"),
                 )
                 continue
             dates = [
@@ -1005,7 +999,7 @@ def calc_wt_std(
                     "calc_wt_std - CAUTION: Skipped wt %s \
                     for dataset %s!",
                     wt,
-                    dataset,
+                    data_info.get("dataset"),
                 )
                 continue
             dates = [
@@ -1021,7 +1015,10 @@ def calc_wt_std(
     else:
         logger.info("WT_STRING NOT SUPPORTED.")
 
-    ancestors = [f"{preproc_path}", f"{work_dir}/ERA5.nc"]
+    ancestors = [
+        f"{data_info.get('preproc_path')}",
+        f"{cfg.get('work_dir')}/ERA5.nc",
+    ]
     provenance_record = get_provenance_record(
         f"{var_name} standard \
                                               deviation for \
@@ -1035,16 +1032,14 @@ def calc_wt_std(
     local_path = f"{cfg.get('plot_dir')}/stddev"
 
     log_provenance(
-        f"{local_path}/{wt_string}_{wt}_{dataset}_{ensemble}"
-        f"_{var_name}_stddev_{timerange}",
+        f"{local_path}/{wt_string}_{wt}_{data_info.get('dataset')}_{data_info.get('ensemble', '')}"
+        f"_{var_name}_stddev_{data_info.get('timerange')}",
         cfg,
         provenance_record,
     )
 
 
-def calc_lwt_model(
-    cfg: dict, cube: iris.cube.Cube, dataset: str, data_info: dict
-):
+def calc_lwt_model(cfg: dict, cube: iris.cube.Cube, data_info: dict):
     """Calculate lwt for model data.
 
     Args:
@@ -1058,22 +1053,20 @@ def calc_lwt_model(
         data_info : dict
             Dictionary with info to dataset
     """
-    work_dir = cfg.get("work_dir")
-    dataset = data_info.get("dataset")
-    output_file_path = data_info.get("output_file_path")
-    ensemble = data_info.get("ensemble", "")
-    timerange = data_info.get("timerange")
     driver = data_info.get("driver", "")
     if driver != "":
         driver = f"_{driver}"
 
-    if not os.path.exists(f"{work_dir}/{output_file_path}"):
-        os.makedirs(f"{work_dir}/{output_file_path}")
+    if not os.path.exists(
+        f"{cfg.get('work_dir')}/{data_info.get('output_file_path')}"
+    ):
+        os.makedirs(
+            f"{cfg.get('work_dir')}/{data_info.get('output_file_path')}"
+        )
 
-    wt = wt_algorithm(cube, dataset)
+    wt = wt_algorithm(cube, data_info.get("dataset"))
 
-    tcoord = cube.coord("time")
-    time_points = tcoord.units.num2date(tcoord.points)
+    time_points = cube.coord("time").units.num2date(cube.coord("time").points)
 
     wt_cube = iris.cube.CubeList()
     wt_cube.append(iris.cube.Cube(wt, long_name="lwt"))
@@ -1081,37 +1074,37 @@ def calc_lwt_model(
     logger.info(
         "Writing Lamb Weathertype for %s \
                 to file %s.nc",
-        dataset,
-        dataset,
+        data_info.get("dataset"),
+        data_info.get("dataset"),
     )
 
-    wt_cube[0].add_dim_coord(tcoord, 0)
+    wt_cube[0].add_dim_coord(cube.coord("time"), 0)
 
     iris.save(
         wt_cube,
-        f"{work_dir}/{output_file_path}/{dataset}{driver}"
-        f"_{ensemble}_{timerange}.nc",
+        f"{cfg.get('work_dir')}/{data_info.get('output_file_path')}/{data_info.get('dataset')}{driver}"
+        f"_{data_info.get('ensemble', '')}_{data_info.get('timerange')}.nc",
     )
 
     # write to csv file
     d = {"date": time_points[:], "lwt": np.int8(wt)}
     df = pd.DataFrame(data=d)
     df.to_csv(
-        f"{work_dir}/{output_file_path}/{dataset}{driver}_{ensemble}_"
-        f"{timerange}.csv",
+        f"{cfg.get('work_dir')}/{data_info.get('output_file_path')}/{data_info.get('dataset')}{driver}_{data_info.get('ensemble', '')}_"
+        f"{data_info.get('timerange')}.csv",
         index=False,
     )
 
     ancestors = [
-        f"{work_dir}/wt_mapping_dict_ERA5.json",
-        f"{work_dir}/wt_mapping_dict_E-OBS.json",
+        f"{cfg.get('work_dir')}/wt_mapping_dict_ERA5.json",
+        f"{cfg.get('work_dir')}/wt_mapping_dict_E-OBS.json",
     ]
     provenance_record = get_provenance_record(
         "Lamb Weathertypes", ancestors, ["Lamb Weathertypes"], False, False
     )
 
     log_provenance(
-        f"{work_dir}/{output_file_path}/{dataset}_{ensemble}",
+        f"{cfg.get('work_dir')}/{data_info.get('output_file_path')}/{data_info.get('dataset')}_{data_info.get('ensemble', '')}",
         cfg,
         provenance_record,
     )
diff --git a/esmvaltool/diag_scripts/weathertyping/weathertyping.py b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
index 841b8b9b06..b2c9c0ff13 100644
--- a/esmvaltool/diag_scripts/weathertyping/weathertyping.py
+++ b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
@@ -8,7 +8,7 @@
 import iris
 
 # import internal esmvaltool modules here
-from esmvaltool.diag_scripts.shared import run_diagnostic
+from esmvaltool.diag_scripts.shared import group_metadata, run_diagnostic
 from esmvaltool.diag_scripts.weathertyping.calc_utils import (
     calc_lwt_model,
     calc_lwt_slwt_model,
@@ -22,7 +22,6 @@
 from esmvaltool.diag_scripts.weathertyping.wt_utils import (
     combine_wt_to_file,
     get_ancestors_era5_eobs,
-    get_cfg_vars,
     get_looping_dict,
     get_model_output_filepath,
     get_preproc_lists_ensemble,
@@ -35,276 +34,346 @@
 )
 
 
-def run_automatic_slwt(cfg: dict):
-    """Run the automated calculation for simplified weathertypes \
-    and write to file, and plot the means and seasonal occurrence \
-    of the weathertypes.
+def process_models_automatic_slwt(
+    cfg: dict, dataset_vars: list, data_info: dict
+):
+    """Process model data for calculating Lamb and simplified weathertypes.
 
     Args:
     ----
         cfg : dict
             Nested dictionary of metadata
+        dataset_vars : list
+            List of variable dictionaries for a specific dataset
+        data_info : dict
+            Dictionary holding dataset information.
     """
-    preproc_variables_dict, _, _, work_dir, plotting, _, predefined_slwt = (
-        get_cfg_vars(cfg)
-    )
-    for dataset_name, dataset_vars in preproc_variables_dict.items():
-        data_info = {
-            "timerange": dataset_vars[0].get("timerange").replace("/", "-"),
-            "dataset": dataset_name,
-        }
-        if dataset_name == "ERA5":
-            wt_preproc, wt_preproc_prcp, wt_preproc_prcp_eobs = (
-                load_wt_preprocessors(dataset_name, preproc_variables_dict)
-            )
+    for ensemble_var in dataset_vars:
+        if ensemble_var.get("preprocessor") == "weathertype_preproc":
+            data_info["ensemble"] = ensemble_var.get("ensemble", "")
+            data_info["driver"] = ensemble_var.get("driver", "")
+
+            if data_info["driver"] != "":
+                data_info["driver"] = "_" + data_info["driver"]
 
-            # calculate lwt
-            lwt = wt_algorithm(wt_preproc, dataset_name)
+            wt_preproc = iris.load_cube(ensemble_var.get("filename"))
 
-            era5_ancestors, eobs_ancestors = get_ancestors_era5_eobs(
-                dataset_name, preproc_variables_dict
+            output_file_path, preproc_path = get_model_output_filepath(
+                data_info["dataset"], ensemble_var
             )
 
-            # calculate simplified lwt based on precipitation
-            # patterns or use predefined_slwt
-            if not predefined_slwt:
-                slwt_era5 = calc_slwt_obs(
-                    cfg,
-                    lwt,
-                    wt_preproc_prcp,
-                    dataset_name,
-                    era5_ancestors,
-                    data_info["timerange"],
-                )
-                slwt_eobs = calc_slwt_obs(
-                    cfg,
-                    lwt,
-                    wt_preproc_prcp_eobs,
-                    "E-OBS",
-                    eobs_ancestors,
-                    data_info["timerange"],
-                )
-            else:
-                slwt_era5, slwt_eobs = run_predefined_slwt(
-                    work_dir, dataset_name, lwt, predefined_slwt
-                )
+            data_info["output_file_path"] = output_file_path
+            data_info["preproc_path"] = preproc_path
 
-            # write to file
-            wt_list = [lwt, slwt_era5, slwt_eobs]
-            combine_wt_to_file(cfg, wt_list, wt_preproc, dataset_name)
+            # calculate weathertypes
+            calc_lwt_slwt_model(
+                cfg, wt_preproc, data_info, cfg.get("predefined_slwt")
+            )
 
-            # load weathertype files as cubes
-            wt_cubes = load_wt_files(f"{work_dir}/{dataset_name}.nc")
+            # load wt files
+            wt_cubes = load_wt_files(
+                f"{cfg.get('work_dir')}/{output_file_path}"
+                f"/{data_info['dataset']}"
+                f"{data_info['driver']}_"
+                f"{data_info['ensemble']}_"
+                f"{data_info['timerange']}.nc"
+            )
 
-            var_dict = get_looping_dict(
-                dataset_vars
-            )  # dataset_vars is list of variables for dataset dataset_name
-            if plotting:
-                # plot means
+            var_dict = {
+                f"{ensemble_var.get('short_name')}": get_preproc_lists_ensemble(
+                    ensemble_var
+                )
+            }
+
+            # plot means
+            if cfg.get("plotting", False):
                 for var_name, var_data in var_dict.items():
                     data_info["var"] = var_name
                     data_info["preproc_path"] = var_data[1]
 
                     plot_means(cfg, var_data[0], wt_cubes, data_info)
                 plot_seasonal_occurrence(cfg, wt_cubes, data_info)
-        else:
-            if dataset_name == "E-OBS":
-                continue
-            for ensemble_var in dataset_vars:
-                if ensemble_var.get("preprocessor") == "weathertype_preproc":
-                    data_info["ensemble"] = ensemble_var.get("ensemble", "")
-                    data_info["driver"] = ensemble_var.get("driver", "")
 
-                    if data_info["driver"] != "":
-                        data_info["driver"] = "_" + data_info["driver"]
 
-                    wt_preproc = iris.load_cube(ensemble_var.get("filename"))
+def process_era5_automatic_slwt(
+    data_info: dict,
+    preproc_variables_dict: dict,
+    cfg: dict,
+    dataset_vars: list,
+):
+    """Process ERA5 data for calculating Lamb and simplified weathertypes.
 
-                    output_file_path, preproc_path = get_model_output_filepath(
-                        dataset_name, ensemble_var
-                    )
-
-                    data_info["output_file_path"] = output_file_path
-                    data_info["preproc_path"] = preproc_path
+    Args:
+    -----
+        data_info : dict
+            Dictionary holding dataset information.
+        preproc_variables_dict : dict
+            Dictionary holding preprocessed variables for all datasets.
+        cfg : dict
+            Nested dictionary of metadata
+        dataset_vars : list
+            List of variable dictionaries for a specific dataset
+    """
+    wt_preproc, wt_preproc_prcp, wt_preproc_prcp_eobs = load_wt_preprocessors(
+        data_info["dataset"], preproc_variables_dict
+    )
 
-                    # calculate weathertypes
-                    calc_lwt_slwt_model(
-                        cfg, wt_preproc, data_info, predefined_slwt
-                    )
+    # calculate lwt
+    lwt = wt_algorithm(wt_preproc, data_info["dataset"])
 
-                    # load wt files
-                    wt_cubes = load_wt_files(
-                        f"{work_dir}/{output_file_path}"
-                        f"/{dataset_name}"
-                        f"{data_info['driver']}_"
-                        f"{data_info['ensemble']}_"
-                        f"{data_info['timerange']}.nc"
-                    )
+    era5_ancestors, eobs_ancestors = get_ancestors_era5_eobs(
+        data_info["dataset"], preproc_variables_dict
+    )
 
-                    var_dict = {
-                        f"{ensemble_var.get('short_name')}": get_preproc_lists_ensemble(
-                            ensemble_var
-                        )
-                    }
+    # calculate simplified lwt based on precipitation
+    # patterns or use predefined_slwt
+    if not cfg.get("predefined_slwt"):
+        slwt_era5 = calc_slwt_obs(
+            cfg,
+            lwt,
+            wt_preproc_prcp,
+            data_info["dataset"],
+            era5_ancestors,
+            data_info["timerange"],
+        )
+        slwt_eobs = calc_slwt_obs(
+            cfg,
+            lwt,
+            wt_preproc_prcp_eobs,
+            "E-OBS",
+            eobs_ancestors,
+            data_info["timerange"],
+        )
+    else:
+        slwt_era5, slwt_eobs = run_predefined_slwt(
+            cfg.get("work_dir"),
+            data_info["dataset"],
+            lwt,
+            cfg.get("predefined_slwt"),
+        )
+
+    # write to file
+    wt_list = [lwt, slwt_era5, slwt_eobs]
+    combine_wt_to_file(cfg, wt_list, wt_preproc, data_info["dataset"])
+
+    # load weathertype files as cubes
+    wt_cubes = load_wt_files(
+        f"{cfg.get('work_dir')}/{data_info['dataset']}.nc"
+    )
 
-                    # plot means
-                    if plotting:
-                        for var_name, var_data in var_dict.items():
-                            data_info["var"] = var_name
-                            data_info["preproc_path"] = var_data[1]
+    var_dict = get_looping_dict(
+        dataset_vars
+    )  # dataset_vars is list of variables for dataset dataset_name
+    if cfg.get("plotting", False):
+        # plot means
+        for var_name, var_data in var_dict.items():
+            data_info["var"] = var_name
+            data_info["preproc_path"] = var_data[1]
 
-                            plot_means(cfg, var_data[0], wt_cubes, data_info)
-                        plot_seasonal_occurrence(cfg, wt_cubes, data_info)
+            plot_means(cfg, var_data[0], wt_cubes, data_info)
+        plot_seasonal_occurrence(cfg, wt_cubes, data_info)
 
 
-def run_lwt(cfg: dict):
-    """Run calculation of weathertypes and write to file, and plot the means \
-    and seasonal occurrence of the weathertypes.
+def run_automatic_slwt(cfg: dict):
+    """Run the automated calculation for simplified weathertypes \
+    and write to file, and plot the means and seasonal occurrence \
+    of the weathertypes.
 
     Args:
     ----
         cfg : dict
             Nested dictionary of metadata
-
     """
-    preproc_variables_dict, _, _, work_dir, plotting, _, _ = get_cfg_vars(cfg)
+    preproc_variables_dict = group_metadata(
+        cfg.get("input_data").values(), "dataset"
+    )
     for dataset_name, dataset_vars in preproc_variables_dict.items():
         data_info = {
             "timerange": dataset_vars[0].get("timerange").replace("/", "-"),
             "dataset": dataset_name,
         }
-
         if dataset_name == "ERA5":
-            wt_preproc, _, _ = load_wt_preprocessors(
-                dataset_name, preproc_variables_dict
+            process_era5_automatic_slwt(
+                data_info, preproc_variables_dict, cfg, dataset_vars
             )
+        else:
+            if data_info["dataset"] == "E-OBS":
+                continue
+            process_models_automatic_slwt(cfg, dataset_vars, data_info)
 
-            # calculate lwt
-            lwt = wt_algorithm(wt_preproc, dataset_name)
 
-            era5_ancestors, eobs_ancestors = get_ancestors_era5_eobs(
-                dataset_name, preproc_variables_dict
-            )
+def process_era5_lwt(preproc_variables_dict, cfg, dataset_vars, data_info):
+    """Process ERA5 data for calculating Lamb weathertypes.
+
+    Args:
+    ----
+        preproc_variables_dict : dict
+            Dictionary holding preprocessed variables for all datasets.
+        cfg : dict
+            Nested dictionary of metadata
+        dataset_vars : list
+            List of variable dictionaries for a specific dataset
+        data_info : dict
+            Dictionary holding dataset information.
+    """
+    wt_preproc, _, _ = load_wt_preprocessors(
+        data_info["dataset"], preproc_variables_dict
+    )
+
+    # calculate lwt
+    lwt = wt_algorithm(wt_preproc, data_info["dataset"])
+
+    era5_ancestors, eobs_ancestors = get_ancestors_era5_eobs(
+        data_info["dataset"], preproc_variables_dict
+    )
+
+    ancestors = [era5_ancestors, eobs_ancestors]
+
+    provenance_record = get_provenance_record(
+        "Lamb Weathertypes",
+        ancestors,
+        ["Lamb Weathertypes"],
+        False,
+        False,
+    )
+
+    log_provenance(f"{cfg.get('work_dir')}/lwt_era5", cfg, provenance_record)
+
+    # write only lwt to file
+    write_lwt_to_file(cfg, lwt, wt_preproc, data_info["dataset"])
+
+    # load wt files
+    wt_cubes = load_wt_files(
+        f"{cfg.get('work_dir')}/{data_info['dataset']}.nc", only_lwt=True
+    )
+
+    var_dict = get_looping_dict(
+        dataset_vars
+    )  # dataset_vars is list of variables for dataset dataset_name
+
+    if cfg.get("plotting", False):
+        # plot means
+        for var_name, var_data in var_dict.items():
+            data_info["var"] = var_name
+            data_info["preproc_path"] = var_data[1]
 
-            ancestors = [era5_ancestors, eobs_ancestors]
+            plot_means(cfg, var_data[0], wt_cubes, data_info, only_lwt=True)
+        plot_seasonal_occurrence(cfg, wt_cubes, data_info)
 
-            provenance_record = get_provenance_record(
-                "Lamb Weathertypes",
-                ancestors,
-                ["Lamb Weathertypes"],
-                False,
-                False,
+
+def process_models_lwt(cfg: dict, dataset_vars: list, data_info: dict):
+    """Process model data for calculating Lamb weathertypes.
+
+    Args:
+    ----
+        cfg : dict
+            Nested dictionary of metadata
+        dataset_vars : list
+            List of variable dictionaries for a specific dataset
+        data_info : dict
+            Dictionary holding dataset information.
+    """
+    for ensemble_var in dataset_vars:
+        if ensemble_var.get("preprocessor") == "weathertype_preproc":
+            data_info["ensemble"] = ensemble_var.get("ensemble", "")
+            data_info["driver"] = ensemble_var.get("driver", "")
+
+            if data_info["driver"] != "":
+                data_info["driver"] = "_" + {data_info["driver"]}
+
+            wt_preproc = iris.load_cube(dataset_vars[0].get("filename"))
+
+            output_file_path, preproc_path = get_model_output_filepath(
+                data_info["dataset"], dataset_vars
             )
 
-            log_provenance(f"{work_dir}/lwt_era5", cfg, provenance_record)
+            data_info["output_file_path"] = output_file_path
+            data_info["preproc_path"] = preproc_path
 
-            # write only lwt to file
-            write_lwt_to_file(cfg, lwt, wt_preproc, dataset_name)
+            # calculate weathertypes
+            calc_lwt_model(cfg, wt_preproc, data_info)
 
             # load wt files
             wt_cubes = load_wt_files(
-                f"{work_dir}/{dataset_name}.nc", only_lwt=True
+                f"{cfg.get('work_dir')}/{output_file_path}"
+                f"/{data_info['dataset']}"
+                f"{data_info['driver']}"
+                f"_{data_info['ensemble']}_"
+                f"{data_info['timerange']}.nc",
+                only_lwt=True,
             )
 
             var_dict = get_looping_dict(
                 dataset_vars
-            )  # dataset_vars is list of variables for dataset dataset_name
+            )  # dataset_vars is list of variables for dataset_name
 
-            if plotting:
+            if cfg.get("plotting", False):
                 # plot means
                 for var_name, var_data in var_dict.items():
                     data_info["var"] = var_name
                     data_info["preproc_path"] = var_data[1]
 
                     plot_means(
-                        cfg, var_data[0], wt_cubes, data_info, only_lwt=True
+                        cfg,
+                        var_data[0],
+                        wt_cubes,
+                        data_info,
+                        only_lwt=True,
                     )
                 plot_seasonal_occurrence(cfg, wt_cubes, data_info)
-        else:
-            if dataset_name == "E-OBS":
-                continue
-            for ensemble_var in dataset_vars:
-                if ensemble_var.get("preprocessor") == "weathertype_preproc":
-                    data_info["ensemble"] = ensemble_var.get("ensemble", "")
-                    data_info["driver"] = ensemble_var.get("driver", "")
 
-                    if data_info["driver"] != "":
-                        data_info["driver"] = "_" + {data_info["driver"]}
 
-                    wt_preproc = iris.load_cube(
-                        dataset_vars[0].get("filename")
-                    )
-
-                    output_file_path, preproc_path = get_model_output_filepath(
-                        dataset_name, dataset_vars
-                    )
-
-                    # calculate weathertypes
-                    data_info["output_file_path"] = output_file_path
-                    data_info["preproc_path"] = preproc_path
-
-                    calc_lwt_model(cfg, wt_preproc, dataset_name, data_info)
-
-                    # load wt files
-                    wt_cubes = load_wt_files(
-                        f"{work_dir}/{output_file_path}"
-                        f"/{dataset_name}"
-                        f"{data_info['driver']}"
-                        f"_{data_info['ensemble']}_"
-                        f"{data_info['timerange']}.nc",
-                        only_lwt=True,
-                    )
+def run_lwt(cfg: dict):
+    """Run calculation of weathertypes.
+    Write to file, and plot the means of psl, tas, and pr \
+    for each weathertype. \
+    Plot seasonal occurrence of the weathertypes.
 
-                    var_dict = get_looping_dict(
-                        dataset_vars
-                    )  # dataset_vars is list of variables for dataset_name
+    Args:
+    ----
+        cfg : dict
+            Nested dictionary of metadata
 
-                    if plotting:
-                        # plot means
-                        for var_name, var_data in var_dict.items():
-                            data_info["var"] = var_name
-                            data_info["preproc_path"] = var_data[1]
+    """
+    preproc_variables_dict = group_metadata(
+        cfg.get("input_data").values(), "dataset"
+    )
+    for dataset_name, dataset_vars in preproc_variables_dict.items():
+        data_info = {
+            "timerange": dataset_vars[0].get("timerange").replace("/", "-"),
+            "dataset": dataset_name,
+        }
 
-                            plot_means(
-                                cfg,
-                                var_data[0],
-                                wt_cubes,
-                                data_info,
-                                only_lwt=True,
-                            )
-                        plot_seasonal_occurrence(cfg, wt_cubes, data_info)
+        if dataset_name == "ERA5":
+            process_era5_lwt(
+                preproc_variables_dict, cfg, dataset_vars, data_info
+            )
+        else:
+            if data_info["dataset"] == "E-OBS":
+                continue
+            process_models_lwt(cfg, dataset_vars, data_info)
 
 
 def run_my_diagnostic(cfg: dict):
     """Run the weathertyping diagnostic.
 
-    Arguments:
+    Args:
+    -----
         cfg : dict
             nested dictionary of metadata
-
-    Returns
-        string
-            runs the user diagnostic
-
     """
-    # assemble the data dictionary keyed by dataset name
-    # this makes use of the handy group_metadata function that
-    # orders the data by 'dataset'; the resulting dictionary is
-    # keyed on datasets e.g. dict = {'MPI-ESM-LR': [var1, var2...]}
-    # where var1, var2 are dicts holding all needed information per variable
     automatic_slwt = cfg.get("automatic_slwt")
 
+    # check if user wants to calculate simplified weathertypes automatically
     if automatic_slwt:
         run_automatic_slwt(cfg)
     # if automatic_slwt is false, and predefined_slwt is false,
     # just write selected pairs to file
-    elif not automatic_slwt:
+    else:
         run_lwt(cfg)
 
 
 if __name__ == "__main__":
-    # always use run_diagnostic() to get the config (the preprocessor
-    # nested dictionary holding all the needed information)
     with run_diagnostic() as config:
-        # list here the functions that need to run
+        # main function for running the diagnostic
         run_my_diagnostic(config)

From 65402ba0ad3f2f4c333f3a74d8a26978a1017c1d Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Tue, 4 Nov 2025 09:22:38 +0100
Subject: [PATCH 07/57] less locals, use function for driver string

---
 .../diag_scripts/weathertyping/calc_utils.py  | 13 ++--
 .../diag_scripts/weathertyping/plot_utils.py  | 59 +++++++++----------
 .../diag_scripts/weathertyping/wt_utils.py    | 31 ++++------
 3 files changed, 43 insertions(+), 60 deletions(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/calc_utils.py b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
index 85c5e3c8e9..fe5f195ae5 100644
--- a/esmvaltool/diag_scripts/weathertyping/calc_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
@@ -18,6 +18,7 @@
 from plot_utils import plot_corr_rmse_heatmaps, plot_maps
 from wt_utils import (
     check_mapping_dict_format,
+    get_driver,
     get_mapping_dict,
     get_provenance_record,
     log_provenance,
@@ -480,9 +481,7 @@ def calc_lwt_slwt_model(
             If dict, this mapping dict will be used.
             (see recipe option predefined_slwt)
     """
-    driver = data_info.get("driver", "")
-    if driver != "":
-        driver = f"_{driver}"
+    driver = get_driver(data_info)
 
     if not os.path.exists(
         f"{cfg.get('work_dir')}/{data_info.get('output_file_path')}"
@@ -673,9 +672,7 @@ def calc_wt_means(
     """
     var_name = data_info.get("var")
     wt_string = data_info.get("wt_string")
-    driver = data_info.get("driver", "")
-    if driver != "":
-        driver = f"_{driver}"
+    driver = get_driver(data_info)
 
     logger.info(
         "Calculating %s %s means for %s",
@@ -1053,9 +1050,7 @@ def calc_lwt_model(cfg: dict, cube: iris.cube.Cube, data_info: dict):
         data_info : dict
             Dictionary with info to dataset
     """
-    driver = data_info.get("driver", "")
-    if driver != "":
-        driver = f"_{driver}"
+    driver = get_driver(data_info)
 
     if not os.path.exists(
         f"{cfg.get('work_dir')}/{data_info.get('output_file_path')}"
diff --git a/esmvaltool/diag_scripts/weathertyping/plot_utils.py b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
index 7d30f9332b..7f4c11bfeb 100644
--- a/esmvaltool/diag_scripts/weathertyping/plot_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
@@ -23,6 +23,7 @@
 # local imports
 from cartopy.mpl.gridliner import LATITUDE_FORMATTER, LONGITUDE_FORMATTER
 from matplotlib.colors import ListedColormap
+from wt_utils import get_driver
 
 iris.FUTURE.datum_support = True
 
@@ -68,10 +69,7 @@ def plot_seasonal_occurrence(
         data_info : dict
             Dictionary with relevant info to dataset
     """
-    dataset_name = data_info.get("dataset")
-    timerange = data_info.get("timerange")
-    ensemble = data_info.get("ensemble", "")
-    driver = data_info.get("driver", "")
+    driver = get_driver(data_info)
 
     output_path = f"{cfg['plot_dir']}/seasonal_occurrence"
 
@@ -130,7 +128,7 @@ def plot_seasonal_occurrence(
 
         ax_.set_title(
             f"Seasonal occurence of {wt_string}, \
-            {dataset_name}, {timerange}"
+            {data_info.get('dataset')}, {data_info.get('timerange')}"
         )
 
         ax_.stackplot(x, y, colors=colors)
@@ -154,12 +152,12 @@ def plot_seasonal_occurrence(
         ax_.set_ylabel("Cumulative Relative occurrence")
 
         plt.savefig(
-            f"{output_path}/{driver}{dataset_name}_{ensemble}_"
-            f"{wt_string}_rel_occurrence_{timerange}.png"
+            f"{output_path}/{driver}{data_info.get('dataset')}_{data_info.get('ensemble', '')}_"
+            f"{wt_string}_rel_occurrence_{data_info.get('timerange')}.png"
         )
         plt.savefig(
-            f"{output_path}/{driver}{dataset_name}_{ensemble}_"
-            f"{wt_string}_rel_occurrence_{timerange}.pdf"
+            f"{output_path}/{driver}{data_info.get('dataset')}_{data_info.get('ensemble', '')}_"
+            f"{wt_string}_rel_occurrence_{data_info.get('timerange')}.pdf"
         )
         plt.close()
 
@@ -182,17 +180,16 @@ def plot_maps(
         mode : str
             Statistics that is used
     """
-    dataset = data_info.get("dataset")
     var_name = data_info.get("var")
-    wt_string = data_info.get("wt_string")
-    ensemble = data_info.get("ensemble", "")
-    timerange = data_info.get("timerange")
-    driver = data_info.get("driver", "")
-    if driver != "":
-        driver = f"_{driver}"
+    driver = get_driver(data_info)
 
     logger.info(
-        "Plotting %s %s %s for %s %s", dataset, var_name, mode, wt_string, wt
+        "Plotting %s %s %s for %s %s",
+        data_info.get("dataset"),
+        data_info.get("var"),
+        mode,
+        data_info.get("wt_string"),
+        wt,
     )
 
     local_path = f"{cfg.get('plot_dir')}/{mode}"
@@ -202,11 +199,11 @@ def plot_maps(
 
     ax = plt.axes(projection=ccrs.PlateCarree())
 
-    if var_name == "psl":
+    if data_info.get("var") == "psl":
         psl_cmap = get_colormap("psl")
         plt.title(
-            f"{dataset} {ensemble}, {var_name} {mode}\n"
-            + f"{timerange}, wt: {wt}"
+            f"{data_info.get('dataset')} {data_info.get('ensemble', '')}, {data_info.get('var')} {mode}\n"
+            + f"{data_info.get('timerange')}, wt: {wt}"
         )
         unit = "[hPa]"
         im = iplt.contourf(cube / 100, cmap=psl_cmap)
@@ -215,17 +212,17 @@ def plot_maps(
         cb.set_label(label=f"{var_name} {mode} {unit}")
     elif var_name == "pr":
         prcp_cmap = get_colormap("prcp")
-        if dataset == "ERA5":
+        if data_info.get("dataset") == "ERA5":
             unit = "[m]"
             plt.title(
-                f"{dataset} {ensemble}, total {var_name} {mode}\n"
-                + f"{timerange}, wt: {wt}"
+                f"{data_info.get('dataset')} {data_info.get('ensemble', '')}, total {var_name} {mode}\n"
+                + f"{data_info.get('timerange')}, wt: {wt}"
             )
         else:
             unit = "[kg m-2 s-1]"
             plt.title(
-                f"{dataset} {ensemble}, {var_name} flux {mode}\n"
-                + f"{timerange}, wt: {wt}"
+                f"{data_info.get('dataset')} {data_info.get('ensemble', '')}, {var_name} flux {mode}\n"
+                + f"{data_info.get('timerange')}, wt: {wt}"
             )
         im = iplt.contourf(cube, cmap=prcp_cmap)
         cb = plt.colorbar(im)
@@ -235,8 +232,8 @@ def plot_maps(
         temp_cmap = get_colormap("temp")
         unit = "[K]"
         plt.title(
-            f"{dataset} {ensemble}, 1000 hPa {var_name} {mode}\n"
-            + f"{timerange}, wt: {wt}"
+            f"{data_info.get('dataset')} {data_info.get('ensemble', '')}, 1000 hPa {var_name} {mode}\n"
+            + f"{data_info.get('timerange')}, wt: {wt}"
         )
         im = iplt.contourf(cube, cmap=temp_cmap)
         cb = plt.colorbar(im)
@@ -269,12 +266,12 @@ def plot_maps(
     ax.add_feature(cfeature.BORDERS, linestyle=":")
 
     plt.savefig(
-        f"{local_path}/{wt_string}_{wt}{driver}_{dataset}_{ensemble}"
-        f"_{var_name}_{mode}_{timerange}.png"
+        f"{local_path}/{data_info.get('wt_string')}_{wt}{driver}_{data_info.get('dataset')}_{data_info.get('ensemble', '')}"
+        f"_{var_name}_{mode}_{data_info.get('timerange')}.png"
     )
     plt.savefig(
-        f"{local_path}/{wt_string}_{wt}{driver}_{dataset}_{ensemble}_"
-        f"{var_name}_{mode}_{timerange}.pdf"
+        f"{local_path}/{data_info.get('wt_string')}_{wt}{driver}_{data_info.get('dataset')}_{data_info.get('ensemble', '')}_"
+        f"{var_name}_{mode}_{data_info.get('timerange')}.pdf"
     )
     plt.close()
 
diff --git a/esmvaltool/diag_scripts/weathertyping/wt_utils.py b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
index 4f905dff88..ab57ab3115 100644
--- a/esmvaltool/diag_scripts/weathertyping/wt_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
@@ -15,7 +15,7 @@
 import pandas as pd
 
 # import internal esmvaltool modules here
-from esmvaltool.diag_scripts.shared import ProvenanceLogger, group_metadata
+from esmvaltool.diag_scripts.shared import ProvenanceLogger
 
 iris.FUTURE.datum_support = True
 
@@ -25,32 +25,23 @@
 warnings.filterwarnings("ignore", ".*Collapsing a non-contiguous coordinate*")
 
 
-def get_cfg_vars(cfg: dict):
-    """Get list of vars from configuration dict.
+def get_driver(data_info: dict) -> str:
+    """Get driving model string.
 
     Args:
     ----
-        cfg : dict
-            Configuration dict from recipe.
+        data_info : dict
+            Data information dictionary.
 
     Returns
     -------
-        tuple
-            cfg vars
+        str
+            Driver string with leading underscore or empty string.
     """
-    preproc_variables_dict = group_metadata(
-        cfg.get("input_data").values(), "dataset"
-    )
-
-    return (
-        preproc_variables_dict,
-        cfg.get("correlation_threshold"),
-        cfg.get("rmse_threshold"),
-        cfg.get("work_dir"),
-        cfg.get("plotting", False),
-        cfg.get("automatic_slwt", True),
-        cfg.get("predefined_slwt", False),
-    )
+    driver = data_info.get("driver", "")
+    if driver != "":
+        driver = f"_{driver}"
+    return driver
 
 
 def load_wt_preprocessors(dataset: str, preproc_variables_dict: dict):

From 57e5deaa51251ddb179b2d90c5e7d3b7084769bb Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Tue, 4 Nov 2025 10:00:19 +0100
Subject: [PATCH 08/57] refactor calc_wt_means, calc_wt_std, calc_wt_anomaly

---
 .../diag_scripts/weathertyping/calc_utils.py  | 265 +++++-------------
 1 file changed, 73 insertions(+), 192 deletions(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/calc_utils.py b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
index fe5f195ae5..019660b1fb 100644
--- a/esmvaltool/diag_scripts/weathertyping/calc_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
@@ -681,71 +681,40 @@ def calc_wt_means(
         wt_string,
     )
 
-    num_slwt = 0
-    target_indices = []
-    lwt, slwt_eobs, slwt_era5 = [], [], []
-
     if wt_string == "slwt_ERA5":
         slwt_era5_cube = wt_cubes[1]
         tcoord = slwt_era5_cube.coord("time")
-        slwt_era5 = slwt_era5_cube.data[:]
-        num_slwt = len(np.unique(slwt_era5))
+        wt_array = slwt_era5_cube.data[:]
     elif wt_string == "slwt_EOBS":
         slwt_eobs_cube = wt_cubes[2]
         tcoord = slwt_eobs_cube.coord("time")
-        slwt_eobs = slwt_eobs_cube.data[:]
-        num_slwt = len(np.unique(slwt_eobs))
+        wt_array = slwt_eobs_cube.data[:]
     elif wt_string == "lwt":
         lwt_cube = wt_cubes[0]
         tcoord = lwt_cube.coord("time")
-        lwt = lwt_cube.data[:]
-
-    if "slwt" in wt_string:
-        for wt in range(1, num_slwt + 1):
-            if wt_string == "slwt_ERA5":
-                target_indices = np.where(slwt_era5 == wt)
-            elif wt_string == "slwt_EOBS":
-                target_indices = np.where(slwt_eobs == wt)
-            else:
-                logger.info("WT_STRING not supported!")
-            if len(target_indices[0]) < 1:
-                logger.info(
-                    "calc_wt_means - CAUTION: Skipped %s %s \
-                    for dataset %s!",
-                    wt_string,
-                    wt,
-                    data_info.get("dataset"),
-                )
-                continue
-            dates = [
-                tcoord.units.num2date(tcoord.points[i]) for i in target_indices
-            ]
-            extracted_cube = cube.extract(
-                iris.Constraint(time=lambda t, d=dates: t.point in d[0])
-            )
-            wt_cube_mean = extracted_cube.collapsed("time", iris.analysis.MEAN)
-            plot_maps(wt, cfg, wt_cube_mean, data_info, "mean")
-    elif wt_string == "lwt":
-        for wt in range(1, 28):
-            target_indices = np.where(lwt == wt)
-            if len(target_indices[0]) < 1:
-                logger.info(
-                    "calc_wt_means - CAUTION: Skipped lwt %s \
-                    for dataset %s!",
-                    wt,
-                    data_info.get("dataset"),
-                )
-                continue
-            dates = [
-                tcoord.units.num2date(tcoord.points[i]) for i in target_indices
-            ]
-            extracted_cube = cube.extract(
-                iris.Constraint(time=lambda t, d=dates: t.point in d[0])
-            )
-            wt_cube_mean = extracted_cube.collapsed("time", iris.analysis.MEAN)
-            plot_maps(wt, cfg, wt_cube_mean, data_info, "mean")
+        wt_array = lwt_cube.data[:]
     else:
-        logger.info("WT_STRING NOT SUPPORTED.")
+        raise NameError("wt_array does not exist in line 699.")
+
+    for wt in range(1, len(np.unique(wt_array)) + 1):
+        target_indices = np.where(wt_array == wt)
+        if len(target_indices[0]) < 1:
+            logger.info(
+                "calc_wt_means - CAUTION: Skipped %s %s \
+                for dataset %s!",
+                wt_string,
+                wt,
+                data_info.get("dataset"),
+            )
+            continue
+        dates = [
+            tcoord.units.num2date(tcoord.points[i]) for i in target_indices
+        ]
+        extracted_cube = cube.extract(
+            iris.Constraint(time=lambda t, d=dates: t.point in d[0])
+        )
+        wt_cube_mean = extracted_cube.collapsed("time", iris.analysis.MEAN)
+        plot_maps(wt, cfg, wt_cube_mean, data_info, "mean")
 
     ancestors = [
         f"{data_info.get('preproc_path')}",
@@ -799,90 +768,45 @@ def calc_wt_anomalies(
         wt_string,
     )
 
-    target_indices = []
-    lwt, slwt_eobs, slwt_era5 = [], [], []
-
     if wt_string == "slwt_ERA5":
         slwt_era5_cube = wt_cubes[1]
         tcoord = slwt_era5_cube.coord("time")
-        slwt_era5 = slwt_era5_cube.data[:]
+        wt_array = slwt_era5_cube.data[:]
     elif wt_string == "slwt_EOBS":
         slwt_eobs_cube = wt_cubes[2]
         tcoord = slwt_eobs_cube.coord("time")
-        slwt_eobs = slwt_eobs_cube.data[:]
+        wt_array = slwt_eobs_cube.data[:]
     elif wt_string == "lwt":
         lwt_cube = wt_cubes[0]
         tcoord = lwt_cube.coord("time")
-        lwt = lwt_cube.data[:]
-
-    num_slwt_era5 = len(np.unique(slwt_era5))
-    num_slwt_eobs = len(np.unique(slwt_eobs))
-
-    if num_slwt_eobs != num_slwt_era5:
-        logger.info(
-            "calc_wt_anomalies - CAUTION: unequal number of \
-                    slwt_era5 (%s) and slwt_eobs (%s)!",
-            num_slwt_era5,
-            num_slwt_eobs,
-        )
+        wt_array = lwt_cube.data[:]
+    else:
+        raise NameError("wt_array does not exist in line 817.")
 
-    if "slwt" in wt_string:
-        for wt in range(1, max(num_slwt_era5, num_slwt_eobs)):
-            if wt_string == "slwt_ERA5":
-                target_indices = np.where(slwt_era5 == wt)
-            elif wt_string == "slwt_EOBS":
-                target_indices = np.where(slwt_eobs == wt)
-            else:
-                logger.info("WT_STRING not supported!")
-            if len(target_indices[0]) < 1:
-                logger.info(
-                    "calc_wt_anomalies - CAUTION: Skipped wt %s \
-                    for dataset %s!",
-                    wt,
-                    data_info.get("dataset"),
-                )
-                continue
-            dates = [
-                tcoord.units.num2date(tcoord.points[i]) for i in target_indices
-            ]
-            extracted_cube = cube.extract(
-                iris.Constraint(time=lambda t, d=dates: t.point in d[0])
-            )
-            wt_cube_mean = extracted_cube.collapsed("time", iris.analysis.MEAN)
-            plot_maps(
-                wt,
-                cfg,
-                cube.collapsed("time", iris.analysis.MEAN) - wt_cube_mean,
-                data_info,
-                "anomaly",
-            )
-    elif wt_string == "lwt":
-        for wt in range(1, 28):
-            target_indices = np.where(lwt == wt)
-            if len(target_indices[0]) < 1:
-                logger.info(
-                    "calc_wt_anomalies - CAUTION: Skipped wt %s \
-                    for dataset %s!",
-                    wt,
-                    data_info.get("dataset"),
-                )
-                continue
-            dates = [
-                tcoord.units.num2date(tcoord.points[i]) for i in target_indices
-            ]
-            extracted_cube = cube.extract(
-                iris.Constraint(time=lambda t, d=dates: t.point in d[0])
-            )
-            wt_cube_mean = extracted_cube.collapsed("time", iris.analysis.MEAN)
-            plot_maps(
+    for wt in range(1, len(np.unique(wt_array)) + 1):
+        target_indices = np.where(wt_array == wt)
+        if len(target_indices[0]) < 1:
+            logger.info(
+                "calc_wt_anomalies - CAUTION: Skipped wt %s \
+                for dataset %s!",
                 wt,
-                cfg,
-                cube.collapsed("time", iris.analysis.MEAN) - wt_cube_mean,
-                data_info,
-                "anomaly",
+                data_info.get("dataset"),
             )
-    else:
-        logger.info("WT_STRING NOT SUPPORTED.")
+            continue
+        dates = [
+            tcoord.units.num2date(tcoord.points[i]) for i in target_indices
+        ]
+        extracted_cube = cube.extract(
+            iris.Constraint(time=lambda t, d=dates: t.point in d[0])
+        )
+        wt_cube_mean = extracted_cube.collapsed("time", iris.analysis.MEAN)
+        plot_maps(
+            wt,
+            cfg,
+            cube.collapsed("time", iris.analysis.MEAN) - wt_cube_mean,
+            data_info,
+            "anomaly",
+        )
 
     ancestors = [
         f"{data_info.get('preproc_path')}",
@@ -935,82 +859,39 @@ def calc_wt_std(
         wt_string,
     )
 
-    target_indices = []
-    lwt, slwt_eobs, slwt_era5 = [], [], []
-
     if wt_string == "slwt_ERA5":
         slwt_era5_cube = wt_cubes[1]
         tcoord = slwt_era5_cube.coord("time")
-        slwt_era5 = slwt_era5_cube.data[:]
+        wt_array = slwt_era5_cube.data[:]
     elif wt_string == "slwt_EOBS":
         slwt_eobs_cube = wt_cubes[2]
         tcoord = slwt_eobs_cube.coord("time")
-        slwt_eobs = slwt_eobs_cube.data[:]
+        wt_array = slwt_eobs_cube.data[:]
     elif wt_string == "lwt":
         lwt_cube = wt_cubes[0]
         tcoord = lwt_cube.coord("time")
-        lwt = lwt_cube.data[:]
-
-    num_slwt_era5 = len(np.unique(slwt_era5))
-    num_slwt_eobs = len(np.unique(slwt_eobs))
-
-    if num_slwt_eobs != num_slwt_era5:
-        logger.info(
-            "calc_wt_std - CAUTION: unequal number of \
-                    slwt_era5 (%s) and slwt_eobs (%s)!",
-            num_slwt_era5,
-            num_slwt_eobs,
-        )
+        wt_array = lwt_cube.data[:]
+    else:
+        raise NameError("wt_array does not exist in line 353.")
 
-    if "slwt" in wt_string:
-        for wt in range(1, max(num_slwt_era5, num_slwt_eobs)):
-            if wt_string == "slwt_ERA5":
-                target_indices = np.where(slwt_era5 == wt)
-            elif wt_string == "slwt_EOBS":
-                target_indices = np.where(slwt_eobs == wt)
-            else:
-                logger.info("WT_STRING not supported!")
-            if len(target_indices[0]) < 1:
-                logger.info(
-                    "calc_slwt_obs - CAUTION: Skipped wt %s \
-                    for dataset %s!",
-                    wt,
-                    data_info.get("dataset"),
-                )
-                continue
-            dates = [
-                tcoord.units.num2date(tcoord.points[i]) for i in target_indices
-            ]
-            extracted_cube = cube.extract(
-                iris.Constraint(time=lambda t, d=dates: t.point in d[0])
-            )
-            wt_cube_std = extracted_cube.collapsed(
-                "time", iris.analysis.STD_DEV
-            )
-            plot_maps(wt, cfg, wt_cube_std, data_info, "stddev")
-    elif wt_string == "lwt":
-        for wt in range(1, 28):
-            target_indices = np.where(lwt == wt)
-            if len(target_indices[0]) < 1:
-                logger.info(
-                    "calc_wt_std - CAUTION: Skipped wt %s \
-                    for dataset %s!",
-                    wt,
-                    data_info.get("dataset"),
-                )
-                continue
-            dates = [
-                tcoord.units.num2date(tcoord.points[i]) for i in target_indices
-            ]
-            extracted_cube = cube.extract(
-                iris.Constraint(time=lambda t, d=dates: t.point in d[0])
-            )
-            wt_cube_std = extracted_cube.collapsed(
-                "time", iris.analysis.STD_DEV
+    for wt in range(1, len(np.unique(wt_array)) + 1):
+        target_indices = np.where(wt_array == wt)
+        if len(target_indices[0]) < 1:
+            logger.info(
+                "calc_slwt_obs - CAUTION: Skipped wt %s \
+                for dataset %s!",
+                wt,
+                data_info.get("dataset"),
             )
-            plot_maps(wt, cfg, wt_cube_std, data_info, "stddev")
-    else:
-        logger.info("WT_STRING NOT SUPPORTED.")
+            continue
+        dates = [
+            tcoord.units.num2date(tcoord.points[i]) for i in target_indices
+        ]
+        extracted_cube = cube.extract(
+            iris.Constraint(time=lambda t, d=dates: t.point in d[0])
+        )
+        wt_cube_std = extracted_cube.collapsed("time", iris.analysis.STD_DEV)
+        plot_maps(wt, cfg, wt_cube_std, data_info, "stddev")
 
     ancestors = [
         f"{data_info.get('preproc_path')}",

From 174126effb56d96fd1f267bb999aa753b4103b51 Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Tue, 4 Nov 2025 11:04:40 +0100
Subject: [PATCH 09/57] docstrings

---
 .../diag_scripts/weathertyping/calc_utils.py  | 330 ++++++------------
 .../diag_scripts/weathertyping/plot_utils.py  | 111 +++---
 .../weathertyping/weathertyping.py            |  59 +---
 .../diag_scripts/weathertyping/wt_utils.py    | 330 ++++++------------
 4 files changed, 281 insertions(+), 549 deletions(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/calc_utils.py b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
index 019660b1fb..769771ee8f 100644
--- a/esmvaltool/diag_scripts/weathertyping/calc_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
@@ -44,28 +44,18 @@ def calc_slwt_obs(
     ancestors: list,
     timerange: str,
 ) -> np.array:
-    """Calculate simplified weathertypes for observation datasets based on \
-        precipitation patterns over specified area.
+    """Calculate simplified weathertypes for observational data.
 
     Args:
-    ----
-        cfg : dict
-            Configuration dictionary from recipe
-        lwt : np.array
-            Array of Lamb WT
-        cube : iris.cube.Cube
-            Cube of psl data
-        dataset : str
-            Name of dataset
-        ancestors : list
-            List of ancestors
-        timerange : str
-            Time range for the calculation
-
-    Returns
-    -------
-        np.array
-            Simplified Lamb Weathertypes
+        cfg (dict): Configuration dictionary from recipe
+        lwt (np.array): Array of Lamb WT
+        cube (iris.cube.Cube): Cube of psl data
+        dataset (str): Name of dataset
+        ancestors (list): List of ancestors
+        timerange (str): Time range for the calculation
+
+    Returns:
+        np.array: Simplified Lamb Weathertypes
     """
     logger.info("Calculating simplified Lamb Weathertypes for %s", dataset)
 
@@ -131,10 +121,8 @@ def calc_const():
     scheme.
     Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
 
-    Returns
-    -------
-        tuple
-            The four constants needed for WT calculation.
+    Returns:
+        tuple: The four constants needed for WT calculation.
     """
     const1 = 1 / np.cos(45 * np.pi / 180)
     const2 = np.sin(45 * np.pi / 180) / np.sin(40 * np.pi / 180)
@@ -153,14 +141,10 @@ def calc_westerly_flow(cube: iris.cube.Cube) -> np.array:
     Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
 
     Args:
-    ----
-        cube : iris.cube.Cube
-            Cube of psl data.
+        cube (iris.cube.Cube): Cube of psl data.
 
-    Returns
-    -------
-        np.array
-            westerly flow
+    Returns:
+        np.array: westerly flow
     """
     return 1 / 2 * (cube.data[:, 1, 2] + cube.data[:, 1, 4]) - 1 / 2 * (
         cube.data[:, 3, 2] + cube.data[:, 3, 4]
@@ -176,15 +160,11 @@ def calc_southerly_flow(cube: iris.cube.Cube, const1: float) -> np.array:
     Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
 
     Args:
-    ----
-        cube : iris.cube.Cube
-            cube of psl data
-        const1 : float
-            const1
-    Returns
-    -------
-        np.array
-            southerly flow
+        cube (iris.cube.Cube): cube of psl data
+        const1 (float): const1
+
+    Returns:
+        np.array: southerly flow
     """
     return const1 * (
         1
@@ -208,15 +188,12 @@ def calc_resultant_flow(
 
     Args:
     ----
-        westerly_flow : np.array
-            westerly flow
-        southerly_flow : np.array
-            southerly flow
+        westerly_flow (np.array): westerly flow
+        southerly_flow (np.array): southerly flow
 
     Returns
     -------
-        np.array
-            resultant flow
+        np.array: resultant flow
     """
     return (southerly_flow**2 + westerly_flow**2) ** (1 / 2)
 
@@ -233,17 +210,13 @@ def calc_westerly_shear_velocity(
 
     Args:
     ----
-        cube : iris.cube.Cube
-            cube of psl data
-        const2 : float
-            const2
-        const3 : float
-            const3
+        cube (iris.cube.Cube): cube of psl data
+        const2 (float): const2
+        const3 (float): const3
 
     Returns
     -------
-        np.array
-            westerly shear velocity
+        np.array: westerly shear velocity
     """
     return const2 * (
         1 / 2 * (cube.data[:, 0, 2] + cube.data[:, 0, 4])
@@ -266,15 +239,12 @@ def calc_southerly_shear_velocity(
 
     Args:
     ----
-        cube : iris.cube.Cube
-            cube of psl data
-        const4 : float
-            const
+        cube (iris.cube.Cube): cube of psl data
+        const4 (float): const
 
     Returns
     -------
-        np.array
-            southerly shear velocity
+        np.array: southerly shear velocity
     """
     return const4 * (
         1
@@ -303,16 +273,11 @@ def calc_total_shear_velocity(
     Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
 
     Args:
-    ----
-        westerly_shear_velocity : np.array
-            westerly shear velocity
-        southerly_shear_velocity : np.array
-            southerly shear velocity
+        westerly_shear_velocity (np.array): westerly shear velocity
+        southerly_shear_velocity (np.array): southerly shear velocity
 
     Returns
-    -------
-        np.array
-            total shear velocity
+        np.array:total shear velocity
     """
     return westerly_shear_velocity + southerly_shear_velocity
 
@@ -326,17 +291,13 @@ def wt_algorithm(cube: iris.cube.Cube, dataset: str) -> np.array:
     Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
 
     Args:
-    ----
-        cube : iris.cube.Cube
-            Cube of psl data.
-        dataset : str
-            Name of dataset.
+        cube (iris.cube.Cube): Cube of psl data
+        dataset (str): Name of dataset
 
-    Returns
-    -------
-        np.array
-            Array of Lamb WT for each day
+    Returns:
+        np.array: Lamb weathertypes
     """
+
     # lats and lons corresponding to datapoints
     # 55, 5 -> 1
     # 55, 15 -> 2
@@ -466,18 +427,13 @@ def calc_lwt_slwt_model(
     data_info: dict,
     predefined_slwt: bool | dict,
 ):
-    """Calculate Lamb WT and simplified WT for model data.
+    """Calculate Lamb as well as simplified weathertypes for model.
 
     Args:
-    ----
-        cfg : dict
-            Configuration dictionary from recipe
-        cube : iris.cube.Cube
-            PSL field of dataset
-        data_info : dict
-            Dictionary with info to dataset
-        predefined_slwt : bool | dict
-            If False, automatic_slwt will be used.
+        cfg (dict): Configuration dictionary from recipe
+        cube (iris.cube.Cube): PSL field of dataset
+        data_info (dict): Dictionary with info to dataset
+        predefined_slwt (bool | dict): If False, automatic_slwt will be used.
             If dict, this mapping dict will be used.
             (see recipe option predefined_slwt)
     """
@@ -570,19 +526,7 @@ def calc_lwt_slwt_model(
 
 
 def rmse(subarray1: np.array, subarray2: np.array) -> np.array:
-    """Calculate rsme.
-
-    Args:
-        subarray1 : np.array
-            First subarray
-        subarray2 : np.array
-            Second subarray
-
-    Returns
-    -------
-        np.array
-            rsme array
-    """
+    """Calculate root mean square error between two arrays."""
     return np.sqrt(np.mean((subarray1 - subarray2) ** 2))
 
 
@@ -592,31 +536,16 @@ def process_prcp_mean(
     dataset: str,
     timerange: str,
 ) -> list:
-    """Process precipitation fields.
-
-    Specified area to get a list of selected pairs of weathertypes
-    with the highest correlation (higher than correlation_threshold)
-    and smallest RSME (smaller than rsme_threshold) for
-    further processing and simplifying the WT.
+    """Return which weathertypes can be grouped together for a certain precipitation pattern.
 
     Args:
-        cfg : dict
-            Configuration dictionary from recipe
-        data : np.array
-            Array of precipitation means for each WT
-        correlation_threshold : float
-            correlation threshold
-        rmse_threshold : float
-            rsme threshold
-        dataset : str
-            Name of dataset
-        timerange : str
-            Time range of dataset
+        cfg (dict): Configuration dictionary from recipe
+        data (np.array): Array of precipitation means for each WT
+        dataset (str): Name of dataset
+        timerange (str): Time range of dataset
 
-    Returns
-    -------
-        list
-            Selected pairs of WT. This is passed to get_mapping_dict
+    Returns:
+        list: Selected pairs of WT. This is passed to get_mapping_dict
     """
     logger.info("Calculating corr and rsme matrices for %s", dataset)
 
@@ -658,17 +587,13 @@ def calc_wt_means(
     wt_cubes: iris.cube.CubeList,
     data_info: dict,
 ):
-    """Calculate means of fields of each weathertype.
+    """Calculate means for psl, tas or pr for weathertypes.
 
     Args:
-        cfg : dict
-            Configuration dictionary from recipe
-        cube : iris.cube.Cube
-            Cube with variable data
-        wt_cubes : iris.cube.CubeList
-            List of cubes of lwt, slwt_ERA5 and slwt_EOBS
-        data_info : dict
-            Dictionary with info to dataset
+        cfg (dict): Configuration dictionary from recipe
+        cube (iris.cube.Cube): Cube with variable data
+        wt_cubes (iris.cube.CubeList): List of cubes of lwt, slwt_ERA5 and slwt_EOBS
+        data_info (dict): Dictionary with info to dataset
     """
     var_name = data_info.get("var")
     wt_string = data_info.get("wt_string")
@@ -681,20 +606,7 @@ def calc_wt_means(
         wt_string,
     )
 
-    if wt_string == "slwt_ERA5":
-        slwt_era5_cube = wt_cubes[1]
-        tcoord = slwt_era5_cube.coord("time")
-        wt_array = slwt_era5_cube.data[:]
-    elif wt_string == "slwt_EOBS":
-        slwt_eobs_cube = wt_cubes[2]
-        tcoord = slwt_eobs_cube.coord("time")
-        wt_array = slwt_eobs_cube.data[:]
-    elif wt_string == "lwt":
-        lwt_cube = wt_cubes[0]
-        tcoord = lwt_cube.coord("time")
-        wt_array = lwt_cube.data[:]
-    else:
-        raise NameError("wt_array does not exist in line 699.")
+    wt_array, tcoord = get_wt_array(wt_string, wt_cubes)
 
     for wt in range(1, len(np.unique(wt_array)) + 1):
         target_indices = np.where(wt_array == wt)
@@ -740,23 +652,52 @@ def calc_wt_means(
     )
 
 
+def get_wt_array(wt_string: str, wt_cubes: iris.cube.CubeList) -> np.array:
+    """Get weathertype array and time coordinate based on wt_string.
+
+    Args:
+        wt_string(str): string for weathertype selection
+        wt_cubes(iris.cube.CubeList): list of weathertype cubes
+
+    Raises:
+        NameError: if wt_array does not exist for the given wt_string
+
+    Returns:
+        (np.array, np.array): weathertype array and time coordinate
+    """
+    if wt_string == "slwt_ERA5":
+        slwt_era5_cube = wt_cubes[1]
+        tcoord = slwt_era5_cube.coord("time")
+        wt_array = slwt_era5_cube.data[:]
+    elif wt_string == "slwt_EOBS":
+        slwt_eobs_cube = wt_cubes[2]
+        tcoord = slwt_eobs_cube.coord("time")
+        wt_array = slwt_eobs_cube.data[:]
+    elif wt_string == "lwt":
+        lwt_cube = wt_cubes[0]
+        tcoord = lwt_cube.coord("time")
+        wt_array = lwt_cube.data[:]
+    else:
+        raise NameError(
+            "wt_array does not exist for calc_wt_means, calc_wt_anomalies or calc_wt_std."
+        )
+
+    return wt_array, tcoord
+
+
 def calc_wt_anomalies(
     cfg: dict,
     cube: iris.cube.Cube,
     wt_cubes: iris.cube.CubeList,
     data_info: dict,
 ):
-    """Calculate anomalies of fields of each weathertype.
+    """Calculate anomalies for psl, tas and pr for weathertypes.
 
     Args:
-        cfg : dict
-            Configuration dictionary from recipe
-        cube : iris.cube.Cube
-            Cube with variable data
-        wt_cubes : iris.cube.CubeList
-            List of cubes of lwt, slwt_ERA5 and slwt_EOBS
-        data_info : dict
-            Dictionary with info to dataset
+        cfg (dict): Configuration dictionary from recipe
+        cube (iris.cube.Cube): Cube with variable data
+        wt_cubes (iris.cube.CubeList): List of cubes of lwt, slwt_ERA5 and slwt_EOBS
+        data_info (dict): Dictionary with info to dataset
     """
     var_name = data_info.get("var_name")
     wt_string = data_info.get("wt_string")
@@ -768,20 +709,7 @@ def calc_wt_anomalies(
         wt_string,
     )
 
-    if wt_string == "slwt_ERA5":
-        slwt_era5_cube = wt_cubes[1]
-        tcoord = slwt_era5_cube.coord("time")
-        wt_array = slwt_era5_cube.data[:]
-    elif wt_string == "slwt_EOBS":
-        slwt_eobs_cube = wt_cubes[2]
-        tcoord = slwt_eobs_cube.coord("time")
-        wt_array = slwt_eobs_cube.data[:]
-    elif wt_string == "lwt":
-        lwt_cube = wt_cubes[0]
-        tcoord = lwt_cube.coord("time")
-        wt_array = lwt_cube.data[:]
-    else:
-        raise NameError("wt_array does not exist in line 817.")
+    wt_array, tcoord = get_wt_array(wt_string, wt_cubes)
 
     for wt in range(1, len(np.unique(wt_array)) + 1):
         target_indices = np.where(wt_array == wt)
@@ -821,10 +749,8 @@ def calc_wt_anomalies(
         ["anomaly"],
     )
 
-    local_path = f"{cfg.get('plot_dir')}/anomaly"
-
     log_provenance(
-        f"{local_path}/{wt_string}_{wt}_{data_info.get('dataset')}_{data_info.get('ensemble', '')}"
+        f"{cfg.get('plot_dir')}/anomaly/{wt_string}_{wt}_{data_info.get('dataset')}_{data_info.get('ensemble', '')}"
         f"_{var_name}_anomaly__{data_info.get('timerange')}",
         cfg,
         provenance_record,
@@ -837,17 +763,13 @@ def calc_wt_std(
     wt_cubes: iris.cube.CubeList,
     data_info: dict,
 ):
-    """Calculate standard deviation of fields of each weathertype.
+    """Calculate standard deviation for psl, tas and pr for weathertypes.
 
     Args:
-        cfg : dict
-            Configuration dictionary from recipe
-        cube : iris.cube.Cube
-            Cube with variable data
-        wt_cubes : iris.cube.CubeList
-            List of cubes of lwt, slwt_ERA5 and slwt_EOBS
-        data_info : dict
-            Dictionary with info to dataset
+        cfg (dict): Configuration dictionary from recipe
+        cube (iris.cube.Cube): Cube with variable data
+        wt_cubes (iris.cube.CubeList): List of cubes of lwt, slwt_ERA5 and slwt_EOBS
+        data_info (dict): Dictionary with info to dataset
     """
     var_name = data_info.get("var_name")
     wt_string = data_info.get("wt_string")
@@ -859,20 +781,7 @@ def calc_wt_std(
         wt_string,
     )
 
-    if wt_string == "slwt_ERA5":
-        slwt_era5_cube = wt_cubes[1]
-        tcoord = slwt_era5_cube.coord("time")
-        wt_array = slwt_era5_cube.data[:]
-    elif wt_string == "slwt_EOBS":
-        slwt_eobs_cube = wt_cubes[2]
-        tcoord = slwt_eobs_cube.coord("time")
-        wt_array = slwt_eobs_cube.data[:]
-    elif wt_string == "lwt":
-        lwt_cube = wt_cubes[0]
-        tcoord = lwt_cube.coord("time")
-        wt_array = lwt_cube.data[:]
-    else:
-        raise NameError("wt_array does not exist in line 353.")
+    wt_array, tcoord = get_wt_array(wt_string, wt_cubes)
 
     for wt in range(1, len(np.unique(wt_array)) + 1):
         target_indices = np.where(wt_array == wt)
@@ -918,18 +827,12 @@ def calc_wt_std(
 
 
 def calc_lwt_model(cfg: dict, cube: iris.cube.Cube, data_info: dict):
-    """Calculate lwt for model data.
+    """Calculate Lamb weathertypes for models.
 
     Args:
-    ----
-        cfg : dict
-            Configuration dictionary from recipe
-        cube : iris.cube.Cube
-            Cube to keep time coordinate
-        dataset : str
-            Name of dataset
-        data_info : dict
-            Dictionary with info to dataset
+        cfg (dict): Configuration dictionary from recipe
+        cube (iris.cube.Cube): Cube with variable data
+        data_info (dict): Dictionary with info to dataset
     """
     driver = get_driver(data_info)
 
@@ -993,20 +896,15 @@ def plot_means(
     data_info: dict,
     only_lwt=False,
 ):
-    """Wrapper function to plot various means/std/anomalies.
+    """Wrapper function for plotting means, anomalies and standard deviations.
 
     Args:
-        cfg : dict
-            Configuration dictionary from recipe
-        preproc_var : np.array
-            Preprocessed variable cube
-        wt_cubes : iris.cube.Cube
-            List of cubes of lwt, slwt_ERA5 and slwt_EOBS
-        data_info : dict
-            Dictionary with info to dataset
-        only_lwt : bool
-            If True, only lwt means are calculated.
-            If False, lwt, slwt_ERA5 and slwt_EOBS means are calculated.
+        cfg (dict): Configuration dictionary from recipe
+        preproc_var (np.array): Preprocessed variable cube
+        wt_cubes (iris.cube.Cube): List of cubes of lwt, slwt_ERA5 and slwt_EOBS
+        data_info (dict): Dictionary with info to dataset
+        only_lwt (bool, optional): If True, only lwt means are calculated.
+            If False, lwt, slwt_ERA5 and slwt_EOBS means are calculated. Defaults to False.
     """
     if not only_lwt:
         data_info["wt_string"] = "lwt"
diff --git a/esmvaltool/diag_scripts/weathertyping/plot_utils.py b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
index 7f4c11bfeb..56a496e6a0 100644
--- a/esmvaltool/diag_scripts/weathertyping/plot_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
@@ -34,17 +34,13 @@
 
 
 def generate_grayscale_hex_values(x):
-    """Generate grayscale values for plotting seasonal occurrence.
+    """Generate grayscale values for plotting seasonal occurences.
 
     Args:
-    ----
-        x : int
-            number of weathertypes
-
-    Returns
-    -------
-        np.list
-            array with grayscale values as hex
+        x (list): Amount of weathertypes.
+
+    Returns:
+        list: List of grayscale hex values
     """
     grayscale_values = np.linspace(0, 1, x)
     grayscale_hex = [
@@ -58,16 +54,12 @@ def generate_grayscale_hex_values(x):
 def plot_seasonal_occurrence(
     cfg: dict, wt_cubes: iris.cube.Cube, data_info: dict
 ):
-    """Plot relative monthly/seasonal occurrence of weathertypes.
+    """Plot seasonal occurences of weathertypes.
 
     Args:
-    ----
-        cfg : dict
-            Configuration dictionary from recipe
-        wt_cubes : iris.cube.Cube
-            Cube with weathertypes
-        data_info : dict
-            Dictionary with relevant info to dataset
+        cfg (dict): Configuration dictionary from recipe
+        wt_cubes (iris.cube.Cube): List of cubes of lwt, slwt_ERA5 and slwt_EOBS
+        data_info (dict): Dictionary with info to dataset
     """
     driver = get_driver(data_info)
 
@@ -97,28 +89,23 @@ def plot_seasonal_occurrence(
     for cube in wt_cubes:
         month_dict = {}
         for month in range(1, 13):
-            month_constraint = iris.Constraint(
-                time=iris.time.PartialDateTime(month=month)
-            )
-            array = cube.extract(month_constraint).data
+            array = cube.extract(
+                iris.Constraint(time=iris.time.PartialDateTime(month=month))
+            ).data
             unique, counts = np.unique(array, return_counts=True)
-            count_dict = dict(zip(unique, counts / sum(counts), strict=False))
-            month_dict[month] = count_dict
+            month_dict[month] = dict(
+                zip(unique, counts / sum(counts), strict=False)
+            )
         relative_occurrences[cube.long_name] = month_dict
 
-    x = month_list
-
     for wt_string in relative_occurrences:
         wt_numbers = max(
             len(value)
             for value in relative_occurrences.get(wt_string).values()
         )
-        colors = generate_grayscale_hex_values(wt_numbers)
         wt_stack = np.zeros((wt_numbers, 12))
         for month, month_value in relative_occurrences.get(wt_string).items():
-            print(month_value)
             for wt in month_value.keys():
-                print(month_value.get(wt))
                 wt_stack[np.int8(wt - 1), month - 1] = month_value.get(wt)
 
         y = np.vstack(list(wt_stack))
@@ -131,7 +118,9 @@ def plot_seasonal_occurrence(
             {data_info.get('dataset')}, {data_info.get('timerange')}"
         )
 
-        ax_.stackplot(x, y, colors=colors)
+        ax_.stackplot(
+            month_list, y, colors=generate_grayscale_hex_values(wt_numbers)
+        )
 
         ax_.legend(
             loc="upper center",
@@ -165,23 +154,16 @@ def plot_seasonal_occurrence(
 def plot_maps(
     wt: np.array, cfg: dict, cube: iris.cube.Cube, data_info: dict, mode: str
 ):
-    """Plot maps.
+    """Function to plot maps of means, std and anomalies.
 
     Args:
-    ----
-        wt : np.array
-            weathertype number
-        cfg : dict
-            Configuration dicitonary from recipe
-        cube : iris.cube.Cube
-            Data to be plotted
-        data_info : dict
-            Dictionary with info to dataset
-        mode : str
-            Statistics that is used
+        wt (np.array): WT array
+        cfg (dict): Configuration dictionary from recipe
+        cube (iris.cube.Cube): Data to be plotted
+        data_info (dict): Dictionary with info to dataset
+        mode (str): Statistics that is used
     """
     var_name = data_info.get("var")
-    driver = get_driver(data_info)
 
     logger.info(
         "Plotting %s %s %s for %s %s",
@@ -192,11 +174,6 @@ def plot_maps(
         wt,
     )
 
-    local_path = f"{cfg.get('plot_dir')}/{mode}"
-
-    if not os.path.exists(f"{local_path}"):
-        os.makedirs(f"{local_path}")
-
     ax = plt.axes(projection=ccrs.PlateCarree())
 
     if data_info.get("var") == "psl":
@@ -265,12 +242,15 @@ def plot_maps(
     ax.coastlines()
     ax.add_feature(cfeature.BORDERS, linestyle=":")
 
+    if not os.path.exists(f"{cfg.get('plot_dir')}/{mode}"):
+        os.makedirs(f"{cfg.get('plot_dir')}/{mode}")
+
     plt.savefig(
-        f"{local_path}/{data_info.get('wt_string')}_{wt}{driver}_{data_info.get('dataset')}_{data_info.get('ensemble', '')}"
+        f"{cfg.get('plot_dir')}/{mode}/{data_info.get('wt_string')}_{wt}{get_driver(data_info)}_{data_info.get('dataset')}_{data_info.get('ensemble', '')}"
         f"_{var_name}_{mode}_{data_info.get('timerange')}.png"
     )
     plt.savefig(
-        f"{local_path}/{data_info.get('wt_string')}_{wt}{driver}_{data_info.get('dataset')}_{data_info.get('ensemble', '')}_"
+        f"{cfg.get('plot_dir')}/{mode}/{data_info.get('wt_string')}_{wt}{get_driver(data_info)}_{data_info.get('dataset')}_{data_info.get('ensemble', '')}_"
         f"{var_name}_{mode}_{data_info.get('timerange')}.pdf"
     )
     plt.close()
@@ -283,20 +263,14 @@ def plot_corr_rmse_heatmaps(
     dataset: str,
     timerange: str,
 ):
-    """Plot heatmaps for correlation and rmse matrices.
+    """Plot correlation and rmse heatmaps.
 
     Args:
-    ----
-        cfg : dict
-            Configuration dictionary from recipe
-        pattern_correlation_matrix : np.array
-            pattern correlation matrix
-        rmse_matrix : np.array
-            rmse matrix
-        dataset : str
-            string of dataset
-        timerange : str
-            string of timerange
+        cfg (dict): Configuration dictionary from recipe
+        pattern_correlation_matrix (np.array): Pattern correlation matrix
+        rmse_matrix (np.array): RMSE matrix
+        dataset (str): Name of dataset
+        timerange (str): Time range for the calculation
     """
     output_path = f"{cfg.get('plot_dir')}/heatmaps"
 
@@ -367,18 +341,13 @@ def plot_corr_rmse_heatmaps(
 
 
 def get_colormap(colormap_string: str) -> ListedColormap:
-    """Get colormaps based on string.
+    """Get colormaps for plottings
 
     Args:
-    ----
-        colormap_string : str
-            String to get Colormaps for either
-            psl, tas or precipitation.
-
-    Returns
-    -------
-        ListedColormap
-            Choosen Colormap
+        colormap_string (str): string to identify colormap
+
+    Returns:
+        ListedColormap: Colormap for the specified variable
     """
     misc_seq_2_disc = [
         (230 / 255, 240 / 255, 240 / 255),
diff --git a/esmvaltool/diag_scripts/weathertyping/weathertyping.py b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
index b2c9c0ff13..3d1d2a1979 100644
--- a/esmvaltool/diag_scripts/weathertyping/weathertyping.py
+++ b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
@@ -40,13 +40,9 @@ def process_models_automatic_slwt(
     """Process model data for calculating Lamb and simplified weathertypes.
 
     Args:
-    ----
-        cfg : dict
-            Nested dictionary of metadata
-        dataset_vars : list
-            List of variable dictionaries for a specific dataset
-        data_info : dict
-            Dictionary holding dataset information.
+        cfg (dict): Nested dictionary of metadata
+        dataset_vars (list): List of variable dictionaries for a specific dataset
+        data_info (dict): Dictionary holding dataset information.
     """
     for ensemble_var in dataset_vars:
         if ensemble_var.get("preprocessor") == "weathertype_preproc":
@@ -104,15 +100,10 @@ def process_era5_automatic_slwt(
     """Process ERA5 data for calculating Lamb and simplified weathertypes.
 
     Args:
-    -----
-        data_info : dict
-            Dictionary holding dataset information.
-        preproc_variables_dict : dict
-            Dictionary holding preprocessed variables for all datasets.
-        cfg : dict
-            Nested dictionary of metadata
-        dataset_vars : list
-            List of variable dictionaries for a specific dataset
+        data_info (dict): Dictionary holding dataset information.
+        preproc_variables_dict (dict): Dictionary holding preprocessed variables for all datasets.
+        cfg (dict): Nested dictionary of metadata
+        dataset_vars (list): List of variable dictionaries for a specific dataset
     """
     wt_preproc, wt_preproc_prcp, wt_preproc_prcp_eobs = load_wt_preprocessors(
         data_info["dataset"], preproc_variables_dict
@@ -180,9 +171,7 @@ def run_automatic_slwt(cfg: dict):
     of the weathertypes.
 
     Args:
-    ----
-        cfg : dict
-            Nested dictionary of metadata
+        cfg (dict): Nested dictionary of metadata
     """
     preproc_variables_dict = group_metadata(
         cfg.get("input_data").values(), "dataset"
@@ -206,15 +195,10 @@ def process_era5_lwt(preproc_variables_dict, cfg, dataset_vars, data_info):
     """Process ERA5 data for calculating Lamb weathertypes.
 
     Args:
-    ----
-        preproc_variables_dict : dict
-            Dictionary holding preprocessed variables for all datasets.
-        cfg : dict
-            Nested dictionary of metadata
-        dataset_vars : list
-            List of variable dictionaries for a specific dataset
-        data_info : dict
-            Dictionary holding dataset information.
+        preproc_variables_dict (dict): Dictionary holding preprocessed variables for all datasets.
+        cfg (dict): Nested dictionary of metadata
+        dataset_vars (list): List of variable dictionaries for a specific dataset
+        data_info (dict): Dictionary holding dataset information.
     """
     wt_preproc, _, _ = load_wt_preprocessors(
         data_info["dataset"], preproc_variables_dict
@@ -265,13 +249,9 @@ def process_models_lwt(cfg: dict, dataset_vars: list, data_info: dict):
     """Process model data for calculating Lamb weathertypes.
 
     Args:
-    ----
-        cfg : dict
-            Nested dictionary of metadata
-        dataset_vars : list
-            List of variable dictionaries for a specific dataset
-        data_info : dict
-            Dictionary holding dataset information.
+        cfg (dict): Nested dictionary of metadata
+        dataset_vars (list): List of variable dictionaries for a specific dataset
+        data_info (dict): Dictionary holding dataset information.
     """
     for ensemble_var in dataset_vars:
         if ensemble_var.get("preprocessor") == "weathertype_preproc":
@@ -330,10 +310,7 @@ def run_lwt(cfg: dict):
     Plot seasonal occurrence of the weathertypes.
 
     Args:
-    ----
-        cfg : dict
-            Nested dictionary of metadata
-
+        cfg (dict): Nested dictionary of metadata
     """
     preproc_variables_dict = group_metadata(
         cfg.get("input_data").values(), "dataset"
@@ -358,9 +335,7 @@ def run_my_diagnostic(cfg: dict):
     """Run the weathertyping diagnostic.
 
     Args:
-    -----
-        cfg : dict
-            nested dictionary of metadata
+        cfg (dict): Nested dictionary of metadata
     """
     automatic_slwt = cfg.get("automatic_slwt")
 
diff --git a/esmvaltool/diag_scripts/weathertyping/wt_utils.py b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
index ab57ab3115..03ad8e4279 100644
--- a/esmvaltool/diag_scripts/weathertyping/wt_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
@@ -26,17 +26,13 @@
 
 
 def get_driver(data_info: dict) -> str:
-    """Get driving model string.
+    """Get driving model name and string for further use.
 
     Args:
-    ----
-        data_info : dict
-            Data information dictionary.
-
-    Returns
-    -------
-        str
-            Driver string with leading underscore or empty string.
+        data_info (dict): Data information dictionary.
+
+    Returns:
+        str: Driver string with leading underscore or empty string.
     """
     driver = data_info.get("driver", "")
     if driver != "":
@@ -48,16 +44,11 @@ def load_wt_preprocessors(dataset: str, preproc_variables_dict: dict):
     """Load preprocessor cubes for calculating Lamb weathertypes.
 
     Args:
-    ----
-        dataset : str
-            dataset name
-        preproc_variables_dict : dict
-            dictionary of preprocessor variables
-
-    Returns
-    -------
-        list
-            list of preprocessor vars for weathertyping
+        dataset (str): Name of dataset
+        preproc_variables_dict (dict): Dictionary with info on preprocessor variables
+
+    Returns:
+        tuple: Preprocessor cubes for weathertyping
     """
     wt_preproc = iris.load_cube(
         preproc_variables_dict.get(dataset)[0].get("filename")
@@ -73,19 +64,14 @@ def load_wt_preprocessors(dataset: str, preproc_variables_dict: dict):
 
 
 def get_ancestors_era5_eobs(dataset: str, preproc_variables_dict: dict):
-    """Get ancestors for ERA5/E-OBS.
+    """Get ancestors for observational data.
 
     Args:
-    ----
-       dataset : str
-            dataset name
-        preproc_variables_dict : dict
-            dictionary of preprocessor variables
-
-    Returns
-    -------
-        tuple(list, list)
-            lists of ERA5/E-OBS ancestors
+        dataset (str): Name of dataset
+        preproc_variables_dict (dict): Dictionary with info on preprocessor variables
+
+    Returns:
+        tuple: Lists of ERA5 and E-OBS ancestors
     """
     era5_ancestors = [
         preproc_variables_dict.get(dataset)[0].get("filename"),
@@ -98,47 +84,35 @@ def get_ancestors_era5_eobs(dataset: str, preproc_variables_dict: dict):
     return era5_ancestors, eobs_ancestors
 
 
-def get_model_output_filepath(dataset: str, value: list):
-    """Generate output filepath for model data.
+def get_model_output_filepath(dataset: str, data_info: list):
+    """Get output filepaths for models.
 
     Args:
-    ----
-        dataset : str
-            dataset name
-        value : dict
-            Model variables
-
-    Returns
-    -------
-        tuple(str, str)
-            Output filepath and preprocessor path for
-            future referencing.
+        dataset (str): Name of dataset
+        data_info (list): Model variables
+
+    Returns:
+        tuple: Output filepath and preprocessor path for future referencing.
     """
-    timerange = value.get("timerange").replace("/", "-")
-    experiment = value.get("exp")
-    ensemble = value.get("ensemble")
-    driver = value.get("driver", "")
+    timerange = data_info.get("timerange").replace("/", "-")
+    experiment = data_info.get("exp")
+    ensemble = data_info.get("ensemble")
+    driver = data_info.get("driver", "")
 
     out_path = f"{dataset}/{driver}/{experiment}/{ensemble}/{timerange}"
-    preproc_path = value.get("filename")
+    preproc_path = data_info.get("filename")
 
     return out_path, preproc_path
 
 
 def get_preproc_lists(preproc_vars: list):
-    """Put preprocessor variables and paths into list for further use.
+    """Put preprocessors and paths into lists for further use.
 
     Args:
-    ----
-        preproc_vars : list
-            List of variables for specific
-            dataset
-
-    Returns
-    -------
-        tuple(list, list)
-            List of preprocessor cubes for mean calculations
-            as well as path to files.
+        preproc_vars (list): List of preprocessor variables.
+
+    Returns:
+        tuple: Preprocessor cubes and paths.
     """
     preproc_path_psl = preproc_vars[-3].get("filename")
     preproc_path_prcp = preproc_vars[-2].get("filename")
@@ -155,18 +129,13 @@ def get_preproc_lists(preproc_vars: list):
 
 
 def get_preproc_lists_ensemble(preproc_vars: list):
-    """Put preprocessor variables and paths into list for further use.
+    """Put preprocessors and paths into lists for further use.
 
     Args:
-    ----
-        preproc_vars : list
-            Variable for specific ensemble member.
-
-    Returns
-    -------
-        tuple(iris.cube.Cube, str)
-            Preprocessor cube for mean calculations
-            as well as path to files.
+        preproc_vars (list): List of preprocessor variables.
+
+    Returns:
+        tuple: Preprocessor cubes and paths.
     """
     preproc_path = preproc_vars.get("filename")
 
@@ -179,16 +148,13 @@ def get_preproc_lists_ensemble(preproc_vars: list):
 
 
 def get_looping_dict(preproc_vars: list):
-    """Put cubes into dictionary for further use.
+    """Put variable preprocessors into dict for looping.
 
     Args:
-    ----
-        preproc_vars : list
-            Values of dataset dictionary
+        preproc_vars (list): List of preprocessor variables.
 
-    Returns
-    -------
-        Dictionary of the form {'var': [preproc_var, preproc_path]}
+    Returns:
+        dict: Dictionary of preprocessor cubes and paths.
     """
     preproc, preproc_path = get_preproc_lists(preproc_vars)
     dict_ = {
@@ -200,23 +166,14 @@ def get_looping_dict(preproc_vars: list):
 
 
 def load_wt_files(path: str, only_lwt=False):
-    """Load *.nc files of weathertype data.
-
-    If only_lwt is true, only Lamb
-    weathertypes will be loaded. (useful for automatic_slwt = False)
+    """Load wt files.
 
     Args:
-    ----
-        path : str
-            Path to weathertype data.
-        only_lwt : bool, optional
-            If True, only Lamb weathertypes will be loaded.
-            Defaults to False.
-
-    Returns
-    -------
-        list(iris.cube.Cube)
-            List of weathertype cubes.
+        path (str): Path of wt file
+        only_lwt (bool, optional): If True, only Lamb weathertypes will be loaded. Defaults to False.
+
+    Returns:
+        list: List of weathertype cubes.
     """
     if not only_lwt:
         lwt_cube = iris.load_cube(path, "lwt")
@@ -240,22 +197,14 @@ def get_provenance_record(
     """Get provenance record.
 
     Args:
-    ----
-        caption : str
-            Caption for plots
-        ancestors : list
-            List of ancestors
-        long_names : list
-            Variable long names
-        plot_types : bool | list
-            Type of plot. Can be false if output is not a plot.
-        statistics : bool | list
-            Types of statistics used.
-
-    Returns
-    -------
-        dict
-            Provenance dictionary.
+        caption (str): Caption of plot
+        ancestors (list): List of ancestor plots
+        long_names (list): List of variable long names
+        plot_types (bool | list): Type of plot
+        statistics (bool | list): Types of statistics used
+
+    Returns:
+        dict: Provenance record
     """
     record = {
         "caption": caption,
@@ -274,16 +223,12 @@ def get_provenance_record(
 
 
 def log_provenance(filename: str, cfg: dict, provenance_record: dict):
-    """Log provenance. Produces xml file provenance info.
+    """Log provenance.
 
     Args:
-    ----
-        filename : str
-            Output name of provenance.
-        cfg : dict
-            Configuration dictionary provided by recipe.
-        provenance_record : dict
-            Provenance record dictionary.
+        filename (str): Filename of xml file
+        cfg (dict): Configuration dictionary
+        provenance_record (dict): Provenance record dictionary
     """
     with ProvenanceLogger(cfg) as provenance_logger:
         provenance_logger.log(filename, provenance_record)
@@ -292,17 +237,13 @@ def log_provenance(filename: str, cfg: dict, provenance_record: dict):
 
 
 def turn_list_to_mapping_dict(list_: list) -> dict:
-    """Turn list of combined WT into a dictionary for further processing.
+    """Turn list of combined WT to a dictionary for further use.
 
     Args:
-    ----
-        list_ : list
-            List where entries are lists with related WT
-
-    Returns
-    -------
-        dict
-            Mapping dictionary keys are simplified WT, values are Lamb WT
+        list_ (list): List of combined WTs
+
+    Returns:
+        dict: Dictionary of combined WTs
     """
     result_dict = {}
 
@@ -320,16 +261,10 @@ def get_mapping_dict(selected_pairs: list) -> dict:
     """Get mapping dictionary from list of selected pairs.
 
     Args:
-    ----
-        selected_pairs : list
-            List of selected pairs of related WT based on
-            precipitation patterns over specified area and
-            correlation and RSME thresholds defined in recipe
-
-    Returns
-    -------
-        dict
-            Mapping dicitonary keys are simplified WT, values are Lamb WT
+        selected_pairs (list): List of selected weathertype pairs
+
+    Returns:
+        dict: Mapping dictionary
     """
     mapping_array = []
 
@@ -356,13 +291,9 @@ def write_mapping_dict(work_dir: str, dataset: str, mapping_dict: dict):
     """Write mapping dictionary to file.
 
     Args:
-    ----
-        work_dir : str
-            Working directory to save mapping dict
-        dataset : str
-            Name of dataset
-        mapping_dict : dict
-            Mapping dictionary in {lwt: slwt, ...} format
+        work_dir (str): Current working directory
+        dataset (str): Dataset name
+        mapping_dict (dict): Mapping dictionary in {lwt: slwt, ...} format
     """
     mapping_dict_reformat = convert_dict(mapping_dict)
 
@@ -373,19 +304,14 @@ def write_mapping_dict(work_dir: str, dataset: str, mapping_dict: dict):
 
 
 def convert_dict(dict_: dict) -> dict:
-    """Convert mapping dictionary.
+    """Convert dictionary from {lwt: slwt, ...} format to {slwt: [lwt1, lwt2], ...}.
 
-    From {lwt: slwt, ...} format to {slwt: [lwt1, lwt2], ...}.
 
     Args:
-    ----
-        dict_ : dict
-            Dict in the {lwt: slwt, ...} format
-
-    Returns
-    -------
-        dict
-            Dict in the {slwt: [lwt1, lwt2], ...} format
+        dict_ (dict): Mapping dictionary to be converted
+
+    Returns:
+        dict: Converted dictionary
     """
     new_dict = {}
     for dataset, value in dict_.items():
@@ -401,14 +327,10 @@ def reverse_convert_dict(dict_: dict) -> dict:
     From {slwt: [lwt1, lwt2], ...} format to {lwt: slwt, ...}.
 
     Args:
-    ----
-        original_dict : dict
-        Dict in the {slwt: [lwt1, lwt2], ...} format
-
-    Returns
-    -------
-        dict
-            Dict in the  format {lwt: slwt, ...}
+        original_dict (dict): Dict in the {slwt: [lwt1, lwt2], ...} format
+
+    Returns:
+        dict: Dict in the  format {lwt: slwt, ...}
     """
     new_dict = {}
     for key, value_list in dict_.items():
@@ -426,15 +348,10 @@ def write_corr_rmse_to_csv(
     """Write correlation and rsme matrix to csv files.
 
     Args:
-    ----
-        cfg : dict
-            Configuration dictionary from recipe
-        pattern_correlation_matrix : np.array
-            Correlation matrix
-        rmse_matrix : np.array
-            RSME matrix
-        dataset : str
-            Name of dataset
+        cfg (dict): Configuration dictionary from recipe
+        pattern_correlation_matrix (np.array): Correlation matrix
+        rmse_matrix (np.array): RSME matrix
+        dataset (str): Name of dataset
     """
     logger.info("Writing corr and rsme matrices for %s", dataset)
 
@@ -461,22 +378,14 @@ def run_predefined_slwt(
     """Run predefined slwt mapping.
 
     Args:
-    ----
-        work_dir : str
-            Working directory to save mapping dict
-        dataset_name : str
-            Name of dataset
-        lwt : np.array
-            lwt array
-        predefined_slwt : dict
-            Mapping dictionary in {lwt: slwt, ...} format
-
-    Returns
-    -------
-        np.array
-            slwt_era5 array
-        np.array
-            slwt_eobs array
+        work_dir (str): Working directory to save mapping dict
+        dataset_name (str): Name of dataset
+        lwt (np.array): lwt array
+        predefined_slwt (dict): Mapping dictionary in {lwt: slwt, ...} format
+
+    Returns:
+        np.array: slwt_era5 array
+        np.array: slwt_eobs array
     """
     predefined_slwt = check_mapping_dict_format(predefined_slwt)
     write_mapping_dict(work_dir, dataset_name, predefined_slwt)
@@ -493,15 +402,10 @@ def combine_wt_to_file(
     """Combine lwt and slwt arrays to one file.
 
     Args:
-    ----
-        cfg : dict
-            Configuration dictionary from recipe
-        wt_list : list
-            List of weathertype arrays
-        cube : iris.cube.Cube
-            Cube of data to keep time coordinate
-        file_name : str
-            Name of output file
+        cfg (dict): Configuration dictionary from recipe
+        wt_list (list): List of weathertype arrays
+        cube (iris.cube.Cube): Cube of data to keep time coordinate
+        file_name (str): Name of output file
     """
     lwt = wt_list[0]
     slwt_era5 = wt_list[1]
@@ -542,15 +446,10 @@ def write_lwt_to_file(
     """Write only lwt to file.
 
     Args:
-    ----
-        cfg : dict
-            Configuration dictionary from recipe
-        lwt : np.array
-            lwt array
-        cube : iris.cube.Cube
-            Cube of data to keep time coordinate
-        file_name : str
-            Name of output file
+        cfg (dict): Configuration dictionary from recipe
+        lwt (np.array): lwt array
+        cube (iris.cube.Cube): Cube of data to keep time coordinate
+        file_name (str): Name of output file
     """
     logger.info("Writing Lamb Weathertype to %s", file_name)
 
@@ -575,16 +474,11 @@ def map_lwt_to_slwt(lwt: np.array, mapping_dict: dict) -> np.array:
     """Map lwt array to slwt array.
 
     Args:
-    ----
-        lwt : np.array
-            lwt array
-        mapping_dict : dict
-            Mapping dictionary in {lwt: slwt, ...} format
-
-    Returns
-    -------
-        np.array
-            array of slwt
+        lwt (np.array): lwt array
+        mapping_dict (dict): Mapping dictionary in {lwt: slwt, ...} format
+
+    Returns:
+        np.array: array of slwt
     """
     return np.array([np.int8(mapping_dict.get(value, 0)) for value in lwt])
 
@@ -593,14 +487,10 @@ def check_mapping_dict_format(mapping_dict: dict) -> dict:
     """Check format of mapping dict and return in {lwt: slwt, ...} format.
 
     Args:
-    ----
-        mapping_dict : dict
-            mapping dict in any format
-
-    Returns
-    -------
-        dict
-            mapping dict in {lwt: slwt, ...} format
+        mapping_dict (dict): mapping dict in any format
+
+    Returns:
+        dict: mapping dict in {lwt: slwt, ...} format
     """
     if isinstance(mapping_dict.get(list(mapping_dict.keys())[0]), list):
         dict_ = reverse_convert_dict(mapping_dict)

From a13c03451bf8aa352fb285caf7647b56ad76dfc6 Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Tue, 4 Nov 2025 11:15:50 +0100
Subject: [PATCH 10/57] doc

---
 esmvaltool/diag_scripts/weathertyping/calc_utils.py | 2 +-
 esmvaltool/diag_scripts/weathertyping/wt_utils.py   | 5 +++--
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/calc_utils.py b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
index 769771ee8f..1681d396cf 100644
--- a/esmvaltool/diag_scripts/weathertyping/calc_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
@@ -896,7 +896,7 @@ def plot_means(
     data_info: dict,
     only_lwt=False,
 ):
-    """Wrapper function for plotting means, anomalies and standard deviations.
+    """Function for plotting means, anomalies and standard deviations.
 
     Args:
         cfg (dict): Configuration dictionary from recipe
diff --git a/esmvaltool/diag_scripts/weathertyping/wt_utils.py b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
index 03ad8e4279..e1749560d4 100644
--- a/esmvaltool/diag_scripts/weathertyping/wt_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
@@ -477,7 +477,7 @@ def map_lwt_to_slwt(lwt: np.array, mapping_dict: dict) -> np.array:
         lwt (np.array): lwt array
         mapping_dict (dict): Mapping dictionary in {lwt: slwt, ...} format
 
-    Returns:
+    Returns
         np.array: array of slwt
     """
     return np.array([np.int8(mapping_dict.get(value, 0)) for value in lwt])
@@ -489,7 +489,8 @@ def check_mapping_dict_format(mapping_dict: dict) -> dict:
     Args:
         mapping_dict (dict): mapping dict in any format
 
-    Returns:
+    Returns
+    --------
         dict: mapping dict in {lwt: slwt, ...} format
     """
     if isinstance(mapping_dict.get(list(mapping_dict.keys())[0]), list):

From d4a037442d09dd0fb0e258189816275b5c802ac7 Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Tue, 4 Nov 2025 11:23:38 +0100
Subject: [PATCH 11/57] doc

---
 .../diag_scripts/weathertyping/calc_utils.py  | 31 ++++++++----
 .../diag_scripts/weathertyping/plot_utils.py  |  6 ++-
 .../weathertyping/weathertyping.py            |  7 +++
 .../diag_scripts/weathertyping/wt_utils.py    | 48 +++++++++++++------
 4 files changed, 67 insertions(+), 25 deletions(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/calc_utils.py b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
index 1681d396cf..8aef30e760 100644
--- a/esmvaltool/diag_scripts/weathertyping/calc_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
@@ -54,7 +54,8 @@ def calc_slwt_obs(
         ancestors (list): List of ancestors
         timerange (str): Time range for the calculation
 
-    Returns:
+    Returns
+    -------
         np.array: Simplified Lamb Weathertypes
     """
     logger.info("Calculating simplified Lamb Weathertypes for %s", dataset)
@@ -121,7 +122,8 @@ def calc_const():
     scheme.
     Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
 
-    Returns:
+    Returns
+    -------
         tuple: The four constants needed for WT calculation.
     """
     const1 = 1 / np.cos(45 * np.pi / 180)
@@ -143,7 +145,8 @@ def calc_westerly_flow(cube: iris.cube.Cube) -> np.array:
     Args:
         cube (iris.cube.Cube): Cube of psl data.
 
-    Returns:
+    Returns
+    -------
         np.array: westerly flow
     """
     return 1 / 2 * (cube.data[:, 1, 2] + cube.data[:, 1, 4]) - 1 / 2 * (
@@ -163,7 +166,8 @@ def calc_southerly_flow(cube: iris.cube.Cube, const1: float) -> np.array:
         cube (iris.cube.Cube): cube of psl data
         const1 (float): const1
 
-    Returns:
+    Returns
+    -------
         np.array: southerly flow
     """
     return const1 * (
@@ -187,7 +191,6 @@ def calc_resultant_flow(
     Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
 
     Args:
-    ----
         westerly_flow (np.array): westerly flow
         southerly_flow (np.array): southerly flow
 
@@ -209,7 +212,6 @@ def calc_westerly_shear_velocity(
     Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
 
     Args:
-    ----
         cube (iris.cube.Cube): cube of psl data
         const2 (float): const2
         const3 (float): const3
@@ -238,7 +240,6 @@ def calc_southerly_shear_velocity(
     Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
 
     Args:
-    ----
         cube (iris.cube.Cube): cube of psl data
         const4 (float): const
 
@@ -277,6 +278,7 @@ def calc_total_shear_velocity(
         southerly_shear_velocity (np.array): southerly shear velocity
 
     Returns
+    -------
         np.array:total shear velocity
     """
     return westerly_shear_velocity + southerly_shear_velocity
@@ -294,7 +296,8 @@ def wt_algorithm(cube: iris.cube.Cube, dataset: str) -> np.array:
         cube (iris.cube.Cube): Cube of psl data
         dataset (str): Name of dataset
 
-    Returns:
+    Returns
+    -------
         np.array: Lamb weathertypes
     """
 
@@ -430,6 +433,7 @@ def calc_lwt_slwt_model(
     """Calculate Lamb as well as simplified weathertypes for model.
 
     Args:
+    -------
         cfg (dict): Configuration dictionary from recipe
         cube (iris.cube.Cube): PSL field of dataset
         data_info (dict): Dictionary with info to dataset
@@ -544,7 +548,8 @@ def process_prcp_mean(
         dataset (str): Name of dataset
         timerange (str): Time range of dataset
 
-    Returns:
+    Returns
+    -------
         list: Selected pairs of WT. This is passed to get_mapping_dict
     """
     logger.info("Calculating corr and rsme matrices for %s", dataset)
@@ -590,6 +595,7 @@ def calc_wt_means(
     """Calculate means for psl, tas or pr for weathertypes.
 
     Args:
+    -------
         cfg (dict): Configuration dictionary from recipe
         cube (iris.cube.Cube): Cube with variable data
         wt_cubes (iris.cube.CubeList): List of cubes of lwt, slwt_ERA5 and slwt_EOBS
@@ -662,7 +668,8 @@ def get_wt_array(wt_string: str, wt_cubes: iris.cube.CubeList) -> np.array:
     Raises:
         NameError: if wt_array does not exist for the given wt_string
 
-    Returns:
+    Returns
+    -------
         (np.array, np.array): weathertype array and time coordinate
     """
     if wt_string == "slwt_ERA5":
@@ -694,6 +701,7 @@ def calc_wt_anomalies(
     """Calculate anomalies for psl, tas and pr for weathertypes.
 
     Args:
+    -------
         cfg (dict): Configuration dictionary from recipe
         cube (iris.cube.Cube): Cube with variable data
         wt_cubes (iris.cube.CubeList): List of cubes of lwt, slwt_ERA5 and slwt_EOBS
@@ -766,6 +774,7 @@ def calc_wt_std(
     """Calculate standard deviation for psl, tas and pr for weathertypes.
 
     Args:
+    -------
         cfg (dict): Configuration dictionary from recipe
         cube (iris.cube.Cube): Cube with variable data
         wt_cubes (iris.cube.CubeList): List of cubes of lwt, slwt_ERA5 and slwt_EOBS
@@ -830,6 +839,7 @@ def calc_lwt_model(cfg: dict, cube: iris.cube.Cube, data_info: dict):
     """Calculate Lamb weathertypes for models.
 
     Args:
+    -------
         cfg (dict): Configuration dictionary from recipe
         cube (iris.cube.Cube): Cube with variable data
         data_info (dict): Dictionary with info to dataset
@@ -899,6 +909,7 @@ def plot_means(
     """Function for plotting means, anomalies and standard deviations.
 
     Args:
+    -------
         cfg (dict): Configuration dictionary from recipe
         preproc_var (np.array): Preprocessed variable cube
         wt_cubes (iris.cube.Cube): List of cubes of lwt, slwt_ERA5 and slwt_EOBS
diff --git a/esmvaltool/diag_scripts/weathertyping/plot_utils.py b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
index 56a496e6a0..4ddc2aa8cb 100644
--- a/esmvaltool/diag_scripts/weathertyping/plot_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
@@ -57,6 +57,7 @@ def plot_seasonal_occurrence(
     """Plot seasonal occurences of weathertypes.
 
     Args:
+    -------
         cfg (dict): Configuration dictionary from recipe
         wt_cubes (iris.cube.Cube): List of cubes of lwt, slwt_ERA5 and slwt_EOBS
         data_info (dict): Dictionary with info to dataset
@@ -157,6 +158,7 @@ def plot_maps(
     """Function to plot maps of means, std and anomalies.
 
     Args:
+    -------
         wt (np.array): WT array
         cfg (dict): Configuration dictionary from recipe
         cube (iris.cube.Cube): Data to be plotted
@@ -266,6 +268,7 @@ def plot_corr_rmse_heatmaps(
     """Plot correlation and rmse heatmaps.
 
     Args:
+    -------
         cfg (dict): Configuration dictionary from recipe
         pattern_correlation_matrix (np.array): Pattern correlation matrix
         rmse_matrix (np.array): RMSE matrix
@@ -346,7 +349,8 @@ def get_colormap(colormap_string: str) -> ListedColormap:
     Args:
         colormap_string (str): string to identify colormap
 
-    Returns:
+    Returns
+    -------
         ListedColormap: Colormap for the specified variable
     """
     misc_seq_2_disc = [
diff --git a/esmvaltool/diag_scripts/weathertyping/weathertyping.py b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
index 3d1d2a1979..49f553afaa 100644
--- a/esmvaltool/diag_scripts/weathertyping/weathertyping.py
+++ b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
@@ -40,6 +40,7 @@ def process_models_automatic_slwt(
     """Process model data for calculating Lamb and simplified weathertypes.
 
     Args:
+    -------
         cfg (dict): Nested dictionary of metadata
         dataset_vars (list): List of variable dictionaries for a specific dataset
         data_info (dict): Dictionary holding dataset information.
@@ -100,6 +101,7 @@ def process_era5_automatic_slwt(
     """Process ERA5 data for calculating Lamb and simplified weathertypes.
 
     Args:
+    -------
         data_info (dict): Dictionary holding dataset information.
         preproc_variables_dict (dict): Dictionary holding preprocessed variables for all datasets.
         cfg (dict): Nested dictionary of metadata
@@ -171,6 +173,7 @@ def run_automatic_slwt(cfg: dict):
     of the weathertypes.
 
     Args:
+    -------
         cfg (dict): Nested dictionary of metadata
     """
     preproc_variables_dict = group_metadata(
@@ -195,6 +198,7 @@ def process_era5_lwt(preproc_variables_dict, cfg, dataset_vars, data_info):
     """Process ERA5 data for calculating Lamb weathertypes.
 
     Args:
+    -------
         preproc_variables_dict (dict): Dictionary holding preprocessed variables for all datasets.
         cfg (dict): Nested dictionary of metadata
         dataset_vars (list): List of variable dictionaries for a specific dataset
@@ -249,6 +253,7 @@ def process_models_lwt(cfg: dict, dataset_vars: list, data_info: dict):
     """Process model data for calculating Lamb weathertypes.
 
     Args:
+    -------
         cfg (dict): Nested dictionary of metadata
         dataset_vars (list): List of variable dictionaries for a specific dataset
         data_info (dict): Dictionary holding dataset information.
@@ -310,6 +315,7 @@ def run_lwt(cfg: dict):
     Plot seasonal occurrence of the weathertypes.
 
     Args:
+    -------
         cfg (dict): Nested dictionary of metadata
     """
     preproc_variables_dict = group_metadata(
@@ -335,6 +341,7 @@ def run_my_diagnostic(cfg: dict):
     """Run the weathertyping diagnostic.
 
     Args:
+    -------
         cfg (dict): Nested dictionary of metadata
     """
     automatic_slwt = cfg.get("automatic_slwt")
diff --git a/esmvaltool/diag_scripts/weathertyping/wt_utils.py b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
index e1749560d4..92e3b0a67a 100644
--- a/esmvaltool/diag_scripts/weathertyping/wt_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
@@ -31,7 +31,8 @@ def get_driver(data_info: dict) -> str:
     Args:
         data_info (dict): Data information dictionary.
 
-    Returns:
+    Returns
+    -------
         str: Driver string with leading underscore or empty string.
     """
     driver = data_info.get("driver", "")
@@ -47,7 +48,8 @@ def load_wt_preprocessors(dataset: str, preproc_variables_dict: dict):
         dataset (str): Name of dataset
         preproc_variables_dict (dict): Dictionary with info on preprocessor variables
 
-    Returns:
+    Returns
+    -------
         tuple: Preprocessor cubes for weathertyping
     """
     wt_preproc = iris.load_cube(
@@ -70,7 +72,8 @@ def get_ancestors_era5_eobs(dataset: str, preproc_variables_dict: dict):
         dataset (str): Name of dataset
         preproc_variables_dict (dict): Dictionary with info on preprocessor variables
 
-    Returns:
+    Returns
+    -------
         tuple: Lists of ERA5 and E-OBS ancestors
     """
     era5_ancestors = [
@@ -91,7 +94,8 @@ def get_model_output_filepath(dataset: str, data_info: list):
         dataset (str): Name of dataset
         data_info (list): Model variables
 
-    Returns:
+    Returns
+    -------
         tuple: Output filepath and preprocessor path for future referencing.
     """
     timerange = data_info.get("timerange").replace("/", "-")
@@ -111,7 +115,8 @@ def get_preproc_lists(preproc_vars: list):
     Args:
         preproc_vars (list): List of preprocessor variables.
 
-    Returns:
+    Returns
+    -------
         tuple: Preprocessor cubes and paths.
     """
     preproc_path_psl = preproc_vars[-3].get("filename")
@@ -134,7 +139,8 @@ def get_preproc_lists_ensemble(preproc_vars: list):
     Args:
         preproc_vars (list): List of preprocessor variables.
 
-    Returns:
+    Returns
+    -------
         tuple: Preprocessor cubes and paths.
     """
     preproc_path = preproc_vars.get("filename")
@@ -153,7 +159,8 @@ def get_looping_dict(preproc_vars: list):
     Args:
         preproc_vars (list): List of preprocessor variables.
 
-    Returns:
+    Returns
+    -------
         dict: Dictionary of preprocessor cubes and paths.
     """
     preproc, preproc_path = get_preproc_lists(preproc_vars)
@@ -172,7 +179,8 @@ def load_wt_files(path: str, only_lwt=False):
         path (str): Path of wt file
         only_lwt (bool, optional): If True, only Lamb weathertypes will be loaded. Defaults to False.
 
-    Returns:
+    Returns
+    -------
         list: List of weathertype cubes.
     """
     if not only_lwt:
@@ -203,7 +211,8 @@ def get_provenance_record(
         plot_types (bool | list): Type of plot
         statistics (bool | list): Types of statistics used
 
-    Returns:
+    Returns
+    -------
         dict: Provenance record
     """
     record = {
@@ -226,6 +235,7 @@ def log_provenance(filename: str, cfg: dict, provenance_record: dict):
     """Log provenance.
 
     Args:
+    -------
         filename (str): Filename of xml file
         cfg (dict): Configuration dictionary
         provenance_record (dict): Provenance record dictionary
@@ -242,7 +252,8 @@ def turn_list_to_mapping_dict(list_: list) -> dict:
     Args:
         list_ (list): List of combined WTs
 
-    Returns:
+    Returns
+    -------
         dict: Dictionary of combined WTs
     """
     result_dict = {}
@@ -263,7 +274,8 @@ def get_mapping_dict(selected_pairs: list) -> dict:
     Args:
         selected_pairs (list): List of selected weathertype pairs
 
-    Returns:
+    Returns
+    -------
         dict: Mapping dictionary
     """
     mapping_array = []
@@ -291,6 +303,7 @@ def write_mapping_dict(work_dir: str, dataset: str, mapping_dict: dict):
     """Write mapping dictionary to file.
 
     Args:
+    -------
         work_dir (str): Current working directory
         dataset (str): Dataset name
         mapping_dict (dict): Mapping dictionary in {lwt: slwt, ...} format
@@ -310,7 +323,8 @@ def convert_dict(dict_: dict) -> dict:
     Args:
         dict_ (dict): Mapping dictionary to be converted
 
-    Returns:
+    Returns
+    -------
         dict: Converted dictionary
     """
     new_dict = {}
@@ -329,7 +343,8 @@ def reverse_convert_dict(dict_: dict) -> dict:
     Args:
         original_dict (dict): Dict in the {slwt: [lwt1, lwt2], ...} format
 
-    Returns:
+    Returns
+    -------
         dict: Dict in the  format {lwt: slwt, ...}
     """
     new_dict = {}
@@ -348,6 +363,7 @@ def write_corr_rmse_to_csv(
     """Write correlation and rsme matrix to csv files.
 
     Args:
+    -------
         cfg (dict): Configuration dictionary from recipe
         pattern_correlation_matrix (np.array): Correlation matrix
         rmse_matrix (np.array): RSME matrix
@@ -383,7 +399,8 @@ def run_predefined_slwt(
         lwt (np.array): lwt array
         predefined_slwt (dict): Mapping dictionary in {lwt: slwt, ...} format
 
-    Returns:
+    Returns
+    -------
         np.array: slwt_era5 array
         np.array: slwt_eobs array
     """
@@ -402,6 +419,7 @@ def combine_wt_to_file(
     """Combine lwt and slwt arrays to one file.
 
     Args:
+    -------
         cfg (dict): Configuration dictionary from recipe
         wt_list (list): List of weathertype arrays
         cube (iris.cube.Cube): Cube of data to keep time coordinate
@@ -446,6 +464,7 @@ def write_lwt_to_file(
     """Write only lwt to file.
 
     Args:
+    -------
         cfg (dict): Configuration dictionary from recipe
         lwt (np.array): lwt array
         cube (iris.cube.Cube): Cube of data to keep time coordinate
@@ -478,6 +497,7 @@ def map_lwt_to_slwt(lwt: np.array, mapping_dict: dict) -> np.array:
         mapping_dict (dict): Mapping dictionary in {lwt: slwt, ...} format
 
     Returns
+    --------
         np.array: array of slwt
     """
     return np.array([np.int8(mapping_dict.get(value, 0)) for value in lwt])

From 0523ccba975d1f2f6f32ff394770ab05dcc8d025 Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Fri, 28 Nov 2025 14:03:45 +0100
Subject: [PATCH 12/57] update

---
 .codacy/cli.sh                                | 149 ++++++++++++++++
 .codacy/codacy.yaml                           |   4 +
 .github/copilot-instructions.md               | 165 ++++++++++++++++++
 .../diag_scripts/weathertyping/calc_utils.py  |  19 +-
 .../diag_scripts/weathertyping/plot_utils.py  |  16 +-
 .../weathertyping/weathertyping.py            |  48 ++---
 .../diag_scripts/weathertyping/wt_utils.py    |  57 +++---
 7 files changed, 398 insertions(+), 60 deletions(-)
 create mode 100755 .codacy/cli.sh
 create mode 100644 .codacy/codacy.yaml
 create mode 100644 .github/copilot-instructions.md

diff --git a/.codacy/cli.sh b/.codacy/cli.sh
new file mode 100755
index 0000000000..d5438cdd31
--- /dev/null
+++ b/.codacy/cli.sh
@@ -0,0 +1,149 @@
+#!/usr/bin/env bash
+
+
+set -e +o pipefail
+
+# Set up paths first
+bin_name="codacy-cli-v2"
+
+# Determine OS-specific paths
+os_name=$(uname)
+arch=$(uname -m)
+
+case "$arch" in
+"x86_64")
+  arch="amd64"
+  ;;
+"x86")
+  arch="386"
+  ;;
+"aarch64"|"arm64")
+  arch="arm64"
+  ;;
+esac
+
+if [ -z "$CODACY_CLI_V2_TMP_FOLDER" ]; then
+    if [ "$(uname)" = "Linux" ]; then
+        CODACY_CLI_V2_TMP_FOLDER="$HOME/.cache/codacy/codacy-cli-v2"
+    elif [ "$(uname)" = "Darwin" ]; then
+        CODACY_CLI_V2_TMP_FOLDER="$HOME/Library/Caches/Codacy/codacy-cli-v2"
+    else
+        CODACY_CLI_V2_TMP_FOLDER=".codacy-cli-v2"
+    fi
+fi
+
+version_file="$CODACY_CLI_V2_TMP_FOLDER/version.yaml"
+
+
+get_version_from_yaml() {
+    if [ -f "$version_file" ]; then
+        local version=$(grep -o 'version: *"[^"]*"' "$version_file" | cut -d'"' -f2)
+        if [ -n "$version" ]; then
+            echo "$version"
+            return 0
+        fi
+    fi
+    return 1
+}
+
+get_latest_version() {
+    local response
+    if [ -n "$GH_TOKEN" ]; then
+        response=$(curl -Lq --header "Authorization: Bearer $GH_TOKEN" "https://api.github.com/repos/codacy/codacy-cli-v2/releases/latest" 2>/dev/null)
+    else
+        response=$(curl -Lq "https://api.github.com/repos/codacy/codacy-cli-v2/releases/latest" 2>/dev/null)
+    fi
+
+    handle_rate_limit "$response"
+    local version=$(echo "$response" | grep -m 1 tag_name | cut -d'"' -f4)
+    echo "$version"
+}
+
+handle_rate_limit() {
+    local response="$1"
+    if echo "$response" | grep -q "API rate limit exceeded"; then
+          fatal "Error: GitHub API rate limit exceeded. Please try again later"
+    fi
+}
+
+download_file() {
+    local url="$1"
+
+    echo "Downloading from URL: ${url}"
+    if command -v curl > /dev/null 2>&1; then
+        curl -# -LS "$url" -O
+    elif command -v wget > /dev/null 2>&1; then
+        wget "$url"
+    else
+        fatal "Error: Could not find curl or wget, please install one."
+    fi
+}
+
+download() {
+    local url="$1"
+    local output_folder="$2"
+
+    ( cd "$output_folder" && download_file "$url" )
+}
+
+download_cli() {
+    # OS name lower case
+    suffix=$(echo "$os_name" | tr '[:upper:]' '[:lower:]')
+
+    local bin_folder="$1"
+    local bin_path="$2"
+    local version="$3"
+
+    if [ ! -f "$bin_path" ]; then
+        echo "📥 Downloading CLI version $version..."
+
+        remote_file="codacy-cli-v2_${version}_${suffix}_${arch}.tar.gz"
+        url="https://github.com/codacy/codacy-cli-v2/releases/download/${version}/${remote_file}"
+
+        download "$url" "$bin_folder"
+        tar xzfv "${bin_folder}/${remote_file}" -C "${bin_folder}"
+    fi
+}
+
+# Warn if CODACY_CLI_V2_VERSION is set and update is requested
+if [ -n "$CODACY_CLI_V2_VERSION" ] && [ "$1" = "update" ]; then
+    echo "⚠️  Warning: Performing update with forced version $CODACY_CLI_V2_VERSION"
+    echo "    Unset CODACY_CLI_V2_VERSION to use the latest version"
+fi
+
+# Ensure version.yaml exists and is up to date
+if [ ! -f "$version_file" ] || [ "$1" = "update" ]; then
+    echo "ℹ️  Fetching latest version..."
+    version=$(get_latest_version)
+    mkdir -p "$CODACY_CLI_V2_TMP_FOLDER"
+    echo "version: \"$version\"" > "$version_file"
+fi
+
+# Set the version to use
+if [ -n "$CODACY_CLI_V2_VERSION" ]; then
+    version="$CODACY_CLI_V2_VERSION"
+else
+    version=$(get_version_from_yaml)
+fi
+
+
+# Set up version-specific paths
+bin_folder="${CODACY_CLI_V2_TMP_FOLDER}/${version}"
+
+mkdir -p "$bin_folder"
+bin_path="$bin_folder"/"$bin_name"
+
+# Download the tool if not already installed
+download_cli "$bin_folder" "$bin_path" "$version"
+chmod +x "$bin_path"
+
+run_command="$bin_path"
+if [ -z "$run_command" ]; then
+    fatal "Codacy cli v2 binary could not be found."
+fi
+
+if [ "$#" -eq 1 ] && [ "$1" = "download" ]; then
+    echo "Codacy cli v2 download succeeded"
+else
+    eval "$run_command $*"
+fi
diff --git a/.codacy/codacy.yaml b/.codacy/codacy.yaml
new file mode 100644
index 0000000000..c2cccd0da6
--- /dev/null
+++ b/.codacy/codacy.yaml
@@ -0,0 +1,4 @@
+runtimes:
+    - node@22.2.0
+tools:
+    - eslint@8.57.0
diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md
new file mode 100644
index 0000000000..80925ee934
--- /dev/null
+++ b/.github/copilot-instructions.md
@@ -0,0 +1,165 @@
+## Quick context for AI code contributors
+
+This repository contains ESMValTool — a community climate-model diagnostics and
+preprocessing framework. The goal of these notes is to give an AI agent the
+minimum, high-value knowledge to be productive immediately: how the project is
+structured, common developer workflows, and concrete examples to follow.
+
+### Quick setup (developer environment)
+
+- Prefer Conda/Mamba using the provided environment files at the repository root:
+
+```bash
+# create the dev environment (mamba recommended)
+mamba env create --name esmvaltool --file environment.yml
+conda activate esmvaltool
+# or use environment_python.yml for a lighter python-only env
+```
+
+- Docker: the repository includes `docker/Dockerfile`. Build from the repo root:
+
+```bash
+docker build -t esmvaltool:latest . -f docker/Dockerfile
+```
+
+- Installation for local development: pip install editable mode and tests extras
+
+```bash
+pip install -e .[test]
+pytest   # run tests (pytest options are configured in pyproject.toml)
+```
+
+### Where to run diagnostics / recipes
+
+- Recipes and diagnostics are described in the README and docs. Diagnostics
+  live under `esmvaltool/diag_scripts/`. Example diagnostic:
+  `esmvaltool/diag_scripts/weathertyping/weathertyping.py`.
+
+### Architecture & major components (short)
+
+- `esmvaltool/` — main package. Diagnostics and helper code live here.
+  - `diag_scripts/` — each diagnostic in its own sub-folder. Diagnostics are
+    Python scripts but may call NCL/R tools.
+  - `cmorizers/` — scripts to convert observational datasets to CMOR format.
+- `ESMValTool_stuff/`, `preval/` — user recipes, examples, and auxiliary
+  analysis code. Keep changes to these under separate branches unless they are
+  core fixes.
+- External integration: heavy reliance on `ESMValCore` (preprocessing), Iris
+  (`scitools-iris`) for cube handling, and many scientific packages (see
+  `pyproject.toml` and `environment.yml`). Changes to dataflow must respect
+  ESMValCore's preprocessing contracts.
+
+### Diagnostic implementation conventions (concrete patterns)
+
+- Entry pattern: diagnostics obtain config via the shared context manager:
+
+```python
+from esmvaltool.diag_scripts.shared import run_diagnostic
+
+if __name__ == '__main__':
+    with run_diagnostic() as config:
+        run_my_diagnostic(config)
+```
+
+Follow the `weathertyping` example: functions take the `cfg` dict returned by
+`run_diagnostic()` and extract preprocessor variables through helper functions
+in the same diagnostic package.
+
+- File I/O: diagnostics generally read/write netCDF files (Iris cubes). Use the
+  existing helper utilities in `diag_scripts/<name>/` (e.g., `wt_utils`,
+  `calc_utils`) instead of re-implementing low-level file-handling.
+
+### Testing, linting and formatting specifics
+
+- Tests: configured through `pyproject.toml`. After `pip install -e .[test]` run
+  `pytest`. The project uses 79-character line length.
+- Linting: `ruff` + `prospector` (see `pyproject.toml`). Prefer automatic fixes
+  where safe (the repo config sets `fix = true` for ruff).
+
+### Packaging and runtime notes to keep in mind
+
+- The package expects Python >= 3.11 (see `pyproject.toml`).
+- `esmvaltool/__init__.py` raises a helpful error if the package is not installed
+  editable — prefer `pip install -e .` for local imports and tests.
+- Many dependencies come from conda-forge and some (e.g., ESMPy) are not on
+  PyPI. For CI-like runs prefer the provided `environment.yml` or the Docker
+  image.
+
+### Helpful repository-specific search patterns for agents
+
+- Diagnostics: `esmvaltool/diag_scripts/**/` — look for `run_diagnostic` use.
+- Recipes/user examples: `ESMValTool_stuff/` and `ESMValTool_output/`.
+- Environment and build: `environment.yml`, `environment_python.yml`,
+  `docker/Dockerfile`, `pyproject.toml`.
+
+### What to change and what to avoid
+
+- Safe: small diagnostic improvements, refactors within a diagnostic folder,
+  tests for new behavior, documentation updates to README/docs snippets.
+- Avoid: large changes to preprocessing contracts, switching major dependency
+  versions, or edits to recipes in `ESMValTool_stuff/` that alter user data
+  provenance without discussing with maintainers.
+
+### Quick examples to cite in PRs
+
+- Point reviewers to `esmvaltool/diag_scripts/weathertyping/weathertyping.py`
+  as a model for diagnostic layout and `docker/Dockerfile` and
+  `environment.yml` for environment reproducibility.
+
+### When to ask a human
+
+- If a change affects: the public API of preprocessing (ESMValCore contracts),
+  Docker/CI pipelines, or requires new conda-build recipes. Also ask if you
+  need access to large datasets (CMIP/obs) that are not in the repo.
+
+If anything here is unclear or you'd like a shorter/longer variant, tell me
+which parts to expand and I'll iterate.
+
+
+# Copilot Agents for VSCode
+
+## Auto-Fix Codacy Minor Issues Agent
+
+This agent is designed to assist developers by automatically fixing **Codacy minor issues** in code, using GitHub Copilot suggestions within VSCode.
+
+### Overview
+
+- **Purpose:** Automatically detect and fix minor code quality issues flagged by Codacy.
+- **Scope:** Minor issues only (e.g., formatting, unused imports, variable naming, unnecessary semicolons).
+- **Toolchain:** VSCode + GitHub Copilot + Codacy CLI (optional for local linting).
+- **Trigger:** On file save or on-demand.
+
+### Setup
+
+1. **Install VSCode Extensions**
+   - [GitHub Copilot](https://marketplace.visualstudio.com/items?itemName=GitHub.copilot)
+   - [Codacy VSCode Extension](https://marketplace.visualstudio.com/items?itemName=Codacy.codacy)
+   - Optional: Linter/formatter for your language (e.g., ESLint, Prettier, Black).
+
+2. **Enable Copilot Suggestions**
+   - Go to VSCode `Settings` → `Copilot` → Enable inline suggestions.
+   - Enable `Accept Copilot suggestions on save` for automated workflow (optional, may require keybinding).
+
+3. **Codacy CLI (optional)**
+   - Install Codacy CLI:  
+     ```bash
+     npm install -g @codacy/cli
+     ```
+   - Run to check issues locally:  
+     ```bash
+     codacy analyze
+     ```
+
+### Workflow
+
+1. Open your project in VSCode.
+2. Open the file flagged with minor Codacy issues.
+3. Accept Copilot inline suggestions to automatically fix formatting, variable names, or other minor issues.
+4. Save the file to trigger any auto-formatters configured.
+5. Run Codacy analysis to ensure issues are resolved.
+
+### Example Use Cases
+
+- **Unused import:**
+  ```python
+  import os  # Minor issue: unused import
diff --git a/esmvaltool/diag_scripts/weathertyping/calc_utils.py b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
index 8aef30e760..663ddfda9e 100644
--- a/esmvaltool/diag_scripts/weathertyping/calc_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
@@ -430,7 +430,7 @@ def calc_lwt_slwt_model(
     data_info: dict,
     predefined_slwt: bool | dict,
 ):
-    """Calculate Lamb as well as simplified weathertypes for model.
+    """Calculate Lamb as well as simplified weathertypes for model and write them to file.
 
     Args:
     -------
@@ -543,6 +543,7 @@ def process_prcp_mean(
     """Return which weathertypes can be grouped together for a certain precipitation pattern.
 
     Args:
+    ----
         cfg (dict): Configuration dictionary from recipe
         data (np.array): Array of precipitation means for each WT
         dataset (str): Name of dataset
@@ -595,7 +596,7 @@ def calc_wt_means(
     """Calculate means for psl, tas or pr for weathertypes.
 
     Args:
-    -------
+    ----
         cfg (dict): Configuration dictionary from recipe
         cube (iris.cube.Cube): Cube with variable data
         wt_cubes (iris.cube.CubeList): List of cubes of lwt, slwt_ERA5 and slwt_EOBS
@@ -662,10 +663,12 @@ def get_wt_array(wt_string: str, wt_cubes: iris.cube.CubeList) -> np.array:
     """Get weathertype array and time coordinate based on wt_string.
 
     Args:
+    ----
         wt_string(str): string for weathertype selection
         wt_cubes(iris.cube.CubeList): list of weathertype cubes
 
-    Raises:
+    Raises
+    ------
         NameError: if wt_array does not exist for the given wt_string
 
     Returns
@@ -701,7 +704,7 @@ def calc_wt_anomalies(
     """Calculate anomalies for psl, tas and pr for weathertypes.
 
     Args:
-    -------
+    ----
         cfg (dict): Configuration dictionary from recipe
         cube (iris.cube.Cube): Cube with variable data
         wt_cubes (iris.cube.CubeList): List of cubes of lwt, slwt_ERA5 and slwt_EOBS
@@ -774,7 +777,7 @@ def calc_wt_std(
     """Calculate standard deviation for psl, tas and pr for weathertypes.
 
     Args:
-    -------
+    ----
         cfg (dict): Configuration dictionary from recipe
         cube (iris.cube.Cube): Cube with variable data
         wt_cubes (iris.cube.CubeList): List of cubes of lwt, slwt_ERA5 and slwt_EOBS
@@ -839,7 +842,7 @@ def calc_lwt_model(cfg: dict, cube: iris.cube.Cube, data_info: dict):
     """Calculate Lamb weathertypes for models.
 
     Args:
-    -------
+    ----
         cfg (dict): Configuration dictionary from recipe
         cube (iris.cube.Cube): Cube with variable data
         data_info (dict): Dictionary with info to dataset
@@ -906,10 +909,10 @@ def plot_means(
     data_info: dict,
     only_lwt=False,
 ):
-    """Function for plotting means, anomalies and standard deviations.
+    """Plotting means, anomalies and standard deviations.
 
     Args:
-    -------
+    ----
         cfg (dict): Configuration dictionary from recipe
         preproc_var (np.array): Preprocessed variable cube
         wt_cubes (iris.cube.Cube): List of cubes of lwt, slwt_ERA5 and slwt_EOBS
diff --git a/esmvaltool/diag_scripts/weathertyping/plot_utils.py b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
index 4ddc2aa8cb..dfd1d6676f 100644
--- a/esmvaltool/diag_scripts/weathertyping/plot_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
@@ -37,9 +37,11 @@ def generate_grayscale_hex_values(x):
     """Generate grayscale values for plotting seasonal occurences.
 
     Args:
-        x (list): Amount of weathertypes.
+    ----
+        x (int): Amount of weathertypes.
 
-    Returns:
+    Returns
+    -------
         list: List of grayscale hex values
     """
     grayscale_values = np.linspace(0, 1, x)
@@ -57,7 +59,7 @@ def plot_seasonal_occurrence(
     """Plot seasonal occurences of weathertypes.
 
     Args:
-    -------
+    ----
         cfg (dict): Configuration dictionary from recipe
         wt_cubes (iris.cube.Cube): List of cubes of lwt, slwt_ERA5 and slwt_EOBS
         data_info (dict): Dictionary with info to dataset
@@ -155,10 +157,10 @@ def plot_seasonal_occurrence(
 def plot_maps(
     wt: np.array, cfg: dict, cube: iris.cube.Cube, data_info: dict, mode: str
 ):
-    """Function to plot maps of means, std and anomalies.
+    """Plotting maps of means, std and anomalies.
 
     Args:
-    -------
+    ----
         wt (np.array): WT array
         cfg (dict): Configuration dictionary from recipe
         cube (iris.cube.Cube): Data to be plotted
@@ -268,7 +270,7 @@ def plot_corr_rmse_heatmaps(
     """Plot correlation and rmse heatmaps.
 
     Args:
-    -------
+    ----
         cfg (dict): Configuration dictionary from recipe
         pattern_correlation_matrix (np.array): Pattern correlation matrix
         rmse_matrix (np.array): RMSE matrix
@@ -344,7 +346,7 @@ def plot_corr_rmse_heatmaps(
 
 
 def get_colormap(colormap_string: str) -> ListedColormap:
-    """Get colormaps for plottings
+    """Get colormaps for plottings.
 
     Args:
         colormap_string (str): string to identify colormap
diff --git a/esmvaltool/diag_scripts/weathertyping/weathertyping.py b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
index 49f553afaa..e3c2c04c6a 100644
--- a/esmvaltool/diag_scripts/weathertyping/weathertyping.py
+++ b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
@@ -40,7 +40,7 @@ def process_models_automatic_slwt(
     """Process model data for calculating Lamb and simplified weathertypes.
 
     Args:
-    -------
+    ----
         cfg (dict): Nested dictionary of metadata
         dataset_vars (list): List of variable dictionaries for a specific dataset
         data_info (dict): Dictionary holding dataset information.
@@ -67,23 +67,22 @@ def process_models_automatic_slwt(
                 cfg, wt_preproc, data_info, cfg.get("predefined_slwt")
             )
 
-            # load wt files
-            wt_cubes = load_wt_files(
-                f"{cfg.get('work_dir')}/{output_file_path}"
-                f"/{data_info['dataset']}"
-                f"{data_info['driver']}_"
-                f"{data_info['ensemble']}_"
-                f"{data_info['timerange']}.nc"
-            )
-
-            var_dict = {
-                f"{ensemble_var.get('short_name')}": get_preproc_lists_ensemble(
-                    ensemble_var
-                )
-            }
-
             # plot means
             if cfg.get("plotting", False):
+                # load wt files
+                wt_cubes = load_wt_files(
+                    f"{cfg.get('work_dir')}/{output_file_path}"
+                    f"/{data_info['dataset']}"
+                    f"{data_info['driver']}_"
+                    f"{data_info['ensemble']}_"
+                    f"{data_info['timerange']}.nc"
+                )
+
+                var_dict = {
+                    f"{ensemble_var.get('short_name')}": get_preproc_lists_ensemble(
+                        ensemble_var
+                    )
+                }
                 for var_name, var_data in var_dict.items():
                     data_info["var"] = var_name
                     data_info["preproc_path"] = var_data[1]
@@ -101,7 +100,7 @@ def process_era5_automatic_slwt(
     """Process ERA5 data for calculating Lamb and simplified weathertypes.
 
     Args:
-    -------
+    ----
         data_info (dict): Dictionary holding dataset information.
         preproc_variables_dict (dict): Dictionary holding preprocessed variables for all datasets.
         cfg (dict): Nested dictionary of metadata
@@ -154,10 +153,10 @@ def process_era5_automatic_slwt(
         f"{cfg.get('work_dir')}/{data_info['dataset']}.nc"
     )
 
-    var_dict = get_looping_dict(
-        dataset_vars
-    )  # dataset_vars is list of variables for dataset dataset_name
     if cfg.get("plotting", False):
+        var_dict = get_looping_dict(
+            dataset_vars
+        )  # dataset_vars is list of variables for dataset dataset_name
         # plot means
         for var_name, var_data in var_dict.items():
             data_info["var"] = var_name
@@ -235,11 +234,10 @@ def process_era5_lwt(preproc_variables_dict, cfg, dataset_vars, data_info):
         f"{cfg.get('work_dir')}/{data_info['dataset']}.nc", only_lwt=True
     )
 
-    var_dict = get_looping_dict(
-        dataset_vars
-    )  # dataset_vars is list of variables for dataset dataset_name
-
     if cfg.get("plotting", False):
+        var_dict = get_looping_dict(
+            dataset_vars
+        )  # dataset_vars is list of variables for dataset dataset_name
         # plot means
         for var_name, var_data in var_dict.items():
             data_info["var"] = var_name
@@ -347,6 +345,8 @@ def run_my_diagnostic(cfg: dict):
     automatic_slwt = cfg.get("automatic_slwt")
 
     # check if user wants to calculate simplified weathertypes automatically
+    # for that, automatic_slwt must be true
+    # additionally, if a predefined_slwt is given, those will be used
     if automatic_slwt:
         run_automatic_slwt(cfg)
     # if automatic_slwt is false, and predefined_slwt is false,
diff --git a/esmvaltool/diag_scripts/weathertyping/wt_utils.py b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
index 92e3b0a67a..8f76ea9d01 100644
--- a/esmvaltool/diag_scripts/weathertyping/wt_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
@@ -36,8 +36,6 @@ def get_driver(data_info: dict) -> str:
         str: Driver string with leading underscore or empty string.
     """
     driver = data_info.get("driver", "")
-    if driver != "":
-        driver = f"_{driver}"
     return driver
 
 
@@ -55,12 +53,19 @@ def load_wt_preprocessors(dataset: str, preproc_variables_dict: dict):
     wt_preproc = iris.load_cube(
         preproc_variables_dict.get(dataset)[0].get("filename")
     )
-    wt_preproc_prcp = iris.load_cube(
-        preproc_variables_dict.get(dataset)[1].get("filename")
-    )
-    wt_preproc_prcp_eobs = iris.load_cube(
-        preproc_variables_dict.get("E-OBS")[0].get("filename")
-    )
+    # try and except block to catch missing precipitation preprocessor
+    # if it is not supplied, predefined_slwt has to be given by user
+    try:
+        wt_preproc_prcp = iris.load_cube(
+            preproc_variables_dict.get(dataset)[1].get("filename")
+        )
+        wt_preproc_prcp_eobs = iris.load_cube(
+            preproc_variables_dict.get("E-OBS")[0].get("filename")
+        )
+    except (IndexError, KeyError, TypeError):
+        print("ERA5 precipitation preprocessor not found for automatic slwt.")
+        wt_preproc_prcp = None
+        wt_preproc_prcp_eobs = None
 
     return wt_preproc, wt_preproc_prcp, wt_preproc_prcp_eobs
 
@@ -78,12 +83,19 @@ def get_ancestors_era5_eobs(dataset: str, preproc_variables_dict: dict):
     """
     era5_ancestors = [
         preproc_variables_dict.get(dataset)[0].get("filename"),
-        preproc_variables_dict.get(dataset)[1].get("filename"),
-    ]
-    eobs_ancestors = [
-        preproc_variables_dict.get(dataset)[0].get("filename"),
-        preproc_variables_dict.get("E-OBS")[0].get("filename"),
     ]
+    try:
+        era5_ancestors.append(
+            preproc_variables_dict.get(dataset)[1].get("filename")
+        )
+        eobs_ancestors = [
+            preproc_variables_dict.get(dataset)[0].get("filename"),
+            preproc_variables_dict.get("E-OBS")[0].get("filename"),
+        ]
+    except (IndexError, KeyError, TypeError):
+        print("No ancestors for ERA5 and E-OBS precipitation preprocessor.")
+        eobs_ancestors = []
+
     return era5_ancestors, eobs_ancestors
 
 
@@ -235,7 +247,7 @@ def log_provenance(filename: str, cfg: dict, provenance_record: dict):
     """Log provenance.
 
     Args:
-    -------
+    ----
         filename (str): Filename of xml file
         cfg (dict): Configuration dictionary
         provenance_record (dict): Provenance record dictionary
@@ -303,7 +315,7 @@ def write_mapping_dict(work_dir: str, dataset: str, mapping_dict: dict):
     """Write mapping dictionary to file.
 
     Args:
-    -------
+    ----
         work_dir (str): Current working directory
         dataset (str): Dataset name
         mapping_dict (dict): Mapping dictionary in {lwt: slwt, ...} format
@@ -319,8 +331,8 @@ def write_mapping_dict(work_dir: str, dataset: str, mapping_dict: dict):
 def convert_dict(dict_: dict) -> dict:
     """Convert dictionary from {lwt: slwt, ...} format to {slwt: [lwt1, lwt2], ...}.
 
-
     Args:
+    ----
         dict_ (dict): Mapping dictionary to be converted
 
     Returns
@@ -363,7 +375,7 @@ def write_corr_rmse_to_csv(
     """Write correlation and rsme matrix to csv files.
 
     Args:
-    -------
+    ----
         cfg (dict): Configuration dictionary from recipe
         pattern_correlation_matrix (np.array): Correlation matrix
         rmse_matrix (np.array): RSME matrix
@@ -394,6 +406,7 @@ def run_predefined_slwt(
     """Run predefined slwt mapping.
 
     Args:
+    ----
         work_dir (str): Working directory to save mapping dict
         dataset_name (str): Name of dataset
         lwt (np.array): lwt array
@@ -419,7 +432,7 @@ def combine_wt_to_file(
     """Combine lwt and slwt arrays to one file.
 
     Args:
-    -------
+    ----
         cfg (dict): Configuration dictionary from recipe
         wt_list (list): List of weathertype arrays
         cube (iris.cube.Cube): Cube of data to keep time coordinate
@@ -464,7 +477,7 @@ def write_lwt_to_file(
     """Write only lwt to file.
 
     Args:
-    -------
+    ----
         cfg (dict): Configuration dictionary from recipe
         lwt (np.array): lwt array
         cube (iris.cube.Cube): Cube of data to keep time coordinate
@@ -493,11 +506,12 @@ def map_lwt_to_slwt(lwt: np.array, mapping_dict: dict) -> np.array:
     """Map lwt array to slwt array.
 
     Args:
+    ----
         lwt (np.array): lwt array
         mapping_dict (dict): Mapping dictionary in {lwt: slwt, ...} format
 
     Returns
-    --------
+    -------
         np.array: array of slwt
     """
     return np.array([np.int8(mapping_dict.get(value, 0)) for value in lwt])
@@ -507,10 +521,11 @@ def check_mapping_dict_format(mapping_dict: dict) -> dict:
     """Check format of mapping dict and return in {lwt: slwt, ...} format.
 
     Args:
+    ----
         mapping_dict (dict): mapping dict in any format
 
     Returns
-    --------
+    -------
         dict: mapping dict in {lwt: slwt, ...} format
     """
     if isinstance(mapping_dict.get(list(mapping_dict.keys())[0]), list):

From b6ac979cae4118df34b2b1458461208e0f2bafcf Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Fri, 28 Nov 2025 14:30:35 +0100
Subject: [PATCH 13/57] update

---
 .../diag_scripts/weathertyping/calc_utils.py   | 18 ++++++++++++------
 .../diag_scripts/weathertyping/plot_utils.py   | 10 ++++++----
 .../weathertyping/weathertyping.py             |  6 ++++--
 .../diag_scripts/weathertyping/wt_utils.py     |  7 +++----
 4 files changed, 25 insertions(+), 16 deletions(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/calc_utils.py b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
index 663ddfda9e..d82f0528df 100644
--- a/esmvaltool/diag_scripts/weathertyping/calc_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
@@ -102,7 +102,11 @@ def calc_slwt_obs(
     write_mapping_dict(cfg.get("work_dir"), dataset, mapping_dict)
 
     provenance_record = get_provenance_record(
-        "Lamb Weathertypes", ancestors, ["Lamb Weathertypes"], False, False
+        "Lamb Weathertypes",
+        ancestors,
+        ["Lamb Weathertypes"],
+        plot_types=False,
+        statistics=False,
     )
 
     log_provenance(
@@ -230,7 +234,8 @@ def calc_westerly_shear_velocity(
 
 
 def calc_southerly_shear_velocity(
-    cube: iris.cube.Cube, const4: float
+    cube: iris.cube.Cube,
+    const4: float,
 ) -> np.array:
     """Calculate southerly shear velocity.
 
@@ -264,7 +269,8 @@ def calc_southerly_shear_velocity(
 
 
 def calc_total_shear_velocity(
-    westerly_shear_velocity: np.array, southerly_shear_velocity: np.array
+    westerly_shear_velocity: np.array,
+    southerly_shear_velocity: np.array,
 ) -> np.array:
     """Calculate total shear velocity.
 
@@ -357,7 +363,7 @@ def wt_algorithm(cube: iris.cube.Cube, dataset: str) -> np.array:
 
         # Lamb pure directional type
         if abs(z_i) < total_flow[i]:
-            if 337.5 <= direction or direction < 22.5:
+            if direction >= 337.5 or direction < 22.5:
                 weathertypes[i] = 1
             elif 22.5 <= direction < 67.5:
                 weathertypes[i] = 2
@@ -383,7 +389,7 @@ def wt_algorithm(cube: iris.cube.Cube, dataset: str) -> np.array:
         # Lambs’s synoptic/direction hybrid types
         elif total_flow[i] < abs(z_i) < (2 * total_flow[i]):
             if z_i > 0:
-                if 337.5 <= direction or direction < 22.5:
+                if direction >= 337.5 or direction < 22.5:
                     weathertypes[i] = 11
                 elif 22.5 <= direction < 67.5:
                     weathertypes[i] = 12
@@ -401,7 +407,7 @@ def wt_algorithm(cube: iris.cube.Cube, dataset: str) -> np.array:
                     weathertypes[i] = 18
 
             elif z_i < 0:
-                if 337.5 <= direction or direction < 22.5:
+                if direction >= 337.5 or direction < 22.5:
                     weathertypes[i] = 19
                 elif 22.5 <= direction < 67.5:
                     weathertypes[i] = 20
diff --git a/esmvaltool/diag_scripts/weathertyping/plot_utils.py b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
index dfd1d6676f..761b8c4c7d 100644
--- a/esmvaltool/diag_scripts/weathertyping/plot_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
@@ -54,7 +54,9 @@ def generate_grayscale_hex_values(x):
 
 
 def plot_seasonal_occurrence(
-    cfg: dict, wt_cubes: iris.cube.Cube, data_info: dict
+    cfg: dict,
+    wt_cubes: iris.cube.Cube,
+    data_info: dict,
 ):
     """Plot seasonal occurences of weathertypes.
 
@@ -97,7 +99,7 @@ def plot_seasonal_occurrence(
             ).data
             unique, counts = np.unique(array, return_counts=True)
             month_dict[month] = dict(
-                zip(unique, counts / sum(counts), strict=False)
+                zip(unique, counts / sum(counts), strict=False),
             )
         relative_occurrences[cube.long_name] = month_dict
 
@@ -145,11 +147,11 @@ def plot_seasonal_occurrence(
 
         plt.savefig(
             f"{output_path}/{driver}{data_info.get('dataset')}_{data_info.get('ensemble', '')}_"
-            f"{wt_string}_rel_occurrence_{data_info.get('timerange')}.png"
+            f"{wt_string}_rel_occurrence_{data_info.get('timerange')}.png",
         )
         plt.savefig(
             f"{output_path}/{driver}{data_info.get('dataset')}_{data_info.get('ensemble', '')}_"
-            f"{wt_string}_rel_occurrence_{data_info.get('timerange')}.pdf"
+            f"{wt_string}_rel_occurrence_{data_info.get('timerange')}.pdf",
         )
         plt.close()
 
diff --git a/esmvaltool/diag_scripts/weathertyping/weathertyping.py b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
index e3c2c04c6a..c0c5807da5 100644
--- a/esmvaltool/diag_scripts/weathertyping/weathertyping.py
+++ b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
@@ -231,7 +231,8 @@ def process_era5_lwt(preproc_variables_dict, cfg, dataset_vars, data_info):
 
     # load wt files
     wt_cubes = load_wt_files(
-        f"{cfg.get('work_dir')}/{data_info['dataset']}.nc", only_lwt=True
+        f"{cfg.get('work_dir')}/{data_info['dataset']}.nc",
+        only_lwt=True,
     )
 
     if cfg.get("plotting", False):
@@ -267,7 +268,8 @@ def process_models_lwt(cfg: dict, dataset_vars: list, data_info: dict):
             wt_preproc = iris.load_cube(dataset_vars[0].get("filename"))
 
             output_file_path, preproc_path = get_model_output_filepath(
-                data_info["dataset"], dataset_vars
+                data_info["dataset"],
+                dataset_vars,
             )
 
             data_info["output_file_path"] = output_file_path
diff --git a/esmvaltool/diag_scripts/weathertyping/wt_utils.py b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
index 8f76ea9d01..230d9d330a 100644
--- a/esmvaltool/diag_scripts/weathertyping/wt_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
@@ -3,8 +3,8 @@
 # operating system manipulations (e.g. path constructions)
 import json
 import logging
-import os
 import warnings
+from pathlib import Path
 
 # to manipulate iris cubes
 import iris
@@ -19,7 +19,7 @@
 
 iris.FUTURE.datum_support = True
 
-logger = logging.getLogger(os.path.basename(__file__))
+logger = logging.getLogger(Path(__file__).name)
 
 # Ignoring a warning that is produced when selecting timesteps of a weathertype
 warnings.filterwarnings("ignore", ".*Collapsing a non-contiguous coordinate*")
@@ -306,9 +306,8 @@ def find_intersection(m_list: list) -> list:
         return m_list
 
     merged_tuples = find_intersection(s)
-    mapping_dict = turn_list_to_mapping_dict(merged_tuples)
 
-    return mapping_dict
+    return turn_list_to_mapping_dict(merged_tuples)
 
 
 def write_mapping_dict(work_dir: str, dataset: str, mapping_dict: dict):

From 236cd79e275fe51ec5471a24222d2d8335e43c3d Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Fri, 28 Nov 2025 14:42:47 +0100
Subject: [PATCH 14/57] minor codacy issues

---
 .../diag_scripts/weathertyping/calc_utils.py  | 15 +++--
 .../diag_scripts/weathertyping/plot_utils.py  | 57 +++++++++++--------
 .../weathertyping/weathertyping.py            | 49 ++++++++++------
 .../diag_scripts/weathertyping/wt_utils.py    | 48 +++++++++-------
 4 files changed, 104 insertions(+), 65 deletions(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/calc_utils.py b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
index d82f0528df..51caa1adc8 100644
--- a/esmvaltool/diag_scripts/weathertyping/calc_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
@@ -571,7 +571,7 @@ def process_prcp_mean(
             rmse_matrix[i][j] = rmse(data[i], data[j])
             rmse_matrix[j][i] = rmse_matrix[i][j]
             if pattern_correlation_matrix[i][j] >= cfg.get(
-                "correlation_threshold"
+                "correlation_threshold",
             ) and rmse_matrix[i][j] <= cfg.get("rmse_threshold"):
                 selected_pairs.append(
                     (
@@ -583,11 +583,18 @@ def process_prcp_mean(
 
     # write matrices to csv
     write_corr_rmse_to_csv(
-        cfg, pattern_correlation_matrix, rmse_matrix, dataset
+        cfg,
+        pattern_correlation_matrix,
+        rmse_matrix,
+        dataset,
     )
     # plot heatmaps for matrices
     plot_corr_rmse_heatmaps(
-        cfg, pattern_correlation_matrix, rmse_matrix, dataset, timerange
+        cfg,
+        pattern_correlation_matrix,
+        rmse_matrix,
+        dataset,
+        timerange,
     )
 
     return selected_pairs
@@ -695,7 +702,7 @@ def get_wt_array(wt_string: str, wt_cubes: iris.cube.CubeList) -> np.array:
         wt_array = lwt_cube.data[:]
     else:
         raise NameError(
-            "wt_array does not exist for calc_wt_means, calc_wt_anomalies or calc_wt_std."
+            "wt_array does not exist for calc_wt_means, calc_wt_anomalies or calc_wt_std.",
         )
 
     return wt_array, tcoord
diff --git a/esmvaltool/diag_scripts/weathertyping/plot_utils.py b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
index 761b8c4c7d..a2f51e274c 100644
--- a/esmvaltool/diag_scripts/weathertyping/plot_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
@@ -2,8 +2,8 @@
 
 # operating system manipulations (e.g. path constructions)
 import logging
-import os
 import warnings
+from pathlib import Path
 
 # plotting imports
 import cartopy.crs as ccrs
@@ -27,7 +27,7 @@
 
 iris.FUTURE.datum_support = True
 
-logger = logging.getLogger(os.path.basename(__file__))
+logger = logging.getLogger(Path(__file__).name)
 
 # Ignoring a warning that is produced when selecting timesteps of a weathertype
 warnings.filterwarnings("ignore", ".*Collapsing a non-contiguous coordinate*")
@@ -45,13 +45,12 @@ def generate_grayscale_hex_values(x):
         list: List of grayscale hex values
     """
     grayscale_values = np.linspace(0, 1, x)
-    grayscale_hex = [
+
+    return [
         f"#{int(value * 255):02x}{int(value * 255):02x}{int(value * 255):02x}"
         for value in grayscale_values
     ]
 
-    return grayscale_hex
-
 
 def plot_seasonal_occurrence(
     cfg: dict,
@@ -70,8 +69,8 @@ def plot_seasonal_occurrence(
 
     output_path = f"{cfg['plot_dir']}/seasonal_occurrence"
 
-    if not os.path.exists(f"{output_path}"):
-        os.makedirs(f"{output_path}")
+    if not Path.exists(f"{output_path}"):
+        Path.mkdir(f"{output_path}", parents=True, exist_ok=True)
 
     month_list = [
         "Jan",
@@ -95,7 +94,7 @@ def plot_seasonal_occurrence(
         month_dict = {}
         for month in range(1, 13):
             array = cube.extract(
-                iris.Constraint(time=iris.time.PartialDateTime(month=month))
+                iris.Constraint(time=iris.time.PartialDateTime(month=month)),
             ).data
             unique, counts = np.unique(array, return_counts=True)
             month_dict[month] = dict(
@@ -110,7 +109,7 @@ def plot_seasonal_occurrence(
         )
         wt_stack = np.zeros((wt_numbers, 12))
         for month, month_value in relative_occurrences.get(wt_string).items():
-            for wt in month_value.keys():
+            for wt in month_value:
                 wt_stack[np.int8(wt - 1), month - 1] = month_value.get(wt)
 
         y = np.vstack(list(wt_stack))
@@ -120,11 +119,13 @@ def plot_seasonal_occurrence(
 
         ax_.set_title(
             f"Seasonal occurence of {wt_string}, \
-            {data_info.get('dataset')}, {data_info.get('timerange')}"
+            {data_info.get('dataset')}, {data_info.get('timerange')}",
         )
 
         ax_.stackplot(
-            month_list, y, colors=generate_grayscale_hex_values(wt_numbers)
+            month_list,
+            y,
+            colors=generate_grayscale_hex_values(wt_numbers),
         )
 
         ax_.legend(
@@ -133,7 +134,7 @@ def plot_seasonal_occurrence(
             fancybox=True,
             shadow=True,
             ncol=9,
-            labels=tuple(f"WT {i + 1}" for i in range(0, wt_numbers)),
+            labels=tuple(f"WT {i + 1}" for i in range(wt_numbers)),
         )
 
         ax_.set(
@@ -157,7 +158,11 @@ def plot_seasonal_occurrence(
 
 
 def plot_maps(
-    wt: np.array, cfg: dict, cube: iris.cube.Cube, data_info: dict, mode: str
+    wt: np.array,
+    cfg: dict,
+    cube: iris.cube.Cube,
+    data_info: dict,
+    mode: str,
 ):
     """Plotting maps of means, std and anomalies.
 
@@ -186,7 +191,7 @@ def plot_maps(
         psl_cmap = get_colormap("psl")
         plt.title(
             f"{data_info.get('dataset')} {data_info.get('ensemble', '')}, {data_info.get('var')} {mode}\n"
-            + f"{data_info.get('timerange')}, wt: {wt}"
+            + f"{data_info.get('timerange')}, wt: {wt}",
         )
         unit = "[hPa]"
         im = iplt.contourf(cube / 100, cmap=psl_cmap)
@@ -199,13 +204,13 @@ def plot_maps(
             unit = "[m]"
             plt.title(
                 f"{data_info.get('dataset')} {data_info.get('ensemble', '')}, total {var_name} {mode}\n"
-                + f"{data_info.get('timerange')}, wt: {wt}"
+                + f"{data_info.get('timerange')}, wt: {wt}",
             )
         else:
             unit = "[kg m-2 s-1]"
             plt.title(
                 f"{data_info.get('dataset')} {data_info.get('ensemble', '')}, {var_name} flux {mode}\n"
-                + f"{data_info.get('timerange')}, wt: {wt}"
+                + f"{data_info.get('timerange')}, wt: {wt}",
             )
         im = iplt.contourf(cube, cmap=prcp_cmap)
         cb = plt.colorbar(im)
@@ -216,7 +221,7 @@ def plot_maps(
         unit = "[K]"
         plt.title(
             f"{data_info.get('dataset')} {data_info.get('ensemble', '')}, 1000 hPa {var_name} {mode}\n"
-            + f"{data_info.get('timerange')}, wt: {wt}"
+            + f"{data_info.get('timerange')}, wt: {wt}",
         )
         im = iplt.contourf(cube, cmap=temp_cmap)
         cb = plt.colorbar(im)
@@ -248,16 +253,18 @@ def plot_maps(
     ax.coastlines()
     ax.add_feature(cfeature.BORDERS, linestyle=":")
 
-    if not os.path.exists(f"{cfg.get('plot_dir')}/{mode}"):
-        os.makedirs(f"{cfg.get('plot_dir')}/{mode}")
+    if not Path(f"{cfg.get('plot_dir')}/{mode}").exists():
+        Path.mkdir(
+            f"{cfg.get('plot_dir')}/{mode}", parents=True, exist_ok=True
+        )
 
     plt.savefig(
         f"{cfg.get('plot_dir')}/{mode}/{data_info.get('wt_string')}_{wt}{get_driver(data_info)}_{data_info.get('dataset')}_{data_info.get('ensemble', '')}"
-        f"_{var_name}_{mode}_{data_info.get('timerange')}.png"
+        f"_{var_name}_{mode}_{data_info.get('timerange')}.png",
     )
     plt.savefig(
         f"{cfg.get('plot_dir')}/{mode}/{data_info.get('wt_string')}_{wt}{get_driver(data_info)}_{data_info.get('dataset')}_{data_info.get('ensemble', '')}_"
-        f"{var_name}_{mode}_{data_info.get('timerange')}.pdf"
+        f"{var_name}_{mode}_{data_info.get('timerange')}.pdf",
     )
     plt.close()
 
@@ -281,8 +288,8 @@ def plot_corr_rmse_heatmaps(
     """
     output_path = f"{cfg.get('plot_dir')}/heatmaps"
 
-    if not os.path.exists(f"{output_path}"):
-        os.makedirs(f"{output_path}")
+    if not Path(f"{output_path}").exists():
+        Path.mkdir(f"{output_path}", parents=True, exist_ok=True)
 
     labels = np.arange(1, 28)
 
@@ -313,10 +320,10 @@ def plot_corr_rmse_heatmaps(
         ax.set_ylabel("lwt", fontsize=8)
         plt.tight_layout()
         plt.savefig(
-            f"{output_path}/correlation_matrix_{dataset}_{timerange}.png"
+            f"{output_path}/correlation_matrix_{dataset}_{timerange}.png",
         )
         plt.savefig(
-            f"{output_path}/correlation_matrix_{dataset}_{timerange}.pdf"
+            f"{output_path}/correlation_matrix_{dataset}_{timerange}.pdf",
         )
         plt.close()
 
diff --git a/esmvaltool/diag_scripts/weathertyping/weathertyping.py b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
index c0c5807da5..5512599acb 100644
--- a/esmvaltool/diag_scripts/weathertyping/weathertyping.py
+++ b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
@@ -35,7 +35,9 @@
 
 
 def process_models_automatic_slwt(
-    cfg: dict, dataset_vars: list, data_info: dict
+    cfg: dict,
+    dataset_vars: list,
+    data_info: dict,
 ):
     """Process model data for calculating Lamb and simplified weathertypes.
 
@@ -56,7 +58,8 @@ def process_models_automatic_slwt(
             wt_preproc = iris.load_cube(ensemble_var.get("filename"))
 
             output_file_path, preproc_path = get_model_output_filepath(
-                data_info["dataset"], ensemble_var
+                data_info["dataset"],
+                ensemble_var,
             )
 
             data_info["output_file_path"] = output_file_path
@@ -64,7 +67,10 @@ def process_models_automatic_slwt(
 
             # calculate weathertypes
             calc_lwt_slwt_model(
-                cfg, wt_preproc, data_info, cfg.get("predefined_slwt")
+                cfg,
+                wt_preproc,
+                data_info,
+                cfg.get("predefined_slwt"),
             )
 
             # plot means
@@ -75,12 +81,12 @@ def process_models_automatic_slwt(
                     f"/{data_info['dataset']}"
                     f"{data_info['driver']}_"
                     f"{data_info['ensemble']}_"
-                    f"{data_info['timerange']}.nc"
+                    f"{data_info['timerange']}.nc",
                 )
 
                 var_dict = {
                     f"{ensemble_var.get('short_name')}": get_preproc_lists_ensemble(
-                        ensemble_var
+                        ensemble_var,
                     )
                 }
                 for var_name, var_data in var_dict.items():
@@ -107,14 +113,16 @@ def process_era5_automatic_slwt(
         dataset_vars (list): List of variable dictionaries for a specific dataset
     """
     wt_preproc, wt_preproc_prcp, wt_preproc_prcp_eobs = load_wt_preprocessors(
-        data_info["dataset"], preproc_variables_dict
+        data_info["dataset"],
+        preproc_variables_dict,
     )
 
     # calculate lwt
     lwt = wt_algorithm(wt_preproc, data_info["dataset"])
 
     era5_ancestors, eobs_ancestors = get_ancestors_era5_eobs(
-        data_info["dataset"], preproc_variables_dict
+        data_info["dataset"],
+        preproc_variables_dict,
     )
 
     # calculate simplified lwt based on precipitation
@@ -150,12 +158,12 @@ def process_era5_automatic_slwt(
 
     # load weathertype files as cubes
     wt_cubes = load_wt_files(
-        f"{cfg.get('work_dir')}/{data_info['dataset']}.nc"
+        f"{cfg.get('work_dir')}/{data_info['dataset']}.nc",
     )
 
     if cfg.get("plotting", False):
         var_dict = get_looping_dict(
-            dataset_vars
+            dataset_vars,
         )  # dataset_vars is list of variables for dataset dataset_name
         # plot means
         for var_name, var_data in var_dict.items():
@@ -172,11 +180,13 @@ def run_automatic_slwt(cfg: dict):
     of the weathertypes.
 
     Args:
-    -------
+    ----
         cfg (dict): Nested dictionary of metadata
     """
+
     preproc_variables_dict = group_metadata(
-        cfg.get("input_data").values(), "dataset"
+        cfg.get("input_data").values(),
+        "dataset",
     )
     for dataset_name, dataset_vars in preproc_variables_dict.items():
         data_info = {
@@ -185,7 +195,10 @@ def run_automatic_slwt(cfg: dict):
         }
         if dataset_name == "ERA5":
             process_era5_automatic_slwt(
-                data_info, preproc_variables_dict, cfg, dataset_vars
+                data_info,
+                preproc_variables_dict,
+                cfg,
+                dataset_vars,
             )
         else:
             if data_info["dataset"] == "E-OBS":
@@ -220,8 +233,8 @@ def process_era5_lwt(preproc_variables_dict, cfg, dataset_vars, data_info):
         "Lamb Weathertypes",
         ancestors,
         ["Lamb Weathertypes"],
-        False,
-        False,
+        plot_types=False,
+        statistics=False,
     )
 
     log_provenance(f"{cfg.get('work_dir')}/lwt_era5", cfg, provenance_record)
@@ -319,7 +332,8 @@ def run_lwt(cfg: dict):
         cfg (dict): Nested dictionary of metadata
     """
     preproc_variables_dict = group_metadata(
-        cfg.get("input_data").values(), "dataset"
+        cfg.get("input_data").values(),
+        "dataset",
     )
     for dataset_name, dataset_vars in preproc_variables_dict.items():
         data_info = {
@@ -329,7 +343,10 @@ def run_lwt(cfg: dict):
 
         if dataset_name == "ERA5":
             process_era5_lwt(
-                preproc_variables_dict, cfg, dataset_vars, data_info
+                preproc_variables_dict,
+                cfg,
+                dataset_vars,
+                data_info,
             )
         else:
             if data_info["dataset"] == "E-OBS":
diff --git a/esmvaltool/diag_scripts/weathertyping/wt_utils.py b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
index 230d9d330a..5ced586bb2 100644
--- a/esmvaltool/diag_scripts/weathertyping/wt_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
@@ -51,16 +51,16 @@ def load_wt_preprocessors(dataset: str, preproc_variables_dict: dict):
         tuple: Preprocessor cubes for weathertyping
     """
     wt_preproc = iris.load_cube(
-        preproc_variables_dict.get(dataset)[0].get("filename")
+        preproc_variables_dict.get(dataset)[0].get("filename"),
     )
     # try and except block to catch missing precipitation preprocessor
     # if it is not supplied, predefined_slwt has to be given by user
     try:
         wt_preproc_prcp = iris.load_cube(
-            preproc_variables_dict.get(dataset)[1].get("filename")
+            preproc_variables_dict.get(dataset)[1].get("filename"),
         )
         wt_preproc_prcp_eobs = iris.load_cube(
-            preproc_variables_dict.get("E-OBS")[0].get("filename")
+            preproc_variables_dict.get("E-OBS")[0].get("filename"),
         )
     except (IndexError, KeyError, TypeError):
         print("ERA5 precipitation preprocessor not found for automatic slwt.")
@@ -86,7 +86,7 @@ def get_ancestors_era5_eobs(dataset: str, preproc_variables_dict: dict):
     ]
     try:
         era5_ancestors.append(
-            preproc_variables_dict.get(dataset)[1].get("filename")
+            preproc_variables_dict.get(dataset)[1].get("filename"),
         )
         eobs_ancestors = [
             preproc_variables_dict.get(dataset)[0].get("filename"),
@@ -159,9 +159,6 @@ def get_preproc_lists_ensemble(preproc_vars: list):
 
     preproc = iris.load_cube(preproc_vars.get("filename"))
 
-    # preproc_list = [mean_preproc]
-    # preproc_path_list = [preproc_path_psl]
-
     return preproc, preproc_path
 
 
@@ -176,12 +173,12 @@ def get_looping_dict(preproc_vars: list):
         dict: Dictionary of preprocessor cubes and paths.
     """
     preproc, preproc_path = get_preproc_lists(preproc_vars)
-    dict_ = {
+
+    return {
         "psl": [preproc[0], preproc_path[0]],
         "pr": [preproc[1], preproc_path[1]],
         "tas": [preproc[2], preproc_path[2]],
     }
-    return dict_
 
 
 def load_wt_files(path: str, only_lwt=False):
@@ -293,7 +290,7 @@ def get_mapping_dict(selected_pairs: list) -> dict:
     mapping_array = []
 
     for elem in selected_pairs:
-        mapping_array.append(elem[0])
+        (mapping_array.append(elem[0]),)
 
     s = [set(i) for i in mapping_array if i]
 
@@ -322,7 +319,9 @@ def write_mapping_dict(work_dir: str, dataset: str, mapping_dict: dict):
     mapping_dict_reformat = convert_dict(mapping_dict)
 
     with open(
-        f"{work_dir}/wt_mapping_dict_{dataset}.json", "w", encoding="utf-8"
+        f"{work_dir}/wt_mapping_dict_{dataset}.json",
+        "w",
+        encoding="utf-8",
     ) as file:
         json.dump(mapping_dict_reformat, file)
 
@@ -388,19 +387,24 @@ def write_corr_rmse_to_csv(
     df_corr.index = range(1, len(df_corr) + 1)
     df_corr.columns = range(1, len(df_corr.columns) + 1)
     df_corr.to_csv(
-        f"{work_dir}/correlation_matrix_{dataset}.csv", index_label="Index"
+        f"{work_dir}/correlation_matrix_{dataset}.csv",
+        index_label="Index",
     )
 
     df_rmse = pd.DataFrame(rmse_matrix)
     df_rmse.index = range(1, len(df_rmse) + 1)
     df_rmse.columns = range(1, len(df_rmse.columns) + 1)
     df_rmse.to_csv(
-        f"{work_dir}/rmse_matrix_{dataset}.csv", index_label="Index"
+        f"{work_dir}/rmse_matrix_{dataset}.csv",
+        index_label="Index",
     )
 
 
 def run_predefined_slwt(
-    work_dir: str, dataset_name: str, lwt: np.array, predefined_slwt: dict
+    work_dir: str,
+    dataset_name: str,
+    lwt: np.array,
+    predefined_slwt: dict,
 ):
     """Run predefined slwt mapping.
 
@@ -426,7 +430,10 @@ def run_predefined_slwt(
 
 
 def combine_wt_to_file(
-    cfg: dict, wt_list: list, cube: iris.cube.Cube, file_name: str
+    cfg: dict,
+    wt_list: list,
+    cube: iris.cube.Cube,
+    file_name: str,
 ):
     """Combine lwt and slwt arrays to one file.
 
@@ -471,7 +478,10 @@ def combine_wt_to_file(
 
 
 def write_lwt_to_file(
-    cfg: dict, lwt: np.array, cube: iris.cube.Cube, file_name: str
+    cfg: dict,
+    lwt: np.array,
+    cube: iris.cube.Cube,
+    file_name: str,
 ):
     """Write only lwt to file.
 
@@ -528,8 +538,6 @@ def check_mapping_dict_format(mapping_dict: dict) -> dict:
         dict: mapping dict in {lwt: slwt, ...} format
     """
     if isinstance(mapping_dict.get(list(mapping_dict.keys())[0]), list):
-        dict_ = reverse_convert_dict(mapping_dict)
+        return reverse_convert_dict(mapping_dict)
     else:
-        dict_ = mapping_dict
-
-    return dict_
+        return mapping_dict

From 793c016e99e0de97e90ba956a3853c9a3313c810 Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Fri, 28 Nov 2025 14:53:22 +0100
Subject: [PATCH 15/57] minor codacy issues

---
 .../diag_scripts/weathertyping/calc_utils.py  | 73 ++++++++++++-------
 .../weathertyping/weathertyping.py            |  1 +
 2 files changed, 47 insertions(+), 27 deletions(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/calc_utils.py b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
index 51caa1adc8..24cab66f8d 100644
--- a/esmvaltool/diag_scripts/weathertyping/calc_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
@@ -3,8 +3,8 @@
 # operating system manipulations (e.g. path constructions)
 import json
 import logging
-import os
 import warnings
+from pathlib import Path
 
 # to manipulate iris cubes
 import iris
@@ -30,7 +30,7 @@
 
 iris.FUTURE.datum_support = True
 
-logger = logging.getLogger(os.path.basename(__file__))
+logger = logging.getLogger(Path(__file__).name)
 
 # Ignoring a warning that is produced when selecting timesteps of a weathertype
 warnings.filterwarnings("ignore", ".*Collapsing a non-contiguous coordinate*")
@@ -47,6 +47,7 @@ def calc_slwt_obs(
     """Calculate simplified weathertypes for observational data.
 
     Args:
+    ----
         cfg (dict): Configuration dictionary from recipe
         lwt (np.array): Array of Lamb WT
         cube (iris.cube.Cube): Cube of psl data
@@ -57,6 +58,7 @@ def calc_slwt_obs(
     Returns
     -------
         np.array: Simplified Lamb Weathertypes
+
     """
     logger.info("Calculating simplified Lamb Weathertypes for %s", dataset)
 
@@ -79,7 +81,7 @@ def calc_slwt_obs(
             extracted_cube = cube[target_indices]
         else:
             extracted_cube = cube.extract(
-                iris.Constraint(time=lambda t, d=dates: t.point in d[0])
+                iris.Constraint(time=lambda t, d=dates: t.point in d[0]),
             )
         wt_cube_mean = extracted_cube.collapsed("time", iris.analysis.MEAN)
         wt_data_prcp.append(wt_cube_mean.data.compressed())
@@ -185,7 +187,8 @@ def calc_southerly_flow(cube: iris.cube.Cube, const1: float) -> np.array:
 
 
 def calc_resultant_flow(
-    westerly_flow: np.array, southerly_flow: np.array
+    westerly_flow: np.array,
+    southerly_flow: np.array,
 ) -> np.array:
     """Calculate the resultant flow.
 
@@ -206,7 +209,9 @@ def calc_resultant_flow(
 
 
 def calc_westerly_shear_velocity(
-    cube: iris.cube.Cube, const2: float, const3: float
+    cube: iris.cube.Cube,
+    const2: float,
+    const3: float,
 ) -> np.array:
     """Calculate westerly shear velocity.
 
@@ -340,13 +345,16 @@ def wt_algorithm(cube: iris.cube.Cube, dataset: str) -> np.array:
     total_flow = calc_resultant_flow(westerly_flow, southerly_flow)
     # westerly shear vorticity
     westerly_shear_velocity = calc_westerly_shear_velocity(
-        cube, const2, const3
+        cube,
+        const2,
+        const3,
     )
     # southerly shear vorticity
     southerly_shear_velocity = calc_southerly_shear_velocity(cube, const4)
     # total shear vorticity
     total_shear_velocity = calc_total_shear_velocity(
-        westerly_shear_velocity, southerly_shear_velocity
+        westerly_shear_velocity,
+        southerly_shear_velocity,
     )
 
     weathertypes = np.zeros(len(total_shear_velocity))
@@ -379,14 +387,14 @@ def wt_algorithm(cube: iris.cube.Cube, dataset: str) -> np.array:
                 weathertypes[i] = 7
             elif 292.5 <= direction < 337.5:
                 weathertypes[i] = 8
-        # Lamb’s pure cyclonic and anticyclonic type
+        # Lamb pure cyclonic and anticyclonic type
         elif (2 * total_flow[i]) < abs(z_i):
             if z_i > 0:
                 weathertypes[i] = 9
 
             elif z_i < 0:
                 weathertypes[i] = 10
-        # Lambs’s synoptic/direction hybrid types
+        # Lamb synoptic/direction hybrid types
         elif total_flow[i] < abs(z_i) < (2 * total_flow[i]):
             if z_i > 0:
                 if direction >= 337.5 or direction < 22.5:
@@ -423,7 +431,7 @@ def wt_algorithm(cube: iris.cube.Cube, dataset: str) -> np.array:
                     weathertypes[i] = 25
                 elif 292.5 <= direction < 337.5:
                     weathertypes[i] = 26
-        # light indeterminate flow, corresponding to Lamb’s unclassified type U
+        # light indeterminate flow, corresponding to Lamb unclassified type U
         elif abs(z_i) < 6 and total_flow[i] < 6:
             weathertypes[i] = 27
 
@@ -449,11 +457,13 @@ def calc_lwt_slwt_model(
     """
     driver = get_driver(data_info)
 
-    if not os.path.exists(
-        f"{cfg.get('work_dir')}/{data_info.get('output_file_path')}"
-    ):
-        os.makedirs(
-            f"{cfg.get('work_dir')}/{data_info.get('output_file_path')}"
+    if not Path(
+        f"{cfg.get('work_dir')}/{data_info.get('output_file_path')}",
+    ).exists():
+        Path.mkdir(
+            f"{cfg.get('work_dir')}/{data_info.get('output_file_path')}",
+            parents=True,
+            exist_ok=True,
         )
 
     lwt = wt_algorithm(cube, data_info.get("dataset"))
@@ -525,7 +535,11 @@ def calc_lwt_slwt_model(
         f"{cfg.get('work_dir')}/wt_mapping_dict_E-OBS.json",
     ]
     provenance_record = get_provenance_record(
-        "Lamb Weathertypes", ancestors, ["Lamb Weathertypes"], False, False
+        "Lamb Weathertypes",
+        ancestors,
+        ["Lamb Weathertypes"],
+        plot_types=False,
+        statistics=False,
     )
 
     log_provenance(
@@ -643,7 +657,7 @@ def calc_wt_means(
             tcoord.units.num2date(tcoord.points[i]) for i in target_indices
         ]
         extracted_cube = cube.extract(
-            iris.Constraint(time=lambda t, d=dates: t.point in d[0])
+            iris.Constraint(time=lambda t, d=dates: t.point in d[0]),
         )
         wt_cube_mean = extracted_cube.collapsed("time", iris.analysis.MEAN)
         plot_maps(wt, cfg, wt_cube_mean, data_info, "mean")
@@ -701,8 +715,9 @@ def get_wt_array(wt_string: str, wt_cubes: iris.cube.CubeList) -> np.array:
         tcoord = lwt_cube.coord("time")
         wt_array = lwt_cube.data[:]
     else:
+        error_str = "wt_array does not exist for calc_wt_means, calc_wt_anomalies or calc_wt_std."
         raise NameError(
-            "wt_array does not exist for calc_wt_means, calc_wt_anomalies or calc_wt_std.",
+            error_str,
         )
 
     return wt_array, tcoord
@@ -749,7 +764,7 @@ def calc_wt_anomalies(
             tcoord.units.num2date(tcoord.points[i]) for i in target_indices
         ]
         extracted_cube = cube.extract(
-            iris.Constraint(time=lambda t, d=dates: t.point in d[0])
+            iris.Constraint(time=lambda t, d=dates: t.point in d[0]),
         )
         wt_cube_mean = extracted_cube.collapsed("time", iris.analysis.MEAN)
         plot_maps(
@@ -822,7 +837,7 @@ def calc_wt_std(
             tcoord.units.num2date(tcoord.points[i]) for i in target_indices
         ]
         extracted_cube = cube.extract(
-            iris.Constraint(time=lambda t, d=dates: t.point in d[0])
+            iris.Constraint(time=lambda t, d=dates: t.point in d[0]),
         )
         wt_cube_std = extracted_cube.collapsed("time", iris.analysis.STD_DEV)
         plot_maps(wt, cfg, wt_cube_std, data_info, "stddev")
@@ -862,12 +877,12 @@ def calc_lwt_model(cfg: dict, cube: iris.cube.Cube, data_info: dict):
     """
     driver = get_driver(data_info)
 
-    if not os.path.exists(
-        f"{cfg.get('work_dir')}/{data_info.get('output_file_path')}"
-    ):
-        os.makedirs(
-            f"{cfg.get('work_dir')}/{data_info.get('output_file_path')}"
-        )
+    if not Path(
+        f"{cfg.get('work_dir')}/{data_info.get('output_file_path')}",
+    ).exists():
+        Path(
+            f"{cfg.get('work_dir')}/{data_info.get('output_file_path')}",
+        ).mkdir(parents=True, exist_ok=True)
 
     wt = wt_algorithm(cube, data_info.get("dataset"))
 
@@ -905,7 +920,11 @@ def calc_lwt_model(cfg: dict, cube: iris.cube.Cube, data_info: dict):
         f"{cfg.get('work_dir')}/wt_mapping_dict_E-OBS.json",
     ]
     provenance_record = get_provenance_record(
-        "Lamb Weathertypes", ancestors, ["Lamb Weathertypes"], False, False
+        "Lamb Weathertypes",
+        ancestors,
+        ["Lamb Weathertypes"],
+        plot_types=False,
+        statistics=False,
     )
 
     log_provenance(
diff --git a/esmvaltool/diag_scripts/weathertyping/weathertyping.py b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
index 5512599acb..5e08bdbe49 100644
--- a/esmvaltool/diag_scripts/weathertyping/weathertyping.py
+++ b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
@@ -182,6 +182,7 @@ def run_automatic_slwt(cfg: dict):
     Args:
     ----
         cfg (dict): Nested dictionary of metadata
+
     """
 
     preproc_variables_dict = group_metadata(

From d1fb4f8a5dc6090438bc24221ca1622f75bf8da1 Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Fri, 28 Nov 2025 15:03:05 +0100
Subject: [PATCH 16/57] minor codacy issues

---
 .../diag_scripts/weathertyping/plot_utils.py       | 14 ++++++++------
 .../diag_scripts/weathertyping/weathertyping.py    |  2 +-
 esmvaltool/diag_scripts/weathertyping/wt_utils.py  |  6 ++----
 3 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/plot_utils.py b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
index a2f51e274c..346f213378 100644
--- a/esmvaltool/diag_scripts/weathertyping/plot_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
@@ -164,7 +164,7 @@ def plot_maps(
     data_info: dict,
     mode: str,
 ):
-    """Plotting maps of means, std and anomalies.
+    """Plot maps of means, std and anomalies.
 
     Args:
     ----
@@ -191,7 +191,7 @@ def plot_maps(
         psl_cmap = get_colormap("psl")
         plt.title(
             f"{data_info.get('dataset')} {data_info.get('ensemble', '')}, {data_info.get('var')} {mode}\n"
-            + f"{data_info.get('timerange')}, wt: {wt}",
+            f"{data_info.get('timerange')}, wt: {wt}",
         )
         unit = "[hPa]"
         im = iplt.contourf(cube / 100, cmap=psl_cmap)
@@ -204,13 +204,13 @@ def plot_maps(
             unit = "[m]"
             plt.title(
                 f"{data_info.get('dataset')} {data_info.get('ensemble', '')}, total {var_name} {mode}\n"
-                + f"{data_info.get('timerange')}, wt: {wt}",
+                f"{data_info.get('timerange')}, wt: {wt}",
             )
         else:
             unit = "[kg m-2 s-1]"
             plt.title(
                 f"{data_info.get('dataset')} {data_info.get('ensemble', '')}, {var_name} flux {mode}\n"
-                + f"{data_info.get('timerange')}, wt: {wt}",
+                f"{data_info.get('timerange')}, wt: {wt}",
             )
         im = iplt.contourf(cube, cmap=prcp_cmap)
         cb = plt.colorbar(im)
@@ -221,7 +221,7 @@ def plot_maps(
         unit = "[K]"
         plt.title(
             f"{data_info.get('dataset')} {data_info.get('ensemble', '')}, 1000 hPa {var_name} {mode}\n"
-            + f"{data_info.get('timerange')}, wt: {wt}",
+            f"{data_info.get('timerange')}, wt: {wt}",
         )
         im = iplt.contourf(cube, cmap=temp_cmap)
         cb = plt.colorbar(im)
@@ -255,7 +255,9 @@ def plot_maps(
 
     if not Path(f"{cfg.get('plot_dir')}/{mode}").exists():
         Path.mkdir(
-            f"{cfg.get('plot_dir')}/{mode}", parents=True, exist_ok=True
+            f"{cfg.get('plot_dir')}/{mode}",
+            parents=True,
+            exist_ok=True,
         )
 
     plt.savefig(
diff --git a/esmvaltool/diag_scripts/weathertyping/weathertyping.py b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
index 5e08bdbe49..5ceb819212 100644
--- a/esmvaltool/diag_scripts/weathertyping/weathertyping.py
+++ b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
@@ -87,7 +87,7 @@ def process_models_automatic_slwt(
                 var_dict = {
                     f"{ensemble_var.get('short_name')}": get_preproc_lists_ensemble(
                         ensemble_var,
-                    )
+                    ),
                 }
                 for var_name, var_data in var_dict.items():
                     data_info["var"] = var_name
diff --git a/esmvaltool/diag_scripts/weathertyping/wt_utils.py b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
index 5ced586bb2..1ad000d861 100644
--- a/esmvaltool/diag_scripts/weathertyping/wt_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
@@ -35,8 +35,7 @@ def get_driver(data_info: dict) -> str:
     -------
         str: Driver string with leading underscore or empty string.
     """
-    driver = data_info.get("driver", "")
-    return driver
+    return data_info.get("driver", "")
 
 
 def load_wt_preprocessors(dataset: str, preproc_variables_dict: dict):
@@ -539,5 +538,4 @@ def check_mapping_dict_format(mapping_dict: dict) -> dict:
     """
     if isinstance(mapping_dict.get(list(mapping_dict.keys())[0]), list):
         return reverse_convert_dict(mapping_dict)
-    else:
-        return mapping_dict
+    return mapping_dict

From 216ed8f2f0faee271deb0eae6c08fc19e5290506 Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Fri, 28 Nov 2025 15:11:50 +0100
Subject: [PATCH 17/57] minor codacy issues

---
 esmvaltool/diag_scripts/weathertyping/calc_utils.py  | 12 ++++++------
 esmvaltool/diag_scripts/weathertyping/plot_utils.py  |  7 +++----
 .../diag_scripts/weathertyping/weathertyping.py      | 10 +++++-----
 3 files changed, 14 insertions(+), 15 deletions(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/calc_utils.py b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
index 24cab66f8d..a994cd06df 100644
--- a/esmvaltool/diag_scripts/weathertyping/calc_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
@@ -304,6 +304,7 @@ def wt_algorithm(cube: iris.cube.Cube, dataset: str) -> np.array:
     Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
 
     Args:
+    ----
         cube (iris.cube.Cube): Cube of psl data
         dataset (str): Name of dataset
 
@@ -311,7 +312,6 @@ def wt_algorithm(cube: iris.cube.Cube, dataset: str) -> np.array:
     -------
         np.array: Lamb weathertypes
     """
-
     # lats and lons corresponding to datapoints
     # 55, 5 -> 1
     # 55, 15 -> 2
@@ -332,7 +332,6 @@ def wt_algorithm(cube: iris.cube.Cube, dataset: str) -> np.array:
 
     # lons: -5, 0, 5, 10, 15, 20, 25
     # lats: 35, 40, 45, 50, 55
-
     logger.info("Calculating Lamb Weathertypes for %s", dataset)
 
     const1, const2, const3, const4 = calc_const()
@@ -460,8 +459,9 @@ def calc_lwt_slwt_model(
     if not Path(
         f"{cfg.get('work_dir')}/{data_info.get('output_file_path')}",
     ).exists():
-        Path.mkdir(
-            f"{cfg.get('work_dir')}/{data_info.get('output_file_path')}",
+        Path(
+            f"{cfg.get('work_dir')}/{data_info.get('output_file_path')}"
+        ).mkdir(
             parents=True,
             exist_ok=True,
         )
@@ -592,7 +592,7 @@ def process_prcp_mean(
                         (i + 1, j + 1),
                         pattern_correlation_matrix[i][j],
                         rmse_matrix[i][j],
-                    )
+                    ),
                 )
 
     # write matrices to csv
@@ -941,7 +941,7 @@ def plot_means(
     data_info: dict,
     only_lwt=False,
 ):
-    """Plotting means, anomalies and standard deviations.
+    """Plot means, anomalies and standard deviations.
 
     Args:
     ----
diff --git a/esmvaltool/diag_scripts/weathertyping/plot_utils.py b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
index 346f213378..c2fadd2a61 100644
--- a/esmvaltool/diag_scripts/weathertyping/plot_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
@@ -70,7 +70,7 @@ def plot_seasonal_occurrence(
     output_path = f"{cfg['plot_dir']}/seasonal_occurrence"
 
     if not Path.exists(f"{output_path}"):
-        Path.mkdir(f"{output_path}", parents=True, exist_ok=True)
+        Path(f"{output_path}").mkdir(parents=True, exist_ok=True)
 
     month_list = [
         "Jan",
@@ -254,8 +254,7 @@ def plot_maps(
     ax.add_feature(cfeature.BORDERS, linestyle=":")
 
     if not Path(f"{cfg.get('plot_dir')}/{mode}").exists():
-        Path.mkdir(
-            f"{cfg.get('plot_dir')}/{mode}",
+        Path(f"{cfg.get('plot_dir')}/{mode}").mkdir(
             parents=True,
             exist_ok=True,
         )
@@ -291,7 +290,7 @@ def plot_corr_rmse_heatmaps(
     output_path = f"{cfg.get('plot_dir')}/heatmaps"
 
     if not Path(f"{output_path}").exists():
-        Path.mkdir(f"{output_path}", parents=True, exist_ok=True)
+        Path(f"{output_path}").mkdir(parents=True, exist_ok=True)
 
     labels = np.arange(1, 28)
 
diff --git a/esmvaltool/diag_scripts/weathertyping/weathertyping.py b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
index 5ceb819212..8524b19975 100644
--- a/esmvaltool/diag_scripts/weathertyping/weathertyping.py
+++ b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
@@ -182,9 +182,7 @@ def run_automatic_slwt(cfg: dict):
     Args:
     ----
         cfg (dict): Nested dictionary of metadata
-
     """
-
     preproc_variables_dict = group_metadata(
         cfg.get("input_data").values(),
         "dataset",
@@ -218,14 +216,16 @@ def process_era5_lwt(preproc_variables_dict, cfg, dataset_vars, data_info):
         data_info (dict): Dictionary holding dataset information.
     """
     wt_preproc, _, _ = load_wt_preprocessors(
-        data_info["dataset"], preproc_variables_dict
+        data_info["dataset"],
+        preproc_variables_dict,
     )
 
     # calculate lwt
     lwt = wt_algorithm(wt_preproc, data_info["dataset"])
 
     era5_ancestors, eobs_ancestors = get_ancestors_era5_eobs(
-        data_info["dataset"], preproc_variables_dict
+        data_info["dataset"],
+        preproc_variables_dict,
     )
 
     ancestors = [era5_ancestors, eobs_ancestors]
@@ -251,7 +251,7 @@ def process_era5_lwt(preproc_variables_dict, cfg, dataset_vars, data_info):
 
     if cfg.get("plotting", False):
         var_dict = get_looping_dict(
-            dataset_vars
+            dataset_vars,
         )  # dataset_vars is list of variables for dataset dataset_name
         # plot means
         for var_name, var_data in var_dict.items():

From 04dd54f7de088fd042ff126fba01bb2a6025fd36 Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Mon, 1 Dec 2025 13:30:39 +0100
Subject: [PATCH 18/57] some medium issues

---
 .gitignore                                    |  4 ++++
 .../diag_scripts/weathertyping/__init__.py    |  1 +
 .../diag_scripts/weathertyping/calc_utils.py  | 10 ++------
 .../weathertyping/weathertyping.py            |  8 +++----
 .../diag_scripts/weathertyping/wt_utils.py    | 24 ++++++++++---------
 5 files changed, 23 insertions(+), 24 deletions(-)
 create mode 100644 esmvaltool/diag_scripts/weathertyping/__init__.py

diff --git a/.gitignore b/.gitignore
index 7c783dce5a..abec495ffb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -112,3 +112,7 @@ esmvaltool/utils/testing/regression/.service/
 
 # Temporary imagehash files
 imagehashes_*.yml
+
+
+#Ignore vscode AI rules
+.github/instructions/codacy.instructions.md
diff --git a/esmvaltool/diag_scripts/weathertyping/__init__.py b/esmvaltool/diag_scripts/weathertyping/__init__.py
new file mode 100644
index 0000000000..e31afa296a
--- /dev/null
+++ b/esmvaltool/diag_scripts/weathertyping/__init__.py
@@ -0,0 +1 @@
+"""Initialize the ESMValTool weathertyping package."""
diff --git a/esmvaltool/diag_scripts/weathertyping/calc_utils.py b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
index a994cd06df..3221e8c12f 100644
--- a/esmvaltool/diag_scripts/weathertyping/calc_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
@@ -107,8 +107,6 @@ def calc_slwt_obs(
         "Lamb Weathertypes",
         ancestors,
         ["Lamb Weathertypes"],
-        plot_types=False,
-        statistics=False,
     )
 
     log_provenance(
@@ -441,7 +439,7 @@ def calc_lwt_slwt_model(
     cfg: dict,
     cube: iris.cube.Cube,
     data_info: dict,
-    predefined_slwt: bool | dict,
+    predefined_slwt: dict = None,
 ):
     """Calculate Lamb as well as simplified weathertypes for model and write them to file.
 
@@ -450,7 +448,7 @@ def calc_lwt_slwt_model(
         cfg (dict): Configuration dictionary from recipe
         cube (iris.cube.Cube): PSL field of dataset
         data_info (dict): Dictionary with info to dataset
-        predefined_slwt (bool | dict): If False, automatic_slwt will be used.
+        predefined_slwt (dict | None): If None, automatic_slwt will be used.
             If dict, this mapping dict will be used.
             (see recipe option predefined_slwt)
     """
@@ -538,8 +536,6 @@ def calc_lwt_slwt_model(
         "Lamb Weathertypes",
         ancestors,
         ["Lamb Weathertypes"],
-        plot_types=False,
-        statistics=False,
     )
 
     log_provenance(
@@ -923,8 +919,6 @@ def calc_lwt_model(cfg: dict, cube: iris.cube.Cube, data_info: dict):
         "Lamb Weathertypes",
         ancestors,
         ["Lamb Weathertypes"],
-        plot_types=False,
-        statistics=False,
     )
 
     log_provenance(
diff --git a/esmvaltool/diag_scripts/weathertyping/weathertyping.py b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
index 8524b19975..2ef83b3137 100644
--- a/esmvaltool/diag_scripts/weathertyping/weathertyping.py
+++ b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
@@ -234,8 +234,6 @@ def process_era5_lwt(preproc_variables_dict, cfg, dataset_vars, data_info):
         "Lamb Weathertypes",
         ancestors,
         ["Lamb Weathertypes"],
-        plot_types=False,
-        statistics=False,
     )
 
     log_provenance(f"{cfg.get('work_dir')}/lwt_era5", cfg, provenance_record)
@@ -246,7 +244,7 @@ def process_era5_lwt(preproc_variables_dict, cfg, dataset_vars, data_info):
     # load wt files
     wt_cubes = load_wt_files(
         f"{cfg.get('work_dir')}/{data_info['dataset']}.nc",
-        only_lwt=True,
+        mode="lamb",
     )
 
     if cfg.get("plotting", False):
@@ -299,11 +297,11 @@ def process_models_lwt(cfg: dict, dataset_vars: list, data_info: dict):
                 f"{data_info['driver']}"
                 f"_{data_info['ensemble']}_"
                 f"{data_info['timerange']}.nc",
-                only_lwt=True,
+                mode="lamb",
             )
 
             var_dict = get_looping_dict(
-                dataset_vars
+                dataset_vars,
             )  # dataset_vars is list of variables for dataset_name
 
             if cfg.get("plotting", False):
diff --git a/esmvaltool/diag_scripts/weathertyping/wt_utils.py b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
index 1ad000d861..eb4e982bbb 100644
--- a/esmvaltool/diag_scripts/weathertyping/wt_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
@@ -180,25 +180,27 @@ def get_looping_dict(preproc_vars: list):
     }
 
 
-def load_wt_files(path: str, only_lwt=False):
+def load_wt_files(path: str, mode="simplified"):
     """Load wt files.
 
     Args:
         path (str): Path of wt file
-        only_lwt (bool, optional): If True, only Lamb weathertypes will be loaded. Defaults to False.
+        mode (str, optional): Type of weathertype to load. Defaults to "simplified".
 
     Returns
     -------
         list: List of weathertype cubes.
     """
-    if not only_lwt:
+    if mode == "simplified":
         lwt_cube = iris.load_cube(path, "lwt")
         slwt_era5_cube = iris.load_cube(path, "slwt_era5")
         slwt_eobs_cube = iris.load_cube(path, "slwt_eobs")
         wt_cubes = [lwt_cube, slwt_era5_cube, slwt_eobs_cube]
-    else:
+    elif mode == "lamb":
         lwt_cube = iris.load_cube(path, "lwt")
         wt_cubes = [lwt_cube]
+    else:
+        raise ValueError("Mode not recognized. Use 'simplified' or 'lamb'.")
 
     return wt_cubes
 
@@ -207,8 +209,8 @@ def get_provenance_record(
     caption: str,
     ancestors: list,
     long_names: list,
-    plot_types: bool | list,
-    statistics: bool | list,
+    plot_types: list = None,
+    statistics: list = None,
 ) -> dict:
     """Get provenance record.
 
@@ -232,9 +234,9 @@ def get_provenance_record(
         "long_names": long_names,
         "ancestors": ancestors,
     }
-    if plot_types is not False:
+    if plot_types:
         record["plot_types"] = plot_types
-    if statistics is not False:
+    if statistics:
         record["statistics"] = statistics
     return record
 
@@ -317,11 +319,11 @@ def write_mapping_dict(work_dir: str, dataset: str, mapping_dict: dict):
     """
     mapping_dict_reformat = convert_dict(mapping_dict)
 
-    with open(
+    with Path(
         f"{work_dir}/wt_mapping_dict_{dataset}.json",
         "w",
         encoding="utf-8",
-    ) as file:
+    ).open("w", encoding="utf-8") as file:
         json.dump(mapping_dict_reformat, file)
 
 
@@ -536,6 +538,6 @@ def check_mapping_dict_format(mapping_dict: dict) -> dict:
     -------
         dict: mapping dict in {lwt: slwt, ...} format
     """
-    if isinstance(mapping_dict.get(list(mapping_dict.keys())[0]), list):
+    if isinstance(mapping_dict[next(iter(mapping_dict))], list):
         return reverse_convert_dict(mapping_dict)
     return mapping_dict

From 3b71b3790966d17d96313bfb8889cfbd77af784b Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Mon, 1 Dec 2025 13:40:43 +0100
Subject: [PATCH 19/57] some medium issues

---
 esmvaltool/diag_scripts/weathertyping/calc_utils.py |  4 ++--
 .../diag_scripts/weathertyping/weathertyping.py     |  4 ++--
 esmvaltool/diag_scripts/weathertyping/wt_utils.py   | 13 +++++++------
 3 files changed, 11 insertions(+), 10 deletions(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/calc_utils.py b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
index 3221e8c12f..a4dd0293a2 100644
--- a/esmvaltool/diag_scripts/weathertyping/calc_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
@@ -439,7 +439,7 @@ def calc_lwt_slwt_model(
     cfg: dict,
     cube: iris.cube.Cube,
     data_info: dict,
-    predefined_slwt: dict = None,
+    predefined_slwt: dict | None = None,
 ):
     """Calculate Lamb as well as simplified weathertypes for model and write them to file.
 
@@ -458,7 +458,7 @@ def calc_lwt_slwt_model(
         f"{cfg.get('work_dir')}/{data_info.get('output_file_path')}",
     ).exists():
         Path(
-            f"{cfg.get('work_dir')}/{data_info.get('output_file_path')}"
+            f"{cfg.get('work_dir')}/{data_info.get('output_file_path')}",
         ).mkdir(
             parents=True,
             exist_ok=True,
diff --git a/esmvaltool/diag_scripts/weathertyping/weathertyping.py b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
index 2ef83b3137..f991cb04ff 100644
--- a/esmvaltool/diag_scripts/weathertyping/weathertyping.py
+++ b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
@@ -175,8 +175,8 @@ def process_era5_automatic_slwt(
 
 
 def run_automatic_slwt(cfg: dict):
-    """Run the automated calculation for simplified weathertypes \
-    and write to file, and plot the means and seasonal occurrence \
+    """Run the automated calculation for simplified weathertypes
+    and write to file, and plot the means and seasonal occurrence
     of the weathertypes.
 
     Args:
diff --git a/esmvaltool/diag_scripts/weathertyping/wt_utils.py b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
index eb4e982bbb..63939df256 100644
--- a/esmvaltool/diag_scripts/weathertyping/wt_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
@@ -200,7 +200,8 @@ def load_wt_files(path: str, mode="simplified"):
         lwt_cube = iris.load_cube(path, "lwt")
         wt_cubes = [lwt_cube]
     else:
-        raise ValueError("Mode not recognized. Use 'simplified' or 'lamb'.")
+        e = "Mode not recognized. Use 'simplified' or 'lamb'."
+        raise ValueError(e)
 
     return wt_cubes
 
@@ -209,8 +210,8 @@ def get_provenance_record(
     caption: str,
     ancestors: list,
     long_names: list,
-    plot_types: list = None,
-    statistics: list = None,
+    plot_types: list | None = None,
+    statistics: list | None = None,
 ) -> dict:
     """Get provenance record.
 
@@ -218,8 +219,8 @@ def get_provenance_record(
         caption (str): Caption of plot
         ancestors (list): List of ancestor plots
         long_names (list): List of variable long names
-        plot_types (bool | list): Type of plot
-        statistics (bool | list): Types of statistics used
+        plot_types (list | None): Type of plot
+        statistics (list | None): Types of statistics used
 
     Returns
     -------
@@ -291,7 +292,7 @@ def get_mapping_dict(selected_pairs: list) -> dict:
     mapping_array = []
 
     for elem in selected_pairs:
-        (mapping_array.append(elem[0]),)
+        mapping_array.append(elem[0])
 
     s = [set(i) for i in mapping_array if i]
 

From e8df48b08afbde0e8bf6af2558f337fcb001e137 Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Mon, 1 Dec 2025 14:01:50 +0100
Subject: [PATCH 20/57] refactor wt_algorithm

---
 .../diag_scripts/weathertyping/calc_utils.py  | 163 +++++++++++-------
 .../diag_scripts/weathertyping/wt_utils.py    |   2 -
 2 files changed, 105 insertions(+), 60 deletions(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/calc_utils.py b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
index a4dd0293a2..2fe5dee458 100644
--- a/esmvaltool/diag_scripts/weathertyping/calc_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
@@ -92,11 +92,9 @@ def calc_slwt_obs(
         timerange,
     )
 
-    with open(
+    with Path(
         f"{cfg.get('work_dir')}/wt_selected_pairs_{dataset}.json",
-        "w",
-        encoding="utf-8",
-    ) as file:
+    ).open("w", encoding="utf-8") as file:
         json.dump(selected_pairs, file)
 
     mapping_dict = get_mapping_dict(selected_pairs)
@@ -293,6 +291,99 @@ def calc_total_shear_velocity(
     return westerly_shear_velocity + southerly_shear_velocity
 
 
+def lamp_pure_directional_type(
+    direction: float, weathertypes: np.array, i: int
+) -> int:
+    """Calculate Lamp pure directional weathertype.
+
+    Args:
+    ----
+        direction (float): direction in degrees
+
+    Returns
+    -------
+        int: Lamb weathertype
+    """
+    if direction >= 337.5 or direction < 22.5:
+        weathertypes[i] = 1
+    if 22.5 <= direction < 67.5:
+        weathertypes[i] = 2
+    if 67.5 <= direction < 112.5:
+        weathertypes[i] = 3
+    if 112.5 <= direction < 157.5:
+        weathertypes[i] = 4
+    if 157.5 <= direction < 202.5:
+        weathertypes[i] = 5
+    if 202.5 <= direction < 247.5:
+        weathertypes[i] = 6
+    if 247.5 <= direction < 292.5:
+        weathertypes[i] = 7
+    if 292.5 <= direction < 337.5:
+        weathertypes[i] = 8
+
+
+def lamp_synoptic_directional_type(
+    direction: float, weathertypes: np.array, i: int
+):
+    """Calculate Lamp synoptic/directional hybrid weathertype.
+
+    Args:
+    ----
+        direction (float): direction in degrees
+
+    Returns
+    -------
+        int: Lamb weathertype
+    """
+    if direction >= 337.5 or direction < 22.5:
+        weathertypes[i] = 11
+    if 22.5 <= direction < 67.5:
+        weathertypes[i] = 12
+    if 67.5 <= direction < 112.5:
+        weathertypes[i] = 13
+    if 112.5 <= direction < 157.5:
+        weathertypes[i] = 14
+    if 157.5 <= direction < 202.5:
+        weathertypes[i] = 15
+    if 202.5 <= direction < 247.5:
+        weathertypes[i] = 16
+    if 247.5 <= direction < 292.5:
+        weathertypes[i] = 17
+    if 292.5 <= direction < 337.5:
+        weathertypes[i] = 18
+
+
+def lamb_synoptic_directional_type_zlt0(
+    direction: float, weathertypes: np.array, i: int
+):
+    """Calculate Lamb synoptic/directional hybrid weathertype with z <0.
+
+    Args:
+    ----
+        direction (float): direction in degrees
+
+    Returns
+    -------
+        int: Lamb weathertype
+    """
+    if direction >= 337.5 or direction < 22.5:
+        weathertypes[i] = 19
+    if 22.5 <= direction < 67.5:
+        weathertypes[i] = 20
+    if 67.5 <= direction < 112.5:
+        weathertypes[i] = 21
+    if 112.5 <= direction < 157.5:
+        weathertypes[i] = 22
+    if 157.5 <= direction < 202.5:
+        weathertypes[i] = 23
+    if 202.5 <= direction < 247.5:
+        weathertypes[i] = 24
+    if 247.5 <= direction < 292.5:
+        weathertypes[i] = 25
+    if 292.5 <= direction < 337.5:
+        weathertypes[i] = 26
+
+
 def wt_algorithm(cube: iris.cube.Cube, dataset: str) -> np.array:
     """Algorithm to calculate Lamb weathertypes.
 
@@ -368,22 +459,8 @@ def wt_algorithm(cube: iris.cube.Cube, dataset: str) -> np.array:
 
         # Lamb pure directional type
         if abs(z_i) < total_flow[i]:
-            if direction >= 337.5 or direction < 22.5:
-                weathertypes[i] = 1
-            elif 22.5 <= direction < 67.5:
-                weathertypes[i] = 2
-            elif 67.5 <= direction < 112.5:
-                weathertypes[i] = 3
-            elif 112.5 <= direction < 157.5:
-                weathertypes[i] = 4
-            elif 157.5 <= direction < 202.5:
-                weathertypes[i] = 5
-            elif 202.5 <= direction < 247.5:
-                weathertypes[i] = 6
-            elif 247.5 <= direction < 292.5:
-                weathertypes[i] = 7
-            elif 292.5 <= direction < 337.5:
-                weathertypes[i] = 8
+            # writes directly to weathertypes array
+            lamp_pure_directional_type(direction, weathertypes, i)
         # Lamb pure cyclonic and anticyclonic type
         elif (2 * total_flow[i]) < abs(z_i):
             if z_i > 0:
@@ -394,40 +471,12 @@ def wt_algorithm(cube: iris.cube.Cube, dataset: str) -> np.array:
         # Lamb synoptic/direction hybrid types
         elif total_flow[i] < abs(z_i) < (2 * total_flow[i]):
             if z_i > 0:
-                if direction >= 337.5 or direction < 22.5:
-                    weathertypes[i] = 11
-                elif 22.5 <= direction < 67.5:
-                    weathertypes[i] = 12
-                elif 67.5 <= direction < 112.5:
-                    weathertypes[i] = 13
-                elif 112.5 <= direction < 157.5:
-                    weathertypes[i] = 14
-                elif 157.5 <= direction < 202.5:
-                    weathertypes[i] = 15
-                elif 202.5 <= direction < 247.5:
-                    weathertypes[i] = 16
-                elif 247.5 <= direction < 292.5:
-                    weathertypes[i] = 17
-                elif 292.5 <= direction < 337.5:
-                    weathertypes[i] = 18
+                # writes directly to weathertypes array
+                lamp_synoptic_directional_type(direction, weathertypes, i)
 
             elif z_i < 0:
-                if direction >= 337.5 or direction < 22.5:
-                    weathertypes[i] = 19
-                elif 22.5 <= direction < 67.5:
-                    weathertypes[i] = 20
-                elif 67.5 <= direction < 112.5:
-                    weathertypes[i] = 21
-                elif 112.5 <= direction < 157.5:
-                    weathertypes[i] = 22
-                elif 157.5 <= direction < 202.5:
-                    weathertypes[i] = 23
-                elif 202.5 <= direction < 247.5:
-                    weathertypes[i] = 24
-                elif 247.5 <= direction < 292.5:
-                    weathertypes[i] = 25
-                elif 292.5 <= direction < 337.5:
-                    weathertypes[i] = 26
+                # writes directly to weathertypes array
+                lamb_synoptic_directional_type_zlt0(direction, weathertypes, i)
         # light indeterminate flow, corresponding to Lamb unclassified type U
         elif abs(z_i) < 6 and total_flow[i] < 6:
             weathertypes[i] = 27
@@ -475,16 +524,14 @@ def calc_lwt_slwt_model(
     )
 
     if not predefined_slwt:
-        with open(
+        with Path(
             f"{cfg.get('work_dir')}/wt_mapping_dict_ERA5.json",
-            encoding="utf-8",
-        ) as file:
+        ).open("r", encoding="utf-8") as file:
             mapping_dict_era5_f = json.load(file)
 
-        with open(
+        with Path(
             f"{cfg.get('work_dir')}/wt_mapping_dict_E-OBS.json",
-            encoding="utf-8",
-        ) as file:
+        ).open("r", encoding="utf-8") as file:
             mapping_dict_eobs_f = json.load(file)
         mapping_dict_era5 = reverse_convert_dict(mapping_dict_era5_f)
         mapping_dict_eobs = reverse_convert_dict(mapping_dict_eobs_f)
diff --git a/esmvaltool/diag_scripts/weathertyping/wt_utils.py b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
index 63939df256..b43e448a08 100644
--- a/esmvaltool/diag_scripts/weathertyping/wt_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
@@ -322,8 +322,6 @@ def write_mapping_dict(work_dir: str, dataset: str, mapping_dict: dict):
 
     with Path(
         f"{work_dir}/wt_mapping_dict_{dataset}.json",
-        "w",
-        encoding="utf-8",
     ).open("w", encoding="utf-8") as file:
         json.dump(mapping_dict_reformat, file)
 

From e2b5013e9dc149a6f1c2ee3bcc2a36e6ad587e2d Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Mon, 1 Dec 2025 14:24:52 +0100
Subject: [PATCH 21/57] magic values

---
 .../diag_scripts/weathertyping/calc_utils.py  | 74 +++++++++++--------
 .../weathertyping/weathertyping.py            |  8 +-
 .../diag_scripts/weathertyping/wt_utils.py    | 10 +--
 3 files changed, 53 insertions(+), 39 deletions(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/calc_utils.py b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
index 2fe5dee458..c7181c047c 100644
--- a/esmvaltool/diag_scripts/weathertyping/calc_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
@@ -5,6 +5,7 @@
 import logging
 import warnings
 from pathlib import Path
+from typing import Final
 
 # to manipulate iris cubes
 import iris
@@ -36,6 +37,17 @@
 warnings.filterwarnings("ignore", ".*Collapsing a non-contiguous coordinate*")
 
 
+DIR_0: Final = 0.0
+DIR_22_5: Final = 22.5
+DIR_67_5: Final = 67.5
+DIR_112_5: Final = 112.5
+DIR_157_5: Final = 157.5
+DIR_202_5: Final = 202.5
+DIR_247_5: Final = 247.5
+DIR_292_5: Final = 292.5
+DIR_337_5: Final = 337.5
+
+
 def calc_slwt_obs(
     cfg: dict,
     lwt: np.array,
@@ -304,21 +316,21 @@ def lamp_pure_directional_type(
     -------
         int: Lamb weathertype
     """
-    if direction >= 337.5 or direction < 22.5:
+    if direction >= DIR_337_5 or direction < DIR_22_5:
         weathertypes[i] = 1
-    if 22.5 <= direction < 67.5:
+    if DIR_22_5 <= direction < DIR_67_5:
         weathertypes[i] = 2
-    if 67.5 <= direction < 112.5:
+    if DIR_67_5 <= direction < DIR_112_5:
         weathertypes[i] = 3
-    if 112.5 <= direction < 157.5:
+    if DIR_112_5 <= direction < DIR_157_5:
         weathertypes[i] = 4
-    if 157.5 <= direction < 202.5:
+    if DIR_157_5 <= direction < DIR_202_5:
         weathertypes[i] = 5
-    if 202.5 <= direction < 247.5:
+    if DIR_202_5 <= direction < DIR_247_5:
         weathertypes[i] = 6
-    if 247.5 <= direction < 292.5:
+    if DIR_247_5 <= direction < DIR_292_5:
         weathertypes[i] = 7
-    if 292.5 <= direction < 337.5:
+    if DIR_292_5 <= direction < DIR_337_5:
         weathertypes[i] = 8
 
 
@@ -335,21 +347,21 @@ def lamp_synoptic_directional_type(
     -------
         int: Lamb weathertype
     """
-    if direction >= 337.5 or direction < 22.5:
+    if direction >= DIR_337_5 or direction < DIR_22_5:
         weathertypes[i] = 11
-    if 22.5 <= direction < 67.5:
+    if DIR_22_5 <= direction < DIR_67_5:
         weathertypes[i] = 12
-    if 67.5 <= direction < 112.5:
+    if DIR_67_5 <= direction < DIR_112_5:
         weathertypes[i] = 13
-    if 112.5 <= direction < 157.5:
+    if DIR_112_5 <= direction < DIR_157_5:
         weathertypes[i] = 14
-    if 157.5 <= direction < 202.5:
+    if DIR_157_5 <= direction < DIR_202_5:
         weathertypes[i] = 15
-    if 202.5 <= direction < 247.5:
+    if DIR_202_5 <= direction < DIR_247_5:
         weathertypes[i] = 16
-    if 247.5 <= direction < 292.5:
+    if DIR_247_5 <= direction < DIR_292_5:
         weathertypes[i] = 17
-    if 292.5 <= direction < 337.5:
+    if DIR_292_5 <= direction < DIR_337_5:
         weathertypes[i] = 18
 
 
@@ -366,21 +378,21 @@ def lamb_synoptic_directional_type_zlt0(
     -------
         int: Lamb weathertype
     """
-    if direction >= 337.5 or direction < 22.5:
+    if direction >= DIR_337_5 or direction < DIR_22_5:
         weathertypes[i] = 19
-    if 22.5 <= direction < 67.5:
+    if DIR_22_5 <= direction < DIR_67_5:
         weathertypes[i] = 20
-    if 67.5 <= direction < 112.5:
+    if DIR_67_5 <= direction < DIR_112_5:
         weathertypes[i] = 21
-    if 112.5 <= direction < 157.5:
+    if DIR_112_5 <= direction < DIR_157_5:
         weathertypes[i] = 22
-    if 157.5 <= direction < 202.5:
+    if DIR_157_5 <= direction < DIR_202_5:
         weathertypes[i] = 23
-    if 202.5 <= direction < 247.5:
+    if DIR_202_5 <= direction < DIR_247_5:
         weathertypes[i] = 24
-    if 247.5 <= direction < 292.5:
+    if DIR_247_5 <= direction < DIR_292_5:
         weathertypes[i] = 25
-    if 292.5 <= direction < 337.5:
+    if DIR_292_5 <= direction < DIR_337_5:
         weathertypes[i] = 26
 
 
@@ -454,7 +466,7 @@ def wt_algorithm(cube: iris.cube.Cube, dataset: str) -> np.array:
         if southerly_flow[i] >= 0:
             direction += 180  # deg
 
-        if direction < 0:
+        if direction < DIR_0:
             direction += 360  # deg
 
         # Lamb pure directional type
@@ -980,7 +992,7 @@ def plot_means(
     preproc_var: np.array,
     wt_cubes: iris.cube.Cube,
     data_info: dict,
-    only_lwt=False,
+    mode: str = "slwt",
 ):
     """Plot means, anomalies and standard deviations.
 
@@ -990,16 +1002,18 @@ def plot_means(
         preproc_var (np.array): Preprocessed variable cube
         wt_cubes (iris.cube.Cube): List of cubes of lwt, slwt_ERA5 and slwt_EOBS
         data_info (dict): Dictionary with info to dataset
-        only_lwt (bool, optional): If True, only lwt means are calculated.
-            If False, lwt, slwt_ERA5 and slwt_EOBS means are calculated. Defaults to False.
+        mode (str, optional): Mode of weathertype calculation, either "slwt" or "lwt". Defaults to "slwt".
     """
-    if not only_lwt:
+    if mode == "slwt":
         data_info["wt_string"] = "lwt"
         calc_wt_means(cfg, preproc_var, wt_cubes, data_info)
         data_info["wt_string"] = "slwt_ERA5"
         calc_wt_means(cfg, preproc_var, wt_cubes, data_info)
         data_info["wt_string"] = "slwt_EOBS"
         calc_wt_means(cfg, preproc_var, wt_cubes, data_info)
-    else:
+    elif mode == "lwt":
         data_info["wt_string"] = "lwt"
         calc_wt_means(cfg, preproc_var, wt_cubes, data_info)
+    else:
+        e = "mode must be either 'slwt' or 'lwt'"
+        raise ValueError(e)
diff --git a/esmvaltool/diag_scripts/weathertyping/weathertyping.py b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
index f991cb04ff..a6a420b893 100644
--- a/esmvaltool/diag_scripts/weathertyping/weathertyping.py
+++ b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
@@ -244,7 +244,7 @@ def process_era5_lwt(preproc_variables_dict, cfg, dataset_vars, data_info):
     # load wt files
     wt_cubes = load_wt_files(
         f"{cfg.get('work_dir')}/{data_info['dataset']}.nc",
-        mode="lamb",
+        mode="lwt",
     )
 
     if cfg.get("plotting", False):
@@ -256,7 +256,7 @@ def process_era5_lwt(preproc_variables_dict, cfg, dataset_vars, data_info):
             data_info["var"] = var_name
             data_info["preproc_path"] = var_data[1]
 
-            plot_means(cfg, var_data[0], wt_cubes, data_info, only_lwt=True)
+            plot_means(cfg, var_data[0], wt_cubes, data_info, "lwt")
         plot_seasonal_occurrence(cfg, wt_cubes, data_info)
 
 
@@ -297,7 +297,7 @@ def process_models_lwt(cfg: dict, dataset_vars: list, data_info: dict):
                 f"{data_info['driver']}"
                 f"_{data_info['ensemble']}_"
                 f"{data_info['timerange']}.nc",
-                mode="lamb",
+                mode="lwt",
             )
 
             var_dict = get_looping_dict(
@@ -315,7 +315,7 @@ def process_models_lwt(cfg: dict, dataset_vars: list, data_info: dict):
                         var_data[0],
                         wt_cubes,
                         data_info,
-                        only_lwt=True,
+                        "lwt",
                     )
                 plot_seasonal_occurrence(cfg, wt_cubes, data_info)
 
diff --git a/esmvaltool/diag_scripts/weathertyping/wt_utils.py b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
index b43e448a08..dc736c0e46 100644
--- a/esmvaltool/diag_scripts/weathertyping/wt_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
@@ -180,27 +180,27 @@ def get_looping_dict(preproc_vars: list):
     }
 
 
-def load_wt_files(path: str, mode="simplified"):
+def load_wt_files(path: str, mode="slwt"):
     """Load wt files.
 
     Args:
         path (str): Path of wt file
-        mode (str, optional): Type of weathertype to load. Defaults to "simplified".
+        mode (str, optional): Type of weathertype to load. Defaults to "slwt".
 
     Returns
     -------
         list: List of weathertype cubes.
     """
-    if mode == "simplified":
+    if mode == "slwt":
         lwt_cube = iris.load_cube(path, "lwt")
         slwt_era5_cube = iris.load_cube(path, "slwt_era5")
         slwt_eobs_cube = iris.load_cube(path, "slwt_eobs")
         wt_cubes = [lwt_cube, slwt_era5_cube, slwt_eobs_cube]
-    elif mode == "lamb":
+    elif mode == "lwt":
         lwt_cube = iris.load_cube(path, "lwt")
         wt_cubes = [lwt_cube]
     else:
-        e = "Mode not recognized. Use 'simplified' or 'lamb'."
+        e = "Mode not recognized. Use 'slwt' or 'lwt'."
         raise ValueError(e)
 
     return wt_cubes

From 899aca19717ad11032148894a7fd26632c8e9921 Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Mon, 1 Dec 2025 14:30:14 +0100
Subject: [PATCH 22/57] minor

---
 .../diag_scripts/weathertyping/calc_utils.py   | 18 ++++++++++++++----
 .../diag_scripts/weathertyping/wt_utils.py     |  5 +----
 2 files changed, 15 insertions(+), 8 deletions(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/calc_utils.py b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
index c7181c047c..b0ccb36fdc 100644
--- a/esmvaltool/diag_scripts/weathertyping/calc_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
@@ -37,6 +37,7 @@
 warnings.filterwarnings("ignore", ".*Collapsing a non-contiguous coordinate*")
 
 
+# defining constants for direction calculations
 DIR_0: Final = 0.0
 DIR_22_5: Final = 22.5
 DIR_67_5: Final = 67.5
@@ -47,6 +48,9 @@
 DIR_292_5: Final = 292.5
 DIR_337_5: Final = 337.5
 
+# defining constant for flow limit
+FLOW_LIMIT: Final = 6
+
 
 def calc_slwt_obs(
     cfg: dict,
@@ -304,7 +308,9 @@ def calc_total_shear_velocity(
 
 
 def lamp_pure_directional_type(
-    direction: float, weathertypes: np.array, i: int
+    direction: float,
+    weathertypes: np.array,
+    i: int,
 ) -> int:
     """Calculate Lamp pure directional weathertype.
 
@@ -335,7 +341,9 @@ def lamp_pure_directional_type(
 
 
 def lamp_synoptic_directional_type(
-    direction: float, weathertypes: np.array, i: int
+    direction: float,
+    weathertypes: np.array,
+    i: int,
 ):
     """Calculate Lamp synoptic/directional hybrid weathertype.
 
@@ -366,7 +374,9 @@ def lamp_synoptic_directional_type(
 
 
 def lamb_synoptic_directional_type_zlt0(
-    direction: float, weathertypes: np.array, i: int
+    direction: float,
+    weathertypes: np.array,
+    i: int,
 ):
     """Calculate Lamb synoptic/directional hybrid weathertype with z <0.
 
@@ -490,7 +500,7 @@ def wt_algorithm(cube: iris.cube.Cube, dataset: str) -> np.array:
                 # writes directly to weathertypes array
                 lamb_synoptic_directional_type_zlt0(direction, weathertypes, i)
         # light indeterminate flow, corresponding to Lamb unclassified type U
-        elif abs(z_i) < 6 and total_flow[i] < 6:
+        elif abs(z_i) < FLOW_LIMIT and total_flow[i] < FLOW_LIMIT:
             weathertypes[i] = 27
 
     return weathertypes
diff --git a/esmvaltool/diag_scripts/weathertyping/wt_utils.py b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
index dc736c0e46..629a0f6e49 100644
--- a/esmvaltool/diag_scripts/weathertyping/wt_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
@@ -289,10 +289,7 @@ def get_mapping_dict(selected_pairs: list) -> dict:
     -------
         dict: Mapping dictionary
     """
-    mapping_array = []
-
-    for elem in selected_pairs:
-        mapping_array.append(elem[0])
+    mapping_array = [elem[0] for elem in selected_pairs]
 
     s = [set(i) for i in mapping_array if i]
 

From b18c427156c69646b53e72ad63aa3a8f7f8780aa Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Mon, 1 Dec 2025 15:54:05 +0100
Subject: [PATCH 23/57] refactor plot_maps

---
 .../diag_scripts/weathertyping/plot_utils.py  | 54 ++++++++++++-------
 .../weathertyping/weathertyping.py            |  4 +-
 2 files changed, 36 insertions(+), 22 deletions(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/plot_utils.py b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
index c2fadd2a61..4ea72c1cb6 100644
--- a/esmvaltool/diag_scripts/weathertyping/plot_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
@@ -157,6 +157,40 @@ def plot_seasonal_occurrence(
         plt.close()
 
 
+def set_gridlines(ax: plt.Axes):
+    """Set gridlines for plotting maps.
+
+    Args:
+    ----
+        ax (plt.Axes): Axes object to draw gridlines on.
+
+    Returns
+    -------
+        gl (ax.gridlines): Gridlines object
+    """
+    gl = ax.gridlines(
+        crs=ccrs.PlateCarree(),
+        draw_labels=True,
+        linewidth=0.5,
+        color="gray",
+        alpha=0.5,
+        linestyle="--",
+    )
+    gl.left_labels = True
+    gl.bottom_labels = True
+    gl.top_labels = False
+    gl.right_labels = False
+    gl.xlines = True
+    gl.ylocator = mticker.FixedLocator(np.arange(20, 70, 5))
+    gl.xlocator = mticker.FixedLocator([-10, -5, 0, 5, 10, 15])
+    gl.xformatter = LONGITUDE_FORMATTER
+    gl.yformatter = LATITUDE_FORMATTER
+    gl.xlabel_style = {"size": 8, "color": "black"}
+    gl.ylabel_style = {"color": "black", "size": 8}
+
+    return gl
+
+
 def plot_maps(
     wt: np.array,
     cfg: dict,
@@ -228,25 +262,7 @@ def plot_maps(
         cb.ax.tick_params(labelsize=8)
         cb.set_label(label=f"{var_name} {mode} {unit}")
 
-    gl = ax.gridlines(
-        crs=ccrs.PlateCarree(),
-        draw_labels=True,
-        linewidth=0.5,
-        color="gray",
-        alpha=0.5,
-        linestyle="--",
-    )
-    gl.left_labels = True
-    gl.bottom_labels = True
-    gl.top_labels = False
-    gl.right_labels = False
-    gl.xlines = True
-    gl.ylocator = mticker.FixedLocator(np.arange(20, 70, 5))
-    gl.xlocator = mticker.FixedLocator([-10, -5, 0, 5, 10, 15])
-    gl.xformatter = LONGITUDE_FORMATTER
-    gl.yformatter = LATITUDE_FORMATTER
-    gl.xlabel_style = {"size": 8, "color": "black"}
-    gl.ylabel_style = {"color": "black", "size": 8}
+    set_gridlines(ax)
 
     ax.set_extent([-15, 20, 27.5, 62.5])
 
diff --git a/esmvaltool/diag_scripts/weathertyping/weathertyping.py b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
index a6a420b893..63496d6a6e 100644
--- a/esmvaltool/diag_scripts/weathertyping/weathertyping.py
+++ b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
@@ -175,9 +175,7 @@ def process_era5_automatic_slwt(
 
 
 def run_automatic_slwt(cfg: dict):
-    """Run the automated calculation for simplified weathertypes
-    and write to file, and plot the means and seasonal occurrence
-    of the weathertypes.
+    """Run the automated calculation for simplified weathertypes and write to file, and plot the means and seasonal occurrence of the weathertypes.
 
     Args:
     ----

From 3273db43a3bb01cc8861adb97d3ac75305f658b6 Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Mon, 1 Dec 2025 16:03:20 +0100
Subject: [PATCH 24/57] refactor plot_maps

---
 .../diag_scripts/weathertyping/plot_utils.py  | 105 +++++++++++-------
 1 file changed, 65 insertions(+), 40 deletions(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/plot_utils.py b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
index 4ea72c1cb6..b5ca7fa21a 100644
--- a/esmvaltool/diag_scripts/weathertyping/plot_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
@@ -191,6 +191,61 @@ def set_gridlines(ax: plt.Axes):
     return gl
 
 
+def prepare_plot_title(
+    data_info: dict, wt: int, var_name: str, mode: str
+) -> str:
+    """Return formatted plot title.
+
+    Args:
+    ----
+        data_info (dict): dict containing info on dataset
+        wt (int): weathertype
+        var_name (str): name of variable
+        mode (str): statistic used
+
+    Returns
+    -------
+        (str): title of plot
+    """
+    ensemble = data_info.get("ensemble", "")
+    timerange = data_info.get("timerange")
+    dataset = data_info.get("dataset")
+    if var_name == "pr" and dataset == "ERA5":
+        title = f"{dataset} {ensemble}, total {var_name} {mode}\n{timerange}, wt: {wt}"
+    elif var_name == "pr":
+        title = f"{dataset} {ensemble}, {var_name} flux {mode}\n{timerange}, wt: {wt}"
+    elif var_name == "tas":
+        title = f"{dataset} {ensemble}, 1000 hPa {var_name} {mode}\n{timerange}, wt: {wt}"
+    else:  # psl or others
+        title = (
+            f"{dataset} {ensemble}, {var_name} {mode}\n{timerange}, wt: {wt}"
+        )
+    return title
+
+
+def get_unit(var_name: str, dataset: str) -> str:
+    """Get unit of variables.
+
+    Args:
+    ----
+        var_name (str): name of variable
+        dataset (str): name of dataset
+
+    Returns
+    -------
+        (str): unit of variable
+    """
+    if var_name == "psl":
+        return "[hPa]"
+    if var_name == "pr" and dataset == "ERA5":
+        return "[m]"
+    if var_name == "pr":
+        return "[kg m-2 s-1]"
+    if var_name == "tas":
+        return "[K]"
+    return ""
+
+
 def plot_maps(
     wt: np.array,
     cfg: dict,
@@ -221,46 +276,16 @@ def plot_maps(
 
     ax = plt.axes(projection=ccrs.PlateCarree())
 
-    if data_info.get("var") == "psl":
-        psl_cmap = get_colormap("psl")
-        plt.title(
-            f"{data_info.get('dataset')} {data_info.get('ensemble', '')}, {data_info.get('var')} {mode}\n"
-            f"{data_info.get('timerange')}, wt: {wt}",
-        )
-        unit = "[hPa]"
-        im = iplt.contourf(cube / 100, cmap=psl_cmap)
-        cb = plt.colorbar(im)
-        cb.ax.tick_params(labelsize=8)
-        cb.set_label(label=f"{var_name} {mode} {unit}")
-    elif var_name == "pr":
-        prcp_cmap = get_colormap("prcp")
-        if data_info.get("dataset") == "ERA5":
-            unit = "[m]"
-            plt.title(
-                f"{data_info.get('dataset')} {data_info.get('ensemble', '')}, total {var_name} {mode}\n"
-                f"{data_info.get('timerange')}, wt: {wt}",
-            )
-        else:
-            unit = "[kg m-2 s-1]"
-            plt.title(
-                f"{data_info.get('dataset')} {data_info.get('ensemble', '')}, {var_name} flux {mode}\n"
-                f"{data_info.get('timerange')}, wt: {wt}",
-            )
-        im = iplt.contourf(cube, cmap=prcp_cmap)
-        cb = plt.colorbar(im)
-        cb.ax.tick_params(labelsize=8)
-        cb.set_label(label=f"{var_name} {mode} {unit}")
-    elif var_name == "tas":
-        temp_cmap = get_colormap("temp")
-        unit = "[K]"
-        plt.title(
-            f"{data_info.get('dataset')} {data_info.get('ensemble', '')}, 1000 hPa {var_name} {mode}\n"
-            f"{data_info.get('timerange')}, wt: {wt}",
-        )
-        im = iplt.contourf(cube, cmap=temp_cmap)
-        cb = plt.colorbar(im)
-        cb.ax.tick_params(labelsize=8)
-        cb.set_label(label=f"{var_name} {mode} {unit}")
+    cmap = get_colormap(var_name if var_name != "pr" else "prcp")
+
+    title = prepare_plot_title(data_info, wt, var_name, mode)
+    plt.title(title)
+    unit = get_unit(var_name, data_info.get("dataset"))
+
+    im = iplt.contourf(cube if var_name != "psl" else cube / 100, cmap=cmap)
+    cb = plt.colorbar(im)
+    cb.ax.tick_params(labelsize=8)
+    cb.set_label(label=f"{var_name} {mode} {unit}")
 
     set_gridlines(ax)
 

From ed31fdb21a5b5a61ccd6c6037acff5f163b841b6 Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Mon, 1 Dec 2025 16:10:24 +0100
Subject: [PATCH 25/57] minor

---
 esmvaltool/diag_scripts/weathertyping/plot_utils.py    | 5 ++++-
 esmvaltool/diag_scripts/weathertyping/weathertyping.py | 5 +----
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/plot_utils.py b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
index b5ca7fa21a..bb9b574e03 100644
--- a/esmvaltool/diag_scripts/weathertyping/plot_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
@@ -192,7 +192,10 @@ def set_gridlines(ax: plt.Axes):
 
 
 def prepare_plot_title(
-    data_info: dict, wt: int, var_name: str, mode: str
+    data_info: dict,
+    wt: int,
+    var_name: str,
+    mode: str,
 ) -> str:
     """Return formatted plot title.
 
diff --git a/esmvaltool/diag_scripts/weathertyping/weathertyping.py b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
index 63496d6a6e..c0ca748f01 100644
--- a/esmvaltool/diag_scripts/weathertyping/weathertyping.py
+++ b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
@@ -319,10 +319,7 @@ def process_models_lwt(cfg: dict, dataset_vars: list, data_info: dict):
 
 
 def run_lwt(cfg: dict):
-    """Run calculation of weathertypes.
-    Write to file, and plot the means of psl, tas, and pr \
-    for each weathertype. \
-    Plot seasonal occurrence of the weathertypes.
+    """Run calculation of weathertypes. Write to file, and plot the means of psl, tas, and pr for each weathertype. Plot seasonal occurrence of the weathertypes.
 
     Args:
     -------

From 3d551dabc619f7ff9923fc521fdbac436b69fe9d Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Thu, 4 Dec 2025 09:51:22 +0100
Subject: [PATCH 26/57] minor changes for provenance handling

---
 .../diag_scripts/weathertyping/calc_utils.py  |  3 +-
 .../diag_scripts/weathertyping/plot_utils.py  | 31 ++++++++++++++++---
 .../diag_scripts/weathertyping/wt_utils.py    |  4 ++-
 3 files changed, 31 insertions(+), 7 deletions(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/calc_utils.py b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
index b0ccb36fdc..5dc1aea5de 100644
--- a/esmvaltool/diag_scripts/weathertyping/calc_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
@@ -732,8 +732,7 @@ def calc_wt_means(
         f"{cfg.get('work_dir')}/ERA5.nc",
     ]
     provenance_record = get_provenance_record(
-        f"{var_name} means for \
-                                              {wt_string}",
+        f"{var_name} means for {wt_string}",
         ancestors,
         [var_name],
         ["map"],
diff --git a/esmvaltool/diag_scripts/weathertyping/plot_utils.py b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
index bb9b574e03..60537981c3 100644
--- a/esmvaltool/diag_scripts/weathertyping/plot_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
@@ -23,7 +23,7 @@
 # local imports
 from cartopy.mpl.gridliner import LATITUDE_FORMATTER, LONGITUDE_FORMATTER
 from matplotlib.colors import ListedColormap
-from wt_utils import get_driver
+from wt_utils import get_driver, get_provenance_record, log_provenance
 
 iris.FUTURE.datum_support = True
 
@@ -69,8 +69,8 @@ def plot_seasonal_occurrence(
 
     output_path = f"{cfg['plot_dir']}/seasonal_occurrence"
 
-    if not Path.exists(f"{output_path}"):
-        Path(f"{output_path}").mkdir(parents=True, exist_ok=True)
+    if not Path(output_path).exists():
+        Path(output_path).mkdir(parents=True, exist_ok=True)
 
     month_list = [
         "Jan",
@@ -271,7 +271,7 @@ def plot_maps(
     logger.info(
         "Plotting %s %s %s for %s %s",
         data_info.get("dataset"),
-        data_info.get("var"),
+        var_name,
         mode,
         data_info.get("wt_string"),
         wt,
@@ -313,6 +313,29 @@ def plot_maps(
     )
     plt.close()
 
+    # log provenance
+    ancestors = [
+        f"{data_info.get('preproc_path')}",
+        f"{cfg.get('work_dir')}/ERA5.nc",
+    ]
+    provenance_record = get_provenance_record(
+        f"{var_name} {mode} for {data_info.get('wt_string')}",
+        ancestors,
+        [var_name],
+        ["map"],
+        [mode],
+    )
+
+    local_path = f"{cfg.get('plot_dir')}/{mode}"
+
+    log_provenance(
+        f"{local_path}/{data_info.get('wt_string')}_{wt}{get_driver(data_info)}_"
+        f"{data_info.get('dataset')}_{data_info.get('ensemble', '')}"
+        f"_{var_name}_{mode}_{data_info.get('timerange')}.png",
+        cfg,
+        provenance_record,
+    )
+
 
 def plot_corr_rmse_heatmaps(
     cfg: dict,
diff --git a/esmvaltool/diag_scripts/weathertyping/wt_utils.py b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
index 629a0f6e49..43911a1e6b 100644
--- a/esmvaltool/diag_scripts/weathertyping/wt_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
@@ -254,7 +254,9 @@ def log_provenance(filename: str, cfg: dict, provenance_record: dict):
     with ProvenanceLogger(cfg) as provenance_logger:
         provenance_logger.log(filename, provenance_record)
 
-    logger.info("Output stored as %s", filename)
+    logger.info(
+        "Provenance added to %s", f"{cfg['run_dir']}/diagnostic_provenance.yml"
+    )
 
 
 def turn_list_to_mapping_dict(list_: list) -> dict:

From d8128c695d44ec42af2761fbb58485d85e396644 Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Thu, 4 Dec 2025 09:53:47 +0100
Subject: [PATCH 27/57] trailing comma

---
 esmvaltool/diag_scripts/weathertyping/wt_utils.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/wt_utils.py b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
index 43911a1e6b..e5a4600855 100644
--- a/esmvaltool/diag_scripts/weathertyping/wt_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
@@ -255,7 +255,8 @@ def log_provenance(filename: str, cfg: dict, provenance_record: dict):
         provenance_logger.log(filename, provenance_record)
 
     logger.info(
-        "Provenance added to %s", f"{cfg['run_dir']}/diagnostic_provenance.yml"
+        "Provenance added to %s",
+        f"{cfg['run_dir']}/diagnostic_provenance.yml",
     )
 
 

From 52e7e59683fc446bb9581a6fd6952a9f7955adb3 Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Thu, 4 Dec 2025 10:33:34 +0100
Subject: [PATCH 28/57] .

---
 esmvaltool/diag_scripts/weathertyping/wt_utils.py | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/esmvaltool/diag_scripts/weathertyping/wt_utils.py b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
index e5a4600855..5ac2eb7896 100644
--- a/esmvaltool/diag_scripts/weathertyping/wt_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
@@ -29,6 +29,7 @@ def get_driver(data_info: dict) -> str:
     """Get driving model name and string for further use.
 
     Args:
+    ----
         data_info (dict): Data information dictionary.
 
     Returns
@@ -42,6 +43,7 @@ def load_wt_preprocessors(dataset: str, preproc_variables_dict: dict):
     """Load preprocessor cubes for calculating Lamb weathertypes.
 
     Args:
+    ----
         dataset (str): Name of dataset
         preproc_variables_dict (dict): Dictionary with info on preprocessor variables
 
@@ -73,6 +75,7 @@ def get_ancestors_era5_eobs(dataset: str, preproc_variables_dict: dict):
     """Get ancestors for observational data.
 
     Args:
+    ----
         dataset (str): Name of dataset
         preproc_variables_dict (dict): Dictionary with info on preprocessor variables
 
@@ -102,6 +105,7 @@ def get_model_output_filepath(dataset: str, data_info: list):
     """Get output filepaths for models.
 
     Args:
+    ----
         dataset (str): Name of dataset
         data_info (list): Model variables
 
@@ -124,6 +128,7 @@ def get_preproc_lists(preproc_vars: list):
     """Put preprocessors and paths into lists for further use.
 
     Args:
+    ----
         preproc_vars (list): List of preprocessor variables.
 
     Returns
@@ -148,6 +153,7 @@ def get_preproc_lists_ensemble(preproc_vars: list):
     """Put preprocessors and paths into lists for further use.
 
     Args:
+    ----
         preproc_vars (list): List of preprocessor variables.
 
     Returns
@@ -165,6 +171,7 @@ def get_looping_dict(preproc_vars: list):
     """Put variable preprocessors into dict for looping.
 
     Args:
+    ----
         preproc_vars (list): List of preprocessor variables.
 
     Returns
@@ -184,6 +191,7 @@ def load_wt_files(path: str, mode="slwt"):
     """Load wt files.
 
     Args:
+    ----
         path (str): Path of wt file
         mode (str, optional): Type of weathertype to load. Defaults to "slwt".
 
@@ -216,6 +224,7 @@ def get_provenance_record(
     """Get provenance record.
 
     Args:
+    ----
         caption (str): Caption of plot
         ancestors (list): List of ancestor plots
         long_names (list): List of variable long names
@@ -264,6 +273,7 @@ def turn_list_to_mapping_dict(list_: list) -> dict:
     """Turn list of combined WT to a dictionary for further use.
 
     Args:
+    ----
         list_ (list): List of combined WTs
 
     Returns
@@ -286,6 +296,7 @@ def get_mapping_dict(selected_pairs: list) -> dict:
     """Get mapping dictionary from list of selected pairs.
 
     Args:
+    ----
         selected_pairs (list): List of selected weathertype pairs
 
     Returns

From 4405f42217c2265a7bf4ba26aaf73335953eabed Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Tue, 9 Dec 2025 08:14:56 +0100
Subject: [PATCH 29/57] rm copilot and codacy-cli files, rm comments for
 imports

---
 .codacy/cli.sh                                | 149 ----------------
 .codacy/codacy.yaml                           |   4 -
 .github/copilot-instructions.md               | 165 ------------------
 .../diag_scripts/weathertyping/calc_utils.py  |   6 -
 .../diag_scripts/weathertyping/plot_utils.py  |   8 -
 .../weathertyping/weathertyping.py            |   1 -
 .../diag_scripts/weathertyping/wt_utils.py    |   5 -
 7 files changed, 338 deletions(-)
 delete mode 100755 .codacy/cli.sh
 delete mode 100644 .codacy/codacy.yaml
 delete mode 100644 .github/copilot-instructions.md

diff --git a/.codacy/cli.sh b/.codacy/cli.sh
deleted file mode 100755
index d5438cdd31..0000000000
--- a/.codacy/cli.sh
+++ /dev/null
@@ -1,149 +0,0 @@
-#!/usr/bin/env bash
-
-
-set -e +o pipefail
-
-# Set up paths first
-bin_name="codacy-cli-v2"
-
-# Determine OS-specific paths
-os_name=$(uname)
-arch=$(uname -m)
-
-case "$arch" in
-"x86_64")
-  arch="amd64"
-  ;;
-"x86")
-  arch="386"
-  ;;
-"aarch64"|"arm64")
-  arch="arm64"
-  ;;
-esac
-
-if [ -z "$CODACY_CLI_V2_TMP_FOLDER" ]; then
-    if [ "$(uname)" = "Linux" ]; then
-        CODACY_CLI_V2_TMP_FOLDER="$HOME/.cache/codacy/codacy-cli-v2"
-    elif [ "$(uname)" = "Darwin" ]; then
-        CODACY_CLI_V2_TMP_FOLDER="$HOME/Library/Caches/Codacy/codacy-cli-v2"
-    else
-        CODACY_CLI_V2_TMP_FOLDER=".codacy-cli-v2"
-    fi
-fi
-
-version_file="$CODACY_CLI_V2_TMP_FOLDER/version.yaml"
-
-
-get_version_from_yaml() {
-    if [ -f "$version_file" ]; then
-        local version=$(grep -o 'version: *"[^"]*"' "$version_file" | cut -d'"' -f2)
-        if [ -n "$version" ]; then
-            echo "$version"
-            return 0
-        fi
-    fi
-    return 1
-}
-
-get_latest_version() {
-    local response
-    if [ -n "$GH_TOKEN" ]; then
-        response=$(curl -Lq --header "Authorization: Bearer $GH_TOKEN" "https://api.github.com/repos/codacy/codacy-cli-v2/releases/latest" 2>/dev/null)
-    else
-        response=$(curl -Lq "https://api.github.com/repos/codacy/codacy-cli-v2/releases/latest" 2>/dev/null)
-    fi
-
-    handle_rate_limit "$response"
-    local version=$(echo "$response" | grep -m 1 tag_name | cut -d'"' -f4)
-    echo "$version"
-}
-
-handle_rate_limit() {
-    local response="$1"
-    if echo "$response" | grep -q "API rate limit exceeded"; then
-          fatal "Error: GitHub API rate limit exceeded. Please try again later"
-    fi
-}
-
-download_file() {
-    local url="$1"
-
-    echo "Downloading from URL: ${url}"
-    if command -v curl > /dev/null 2>&1; then
-        curl -# -LS "$url" -O
-    elif command -v wget > /dev/null 2>&1; then
-        wget "$url"
-    else
-        fatal "Error: Could not find curl or wget, please install one."
-    fi
-}
-
-download() {
-    local url="$1"
-    local output_folder="$2"
-
-    ( cd "$output_folder" && download_file "$url" )
-}
-
-download_cli() {
-    # OS name lower case
-    suffix=$(echo "$os_name" | tr '[:upper:]' '[:lower:]')
-
-    local bin_folder="$1"
-    local bin_path="$2"
-    local version="$3"
-
-    if [ ! -f "$bin_path" ]; then
-        echo "📥 Downloading CLI version $version..."
-
-        remote_file="codacy-cli-v2_${version}_${suffix}_${arch}.tar.gz"
-        url="https://github.com/codacy/codacy-cli-v2/releases/download/${version}/${remote_file}"
-
-        download "$url" "$bin_folder"
-        tar xzfv "${bin_folder}/${remote_file}" -C "${bin_folder}"
-    fi
-}
-
-# Warn if CODACY_CLI_V2_VERSION is set and update is requested
-if [ -n "$CODACY_CLI_V2_VERSION" ] && [ "$1" = "update" ]; then
-    echo "⚠️  Warning: Performing update with forced version $CODACY_CLI_V2_VERSION"
-    echo "    Unset CODACY_CLI_V2_VERSION to use the latest version"
-fi
-
-# Ensure version.yaml exists and is up to date
-if [ ! -f "$version_file" ] || [ "$1" = "update" ]; then
-    echo "ℹ️  Fetching latest version..."
-    version=$(get_latest_version)
-    mkdir -p "$CODACY_CLI_V2_TMP_FOLDER"
-    echo "version: \"$version\"" > "$version_file"
-fi
-
-# Set the version to use
-if [ -n "$CODACY_CLI_V2_VERSION" ]; then
-    version="$CODACY_CLI_V2_VERSION"
-else
-    version=$(get_version_from_yaml)
-fi
-
-
-# Set up version-specific paths
-bin_folder="${CODACY_CLI_V2_TMP_FOLDER}/${version}"
-
-mkdir -p "$bin_folder"
-bin_path="$bin_folder"/"$bin_name"
-
-# Download the tool if not already installed
-download_cli "$bin_folder" "$bin_path" "$version"
-chmod +x "$bin_path"
-
-run_command="$bin_path"
-if [ -z "$run_command" ]; then
-    fatal "Codacy cli v2 binary could not be found."
-fi
-
-if [ "$#" -eq 1 ] && [ "$1" = "download" ]; then
-    echo "Codacy cli v2 download succeeded"
-else
-    eval "$run_command $*"
-fi
diff --git a/.codacy/codacy.yaml b/.codacy/codacy.yaml
deleted file mode 100644
index c2cccd0da6..0000000000
--- a/.codacy/codacy.yaml
+++ /dev/null
@@ -1,4 +0,0 @@
-runtimes:
-    - node@22.2.0
-tools:
-    - eslint@8.57.0
diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md
deleted file mode 100644
index 80925ee934..0000000000
--- a/.github/copilot-instructions.md
+++ /dev/null
@@ -1,165 +0,0 @@
-## Quick context for AI code contributors
-
-This repository contains ESMValTool — a community climate-model diagnostics and
-preprocessing framework. The goal of these notes is to give an AI agent the
-minimum, high-value knowledge to be productive immediately: how the project is
-structured, common developer workflows, and concrete examples to follow.
-
-### Quick setup (developer environment)
-
-- Prefer Conda/Mamba using the provided environment files at the repository root:
-
-```bash
-# create the dev environment (mamba recommended)
-mamba env create --name esmvaltool --file environment.yml
-conda activate esmvaltool
-# or use environment_python.yml for a lighter python-only env
-```
-
-- Docker: the repository includes `docker/Dockerfile`. Build from the repo root:
-
-```bash
-docker build -t esmvaltool:latest . -f docker/Dockerfile
-```
-
-- Installation for local development: pip install editable mode and tests extras
-
-```bash
-pip install -e .[test]
-pytest   # run tests (pytest options are configured in pyproject.toml)
-```
-
-### Where to run diagnostics / recipes
-
-- Recipes and diagnostics are described in the README and docs. Diagnostics
-  live under `esmvaltool/diag_scripts/`. Example diagnostic:
-  `esmvaltool/diag_scripts/weathertyping/weathertyping.py`.
-
-### Architecture & major components (short)
-
-- `esmvaltool/` — main package. Diagnostics and helper code live here.
-  - `diag_scripts/` — each diagnostic in its own sub-folder. Diagnostics are
-    Python scripts but may call NCL/R tools.
-  - `cmorizers/` — scripts to convert observational datasets to CMOR format.
-- `ESMValTool_stuff/`, `preval/` — user recipes, examples, and auxiliary
-  analysis code. Keep changes to these under separate branches unless they are
-  core fixes.
-- External integration: heavy reliance on `ESMValCore` (preprocessing), Iris
-  (`scitools-iris`) for cube handling, and many scientific packages (see
-  `pyproject.toml` and `environment.yml`). Changes to dataflow must respect
-  ESMValCore's preprocessing contracts.
-
-### Diagnostic implementation conventions (concrete patterns)
-
-- Entry pattern: diagnostics obtain config via the shared context manager:
-
-```python
-from esmvaltool.diag_scripts.shared import run_diagnostic
-
-if __name__ == '__main__':
-    with run_diagnostic() as config:
-        run_my_diagnostic(config)
-```
-
-Follow the `weathertyping` example: functions take the `cfg` dict returned by
-`run_diagnostic()` and extract preprocessor variables through helper functions
-in the same diagnostic package.
-
-- File I/O: diagnostics generally read/write netCDF files (Iris cubes). Use the
-  existing helper utilities in `diag_scripts/<name>/` (e.g., `wt_utils`,
-  `calc_utils`) instead of re-implementing low-level file-handling.
-
-### Testing, linting and formatting specifics
-
-- Tests: configured through `pyproject.toml`. After `pip install -e .[test]` run
-  `pytest`. The project uses 79-character line length.
-- Linting: `ruff` + `prospector` (see `pyproject.toml`). Prefer automatic fixes
-  where safe (the repo config sets `fix = true` for ruff).
-
-### Packaging and runtime notes to keep in mind
-
-- The package expects Python >= 3.11 (see `pyproject.toml`).
-- `esmvaltool/__init__.py` raises a helpful error if the package is not installed
-  editable — prefer `pip install -e .` for local imports and tests.
-- Many dependencies come from conda-forge and some (e.g., ESMPy) are not on
-  PyPI. For CI-like runs prefer the provided `environment.yml` or the Docker
-  image.
-
-### Helpful repository-specific search patterns for agents
-
-- Diagnostics: `esmvaltool/diag_scripts/**/` — look for `run_diagnostic` use.
-- Recipes/user examples: `ESMValTool_stuff/` and `ESMValTool_output/`.
-- Environment and build: `environment.yml`, `environment_python.yml`,
-  `docker/Dockerfile`, `pyproject.toml`.
-
-### What to change and what to avoid
-
-- Safe: small diagnostic improvements, refactors within a diagnostic folder,
-  tests for new behavior, documentation updates to README/docs snippets.
-- Avoid: large changes to preprocessing contracts, switching major dependency
-  versions, or edits to recipes in `ESMValTool_stuff/` that alter user data
-  provenance without discussing with maintainers.
-
-### Quick examples to cite in PRs
-
-- Point reviewers to `esmvaltool/diag_scripts/weathertyping/weathertyping.py`
-  as a model for diagnostic layout and `docker/Dockerfile` and
-  `environment.yml` for environment reproducibility.
-
-### When to ask a human
-
-- If a change affects: the public API of preprocessing (ESMValCore contracts),
-  Docker/CI pipelines, or requires new conda-build recipes. Also ask if you
-  need access to large datasets (CMIP/obs) that are not in the repo.
-
-If anything here is unclear or you'd like a shorter/longer variant, tell me
-which parts to expand and I'll iterate.
-
-
-# Copilot Agents for VSCode
-
-## Auto-Fix Codacy Minor Issues Agent
-
-This agent is designed to assist developers by automatically fixing **Codacy minor issues** in code, using GitHub Copilot suggestions within VSCode.
-
-### Overview
-
-- **Purpose:** Automatically detect and fix minor code quality issues flagged by Codacy.
-- **Scope:** Minor issues only (e.g., formatting, unused imports, variable naming, unnecessary semicolons).
-- **Toolchain:** VSCode + GitHub Copilot + Codacy CLI (optional for local linting).
-- **Trigger:** On file save or on-demand.
-
-### Setup
-
-1. **Install VSCode Extensions**
-   - [GitHub Copilot](https://marketplace.visualstudio.com/items?itemName=GitHub.copilot)
-   - [Codacy VSCode Extension](https://marketplace.visualstudio.com/items?itemName=Codacy.codacy)
-   - Optional: Linter/formatter for your language (e.g., ESLint, Prettier, Black).
-
-2. **Enable Copilot Suggestions**
-   - Go to VSCode `Settings` → `Copilot` → Enable inline suggestions.
-   - Enable `Accept Copilot suggestions on save` for automated workflow (optional, may require keybinding).
-
-3. **Codacy CLI (optional)**
-   - Install Codacy CLI:  
-     ```bash
-     npm install -g @codacy/cli
-     ```
-   - Run to check issues locally:  
-     ```bash
-     codacy analyze
-     ```
-
-### Workflow
-
-1. Open your project in VSCode.
-2. Open the file flagged with minor Codacy issues.
-3. Accept Copilot inline suggestions to automatically fix formatting, variable names, or other minor issues.
-4. Save the file to trigger any auto-formatters configured.
-5. Run Codacy analysis to ensure issues are resolved.
-
-### Example Use Cases
-
-- **Unused import:**
-  ```python
-  import os  # Minor issue: unused import
diff --git a/esmvaltool/diag_scripts/weathertyping/calc_utils.py b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
index 5dc1aea5de..43d65b0a80 100644
--- a/esmvaltool/diag_scripts/weathertyping/calc_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
@@ -1,21 +1,15 @@
 """Utility functions for calculations."""
 
-# operating system manipulations (e.g. path constructions)
 import json
 import logging
 import warnings
 from pathlib import Path
 from typing import Final
 
-# to manipulate iris cubes
 import iris
 import iris.analysis.cartography
-
-# general imports
 import numpy as np
 import pandas as pd
-
-# local imports
 from plot_utils import plot_corr_rmse_heatmaps, plot_maps
 from wt_utils import (
     check_mapping_dict_format,
diff --git a/esmvaltool/diag_scripts/weathertyping/plot_utils.py b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
index 60537981c3..86f1dd0830 100644
--- a/esmvaltool/diag_scripts/weathertyping/plot_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
@@ -1,26 +1,18 @@
 """Utility functions for plotting."""
 
-# operating system manipulations (e.g. path constructions)
 import logging
 import warnings
 from pathlib import Path
 
-# plotting imports
 import cartopy.crs as ccrs
 import cartopy.feature as cfeature
-
-# to manipulate iris cubes
 import iris
 import iris.analysis.cartography
 import iris.plot as iplt
 import matplotlib.pyplot as plt
 import matplotlib.ticker as mticker
-
-# general imports
 import numpy as np
 import seaborn as sns
-
-# local imports
 from cartopy.mpl.gridliner import LATITUDE_FORMATTER, LONGITUDE_FORMATTER
 from matplotlib.colors import ListedColormap
 from wt_utils import get_driver, get_provenance_record, log_provenance
diff --git a/esmvaltool/diag_scripts/weathertyping/weathertyping.py b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
index c0ca748f01..18f9bd298c 100644
--- a/esmvaltool/diag_scripts/weathertyping/weathertyping.py
+++ b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
@@ -7,7 +7,6 @@
 
 import iris
 
-# import internal esmvaltool modules here
 from esmvaltool.diag_scripts.shared import group_metadata, run_diagnostic
 from esmvaltool.diag_scripts.weathertyping.calc_utils import (
     calc_lwt_model,
diff --git a/esmvaltool/diag_scripts/weathertyping/wt_utils.py b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
index 5ac2eb7896..3eee1eec94 100644
--- a/esmvaltool/diag_scripts/weathertyping/wt_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
@@ -1,20 +1,15 @@
 """Utility functions for weathertyping script."""
 
-# operating system manipulations (e.g. path constructions)
 import json
 import logging
 import warnings
 from pathlib import Path
 
-# to manipulate iris cubes
 import iris
 import iris.analysis.cartography
-
-# general imports
 import numpy as np
 import pandas as pd
 
-# import internal esmvaltool modules here
 from esmvaltool.diag_scripts.shared import ProvenanceLogger
 
 iris.FUTURE.datum_support = True

From 1ae08c68e9374083d9a2c8898569d29c48eb08f2 Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Tue, 9 Dec 2025 08:34:16 +0100
Subject: [PATCH 30/57] change docstrings to numpy style

---
 .../weathertyping/weathertyping.py            | 86 ++++++++++++-------
 1 file changed, 54 insertions(+), 32 deletions(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/weathertyping.py b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
index 18f9bd298c..b652aae780 100644
--- a/esmvaltool/diag_scripts/weathertyping/weathertyping.py
+++ b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
@@ -40,11 +40,14 @@ def process_models_automatic_slwt(
 ):
     """Process model data for calculating Lamb and simplified weathertypes.
 
-    Args:
-    ----
-        cfg (dict): Nested dictionary of metadata
-        dataset_vars (list): List of variable dictionaries for a specific dataset
-        data_info (dict): Dictionary holding dataset information.
+    Parameters
+    ----------
+    cfg:
+        Nested dictionary of metadata
+    dataset_vars:
+        List of variable dictionaries for a specific dataset
+    data_info:
+        Dictionary holding dataset information.
     """
     for ensemble_var in dataset_vars:
         if ensemble_var.get("preprocessor") == "weathertype_preproc":
@@ -104,12 +107,16 @@ def process_era5_automatic_slwt(
 ):
     """Process ERA5 data for calculating Lamb and simplified weathertypes.
 
-    Args:
-    ----
-        data_info (dict): Dictionary holding dataset information.
-        preproc_variables_dict (dict): Dictionary holding preprocessed variables for all datasets.
-        cfg (dict): Nested dictionary of metadata
-        dataset_vars (list): List of variable dictionaries for a specific dataset
+    Parameters
+    ----------
+    data_info:
+        Dictionary holding dataset information.
+    preproc_variables_dict:
+        Dictionary holding preprocessed variables for all datasets.
+    cfg:
+        Nested dictionary of metadata
+    dataset_vars:
+        List of variable dictionaries for a specific dataset
     """
     wt_preproc, wt_preproc_prcp, wt_preproc_prcp_eobs = load_wt_preprocessors(
         data_info["dataset"],
@@ -176,9 +183,10 @@ def process_era5_automatic_slwt(
 def run_automatic_slwt(cfg: dict):
     """Run the automated calculation for simplified weathertypes and write to file, and plot the means and seasonal occurrence of the weathertypes.
 
-    Args:
-    ----
-        cfg (dict): Nested dictionary of metadata
+    Parameters
+    ----------
+    cfg:
+        Nested dictionary of metadata
     """
     preproc_variables_dict = group_metadata(
         cfg.get("input_data").values(),
@@ -202,15 +210,24 @@ def run_automatic_slwt(cfg: dict):
             process_models_automatic_slwt(cfg, dataset_vars, data_info)
 
 
-def process_era5_lwt(preproc_variables_dict, cfg, dataset_vars, data_info):
+def process_era5_lwt(
+    preproc_variables_dict: dict,
+    cfg: dict,
+    dataset_vars: list,
+    data_info: dict,
+):
     """Process ERA5 data for calculating Lamb weathertypes.
 
-    Args:
-    -------
-        preproc_variables_dict (dict): Dictionary holding preprocessed variables for all datasets.
-        cfg (dict): Nested dictionary of metadata
-        dataset_vars (list): List of variable dictionaries for a specific dataset
-        data_info (dict): Dictionary holding dataset information.
+    Parameters
+    ----------
+    preproc_variables_dict:
+        Dictionary holding preprocessed variables for all datasets.
+    cfg:
+        Nested dictionary of metadata
+    dataset_vars:
+        List of variable dictionaries for a specific dataset
+    data_info:
+        Dictionary holding dataset information.
     """
     wt_preproc, _, _ = load_wt_preprocessors(
         data_info["dataset"],
@@ -260,11 +277,14 @@ def process_era5_lwt(preproc_variables_dict, cfg, dataset_vars, data_info):
 def process_models_lwt(cfg: dict, dataset_vars: list, data_info: dict):
     """Process model data for calculating Lamb weathertypes.
 
-    Args:
-    -------
-        cfg (dict): Nested dictionary of metadata
-        dataset_vars (list): List of variable dictionaries for a specific dataset
-        data_info (dict): Dictionary holding dataset information.
+    Parameters
+    ----------
+    cfg:
+        Nested dictionary of metadata
+    dataset_vars:
+        List of variable dictionaries for a specific dataset
+    data_info:
+        Dictionary holding dataset information.
     """
     for ensemble_var in dataset_vars:
         if ensemble_var.get("preprocessor") == "weathertype_preproc":
@@ -320,9 +340,10 @@ def process_models_lwt(cfg: dict, dataset_vars: list, data_info: dict):
 def run_lwt(cfg: dict):
     """Run calculation of weathertypes. Write to file, and plot the means of psl, tas, and pr for each weathertype. Plot seasonal occurrence of the weathertypes.
 
-    Args:
-    -------
-        cfg (dict): Nested dictionary of metadata
+    Parameters
+    ----------
+    cfg:
+        Nested dictionary of metadata
     """
     preproc_variables_dict = group_metadata(
         cfg.get("input_data").values(),
@@ -350,9 +371,10 @@ def run_lwt(cfg: dict):
 def run_my_diagnostic(cfg: dict):
     """Run the weathertyping diagnostic.
 
-    Args:
-    -------
-        cfg (dict): Nested dictionary of metadata
+    Parameters
+    ----------
+    cfg:
+        Nested dictionary of metadata
     """
     automatic_slwt = cfg.get("automatic_slwt")
 

From a4c476d01dcda5b5cf464562b351bea2eac88bff Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Tue, 9 Dec 2025 09:01:41 +0100
Subject: [PATCH 31/57] change docstrings to numpy style

---
 .../diag_scripts/weathertyping/calc_utils.py  | 280 ++++++++++-------
 .../diag_scripts/weathertyping/plot_utils.py  | 113 ++++---
 .../weathertyping/weathertyping.py            |  52 ++--
 .../diag_scripts/weathertyping/wt_utils.py    | 285 +++++++++++-------
 4 files changed, 447 insertions(+), 283 deletions(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/calc_utils.py b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
index 43d65b0a80..21a645f2d9 100644
--- a/esmvaltool/diag_scripts/weathertyping/calc_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
@@ -56,14 +56,20 @@ def calc_slwt_obs(
 ) -> np.array:
     """Calculate simplified weathertypes for observational data.
 
-    Args:
-    ----
-        cfg (dict): Configuration dictionary from recipe
-        lwt (np.array): Array of Lamb WT
-        cube (iris.cube.Cube): Cube of psl data
-        dataset (str): Name of dataset
-        ancestors (list): List of ancestors
-        timerange (str): Time range for the calculation
+    Parameters
+    ----------
+    cfg
+        Configuration dictionary from recipe
+    lwt
+        Array of Lamb WT
+    cube
+        Cube of psl data
+    dataset
+        Name of dataset
+    ancestors
+        List of ancestors
+    timerange
+        Time range for the calculation
 
     Returns
     -------
@@ -136,7 +142,8 @@ def calc_const():
 
     Returns
     -------
-        tuple: The four constants needed for WT calculation.
+    tuple
+        The four constants needed for WT calculation.
     """
     const1 = 1 / np.cos(45 * np.pi / 180)
     const2 = np.sin(45 * np.pi / 180) / np.sin(40 * np.pi / 180)
@@ -154,12 +161,15 @@ def calc_westerly_flow(cube: iris.cube.Cube) -> np.array:
     scheme.
     Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
 
-    Args:
-        cube (iris.cube.Cube): Cube of psl data.
+    Parameters
+    ----------
+    cube
+        Cube of psl data.
 
     Returns
     -------
-        np.array: westerly flow
+    np.array
+        westerly flow
     """
     return 1 / 2 * (cube.data[:, 1, 2] + cube.data[:, 1, 4]) - 1 / 2 * (
         cube.data[:, 3, 2] + cube.data[:, 3, 4]
@@ -174,13 +184,17 @@ def calc_southerly_flow(cube: iris.cube.Cube, const1: float) -> np.array:
     scheme.
     Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
 
-    Args:
-        cube (iris.cube.Cube): cube of psl data
-        const1 (float): const1
+    Parameters
+    ----------
+    cube
+        cube of psl data
+    const1
+        const1
 
     Returns
     -------
-        np.array: southerly flow
+    np.array
+        southerly flow
     """
     return const1 * (
         1
@@ -203,13 +217,17 @@ def calc_resultant_flow(
     scheme.
     Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
 
-    Args:
-        westerly_flow (np.array): westerly flow
-        southerly_flow (np.array): southerly flow
+    Parameters
+    ----------
+    westerly_flow
+        westerly flow
+    southerly_flow
+        southerly flow
 
     Returns
     -------
-        np.array: resultant flow
+    np.array
+        resultant flow
     """
     return (southerly_flow**2 + westerly_flow**2) ** (1 / 2)
 
@@ -226,14 +244,19 @@ def calc_westerly_shear_velocity(
     scheme.
     Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
 
-    Args:
-        cube (iris.cube.Cube): cube of psl data
-        const2 (float): const2
-        const3 (float): const3
+    Parameters
+    ----------
+    cube
+        cube of psl data
+    const2
+        const2
+    const3
+        const3
 
     Returns
     -------
-        np.array: westerly shear velocity
+    np.array
+        westerly shear velocity
     """
     return const2 * (
         1 / 2 * (cube.data[:, 0, 2] + cube.data[:, 0, 4])
@@ -255,13 +278,17 @@ def calc_southerly_shear_velocity(
     scheme.
     Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
 
-    Args:
-        cube (iris.cube.Cube): cube of psl data
-        const4 (float): const
+    Parameters
+    ----------
+    cube
+        cube of psl data
+    const4
+        const
 
     Returns
     -------
-        np.array: southerly shear velocity
+    np.array
+        southerly shear velocity
     """
     return const4 * (
         1
@@ -290,13 +317,17 @@ def calc_total_shear_velocity(
     scheme.
     Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
 
-    Args:
-        westerly_shear_velocity (np.array): westerly shear velocity
-        southerly_shear_velocity (np.array): southerly shear velocity
+    Parameters
+    ----------
+    westerly_shear_velocity
+        westerly shear velocity
+    southerly_shear_velocity
+        southerly shear velocity
 
     Returns
     -------
-        np.array:total shear velocity
+    np.array
+        total shear velocity
     """
     return westerly_shear_velocity + southerly_shear_velocity
 
@@ -308,13 +339,15 @@ def lamp_pure_directional_type(
 ) -> int:
     """Calculate Lamp pure directional weathertype.
 
-    Args:
-    ----
-        direction (float): direction in degrees
+    Parameters
+    ----------
+    direction
+        direction in degrees
 
     Returns
     -------
-        int: Lamb weathertype
+    int
+        Lamb weathertype
     """
     if direction >= DIR_337_5 or direction < DIR_22_5:
         weathertypes[i] = 1
@@ -338,16 +371,18 @@ def lamp_synoptic_directional_type(
     direction: float,
     weathertypes: np.array,
     i: int,
-):
+) -> int:
     """Calculate Lamp synoptic/directional hybrid weathertype.
 
-    Args:
-    ----
-        direction (float): direction in degrees
+    Parameters
+    ----------
+    direction
+        direction in degrees
 
     Returns
     -------
-        int: Lamb weathertype
+    int
+        Lamb weathertype
     """
     if direction >= DIR_337_5 or direction < DIR_22_5:
         weathertypes[i] = 11
@@ -371,16 +406,18 @@ def lamb_synoptic_directional_type_zlt0(
     direction: float,
     weathertypes: np.array,
     i: int,
-):
+) -> int:
     """Calculate Lamb synoptic/directional hybrid weathertype with z <0.
 
-    Args:
-    ----
-        direction (float): direction in degrees
+    Parameters
+    ----------
+    direction
+        direction in degrees
 
     Returns
     -------
-        int: Lamb weathertype
+    int
+        Lamb weathertype
     """
     if direction >= DIR_337_5 or direction < DIR_22_5:
         weathertypes[i] = 19
@@ -408,14 +445,17 @@ def wt_algorithm(cube: iris.cube.Cube, dataset: str) -> np.array:
     scheme.
     Int. J. Climatol., 13: 655-663. https://doi.org/10.1002/joc.3370130606
 
-    Args:
-    ----
-        cube (iris.cube.Cube): Cube of psl data
-        dataset (str): Name of dataset
+    Parameters
+    ----------
+    cube
+        Cube of psl data
+    dataset
+        Name of dataset
 
     Returns
     -------
-        np.array: Lamb weathertypes
+    np.array
+        Lamb weathertypes
     """
     # lats and lons corresponding to datapoints
     # 55, 5 -> 1
@@ -508,13 +548,17 @@ def calc_lwt_slwt_model(
 ):
     """Calculate Lamb as well as simplified weathertypes for model and write them to file.
 
-    Args:
-    -------
-        cfg (dict): Configuration dictionary from recipe
-        cube (iris.cube.Cube): PSL field of dataset
-        data_info (dict): Dictionary with info to dataset
-        predefined_slwt (dict | None): If None, automatic_slwt will be used.
-            If dict, this mapping dict will be used.
+    Parameters
+    ----------
+    cfg
+        Configuration dictionary from recipe
+    cube
+        PSL field of dataset
+    data_info
+        Dictionary with info to dataset
+    predefined_slwt
+        If None, automatic_slwt will be used.
+        If dict, this mapping dict will be used.
             (see recipe option predefined_slwt)
     """
     driver = get_driver(data_info)
@@ -621,16 +665,20 @@ def process_prcp_mean(
 ) -> list:
     """Return which weathertypes can be grouped together for a certain precipitation pattern.
 
-    Args:
-    ----
-        cfg (dict): Configuration dictionary from recipe
-        data (np.array): Array of precipitation means for each WT
-        dataset (str): Name of dataset
-        timerange (str): Time range of dataset
-
+    Parameters
+    ----------
+    cfg
+        Configuration dictionary from recipe
+    data
+        Array of precipitation means for each WT
+    dataset
+        Name of dataset
+    timerange
+        Time range of dataset
     Returns
     -------
-        list: Selected pairs of WT. This is passed to get_mapping_dict
+    list
+        Selected pairs of WT. This is passed to get_mapping_dict
     """
     logger.info("Calculating corr and rsme matrices for %s", dataset)
 
@@ -681,12 +729,16 @@ def calc_wt_means(
 ):
     """Calculate means for psl, tas or pr for weathertypes.
 
-    Args:
-    ----
-        cfg (dict): Configuration dictionary from recipe
-        cube (iris.cube.Cube): Cube with variable data
-        wt_cubes (iris.cube.CubeList): List of cubes of lwt, slwt_ERA5 and slwt_EOBS
-        data_info (dict): Dictionary with info to dataset
+    Parameters
+    ----------
+    cfg
+        Configuration dictionary from recipe
+    cube
+        Cube with variable data
+    wt_cubes
+        List of cubes of lwt, slwt_ERA5 and slwt_EOBS
+    data_info
+        Dictionary with info to dataset
     """
     var_name = data_info.get("var")
     wt_string = data_info.get("wt_string")
@@ -744,21 +796,25 @@ def calc_wt_means(
     )
 
 
-def get_wt_array(wt_string: str, wt_cubes: iris.cube.CubeList) -> np.array:
+def get_wt_array(wt_string: str, wt_cubes: iris.cube.CubeList) -> tuple:
     """Get weathertype array and time coordinate based on wt_string.
 
-    Args:
-    ----
-        wt_string(str): string for weathertype selection
-        wt_cubes(iris.cube.CubeList): list of weathertype cubes
+    Parameters
+    ----------
+    wt_string
+        string for weathertype selection
+    wt_cubes
+        list of weathertype cubes
 
     Raises
     ------
-        NameError: if wt_array does not exist for the given wt_string
+    NameError
+        if wt_array does not exist for the given wt_string
 
     Returns
     -------
-        (np.array, np.array): weathertype array and time coordinate
+    tuple
+        weathertype array and time coordinate
     """
     if wt_string == "slwt_ERA5":
         slwt_era5_cube = wt_cubes[1]
@@ -789,12 +845,16 @@ def calc_wt_anomalies(
 ):
     """Calculate anomalies for psl, tas and pr for weathertypes.
 
-    Args:
-    ----
-        cfg (dict): Configuration dictionary from recipe
-        cube (iris.cube.Cube): Cube with variable data
-        wt_cubes (iris.cube.CubeList): List of cubes of lwt, slwt_ERA5 and slwt_EOBS
-        data_info (dict): Dictionary with info to dataset
+    Parameters
+    ----------
+    cfg
+        Configuration dictionary from recipe
+    cube
+        Cube with variable data
+    wt_cubes
+        List of cubes of lwt, slwt_ERA5 and slwt_EOBS
+    data_info
+        Dictionary with info to dataset
     """
     var_name = data_info.get("var_name")
     wt_string = data_info.get("wt_string")
@@ -862,12 +922,16 @@ def calc_wt_std(
 ):
     """Calculate standard deviation for psl, tas and pr for weathertypes.
 
-    Args:
-    ----
-        cfg (dict): Configuration dictionary from recipe
-        cube (iris.cube.Cube): Cube with variable data
-        wt_cubes (iris.cube.CubeList): List of cubes of lwt, slwt_ERA5 and slwt_EOBS
-        data_info (dict): Dictionary with info to dataset
+    Parameters
+    ----------
+    cfg
+        Configuration dictionary from recipe
+    cube
+        Cube with variable data
+    wt_cubes
+        List of cubes of lwt, slwt_ERA5 and slwt_EOBS
+    data_info
+        Dictionary with info to dataset
     """
     var_name = data_info.get("var_name")
     wt_string = data_info.get("wt_string")
@@ -927,11 +991,14 @@ def calc_wt_std(
 def calc_lwt_model(cfg: dict, cube: iris.cube.Cube, data_info: dict):
     """Calculate Lamb weathertypes for models.
 
-    Args:
-    ----
-        cfg (dict): Configuration dictionary from recipe
-        cube (iris.cube.Cube): Cube with variable data
-        data_info (dict): Dictionary with info to dataset
+    Parameters
+    ----------
+    cfg
+        Configuration dictionary from recipe
+    cube
+        Cube with variable data
+    data_info
+        Dictionary with info to dataset
     """
     driver = get_driver(data_info)
 
@@ -999,13 +1066,18 @@ def plot_means(
 ):
     """Plot means, anomalies and standard deviations.
 
-    Args:
-    ----
-        cfg (dict): Configuration dictionary from recipe
-        preproc_var (np.array): Preprocessed variable cube
-        wt_cubes (iris.cube.Cube): List of cubes of lwt, slwt_ERA5 and slwt_EOBS
-        data_info (dict): Dictionary with info to dataset
-        mode (str, optional): Mode of weathertype calculation, either "slwt" or "lwt". Defaults to "slwt".
+    Parameters
+    ----------
+    cfg
+        Configuration dictionary from recipe
+    preproc_var
+        Preprocessed variable cube
+    wt_cubes
+        List of cubes of lwt, slwt_ERA5 and slwt_EOBS
+    data_info
+        Dictionary with info to dataset
+    mode
+        Mode of weathertype calculation, either "slwt" or "lwt". Defaults to "slwt".
     """
     if mode == "slwt":
         data_info["wt_string"] = "lwt"
diff --git a/esmvaltool/diag_scripts/weathertyping/plot_utils.py b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
index 86f1dd0830..bb629ab9e1 100644
--- a/esmvaltool/diag_scripts/weathertyping/plot_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
@@ -28,13 +28,15 @@
 def generate_grayscale_hex_values(x):
     """Generate grayscale values for plotting seasonal occurences.
 
-    Args:
-    ----
-        x (int): Amount of weathertypes.
+    Parameters
+    ----------
+    x
+        Amount of weathertypes.
 
     Returns
     -------
-        list: List of grayscale hex values
+    list
+        List of grayscale hex values
     """
     grayscale_values = np.linspace(0, 1, x)
 
@@ -51,11 +53,14 @@ def plot_seasonal_occurrence(
 ):
     """Plot seasonal occurences of weathertypes.
 
-    Args:
-    ----
-        cfg (dict): Configuration dictionary from recipe
-        wt_cubes (iris.cube.Cube): List of cubes of lwt, slwt_ERA5 and slwt_EOBS
-        data_info (dict): Dictionary with info to dataset
+    Parameters
+    ----------
+    cfg
+        Configuration dictionary from recipe
+    wt_cubes
+        List of cubes of lwt, slwt_ERA5 and slwt_EOBS
+    data_info
+        Dictionary with info to dataset
     """
     driver = get_driver(data_info)
 
@@ -152,13 +157,15 @@ def plot_seasonal_occurrence(
 def set_gridlines(ax: plt.Axes):
     """Set gridlines for plotting maps.
 
-    Args:
-    ----
-        ax (plt.Axes): Axes object to draw gridlines on.
+    Parameters
+    ----------
+    ax
+        Axes object to draw gridlines on.
 
     Returns
     -------
-        gl (ax.gridlines): Gridlines object
+    gl : (ax.gridlines)
+        Gridlines object
     """
     gl = ax.gridlines(
         crs=ccrs.PlateCarree(),
@@ -191,16 +198,20 @@ def prepare_plot_title(
 ) -> str:
     """Return formatted plot title.
 
-    Args:
-    ----
-        data_info (dict): dict containing info on dataset
-        wt (int): weathertype
-        var_name (str): name of variable
-        mode (str): statistic used
-
+    Parameters
+    ----------
+    data_info
+        dict containing info on dataset
+    wt
+        weathertype
+    var_name
+        name of variable
+    mode
+        statistic used
     Returns
     -------
-        (str): title of plot
+    str
+        title of plot
     """
     ensemble = data_info.get("ensemble", "")
     timerange = data_info.get("timerange")
@@ -221,14 +232,17 @@ def prepare_plot_title(
 def get_unit(var_name: str, dataset: str) -> str:
     """Get unit of variables.
 
-    Args:
-    ----
-        var_name (str): name of variable
-        dataset (str): name of dataset
+    Parameters
+    ----------
+    var_name
+        name of variable
+    dataset
+        name of dataset
 
     Returns
     -------
-        (str): unit of variable
+    str
+        unit of variable
     """
     if var_name == "psl":
         return "[hPa]"
@@ -250,13 +264,18 @@ def plot_maps(
 ):
     """Plot maps of means, std and anomalies.
 
-    Args:
-    ----
-        wt (np.array): WT array
-        cfg (dict): Configuration dictionary from recipe
-        cube (iris.cube.Cube): Data to be plotted
-        data_info (dict): Dictionary with info to dataset
-        mode (str): Statistics that is used
+    Parameters
+    ----------
+    wt
+        WT array
+    cfg
+        Configuration dictionary from recipe
+    cube
+        Data to be plotted
+    data_info
+        Dictionary with info to dataset
+    mode
+        Statistics that is used
     """
     var_name = data_info.get("var")
 
@@ -338,13 +357,18 @@ def plot_corr_rmse_heatmaps(
 ):
     """Plot correlation and rmse heatmaps.
 
-    Args:
-    ----
-        cfg (dict): Configuration dictionary from recipe
-        pattern_correlation_matrix (np.array): Pattern correlation matrix
-        rmse_matrix (np.array): RMSE matrix
-        dataset (str): Name of dataset
-        timerange (str): Time range for the calculation
+    Parameters
+    ----------
+    cf
+        gConfiguration dictionary from recipe
+    pattern_correlation_matrix
+        Pattern correlation matrix
+    rmse_matrix
+        RMSE matrix
+    dataset
+        Name of dataset
+    timerange
+        Time range for the calculation
     """
     output_path = f"{cfg.get('plot_dir')}/heatmaps"
 
@@ -417,12 +441,15 @@ def plot_corr_rmse_heatmaps(
 def get_colormap(colormap_string: str) -> ListedColormap:
     """Get colormaps for plottings.
 
-    Args:
-        colormap_string (str): string to identify colormap
+    Parameters
+    ----------
+    colormap_string
+        string to identify colormap
 
     Returns
     -------
-        ListedColormap: Colormap for the specified variable
+    ListedColormap
+        Colormap for the specified variable
     """
     misc_seq_2_disc = [
         (230 / 255, 240 / 255, 240 / 255),
diff --git a/esmvaltool/diag_scripts/weathertyping/weathertyping.py b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
index b652aae780..c535a21849 100644
--- a/esmvaltool/diag_scripts/weathertyping/weathertyping.py
+++ b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
@@ -42,11 +42,11 @@ def process_models_automatic_slwt(
 
     Parameters
     ----------
-    cfg:
-        Nested dictionary of metadata
-    dataset_vars:
-        List of variable dictionaries for a specific dataset
-    data_info:
+    cfg
+        Nested dictionary of metadata.
+    dataset_vars
+        List of variable dictionaries for a specific dataset.
+    data_info
         Dictionary holding dataset information.
     """
     for ensemble_var in dataset_vars:
@@ -109,14 +109,14 @@ def process_era5_automatic_slwt(
 
     Parameters
     ----------
-    data_info:
+    data_info
         Dictionary holding dataset information.
-    preproc_variables_dict:
+    preproc_variables_dict
         Dictionary holding preprocessed variables for all datasets.
-    cfg:
-        Nested dictionary of metadata
-    dataset_vars:
-        List of variable dictionaries for a specific dataset
+    cfg
+        Nested dictionary of metadata.
+    dataset_vars
+        List of variable dictionaries for a specific dataset.
     """
     wt_preproc, wt_preproc_prcp, wt_preproc_prcp_eobs = load_wt_preprocessors(
         data_info["dataset"],
@@ -185,7 +185,7 @@ def run_automatic_slwt(cfg: dict):
 
     Parameters
     ----------
-    cfg:
+    cfg
         Nested dictionary of metadata
     """
     preproc_variables_dict = group_metadata(
@@ -220,13 +220,13 @@ def process_era5_lwt(
 
     Parameters
     ----------
-    preproc_variables_dict:
+    preproc_variables_dict
         Dictionary holding preprocessed variables for all datasets.
-    cfg:
-        Nested dictionary of metadata
-    dataset_vars:
-        List of variable dictionaries for a specific dataset
-    data_info:
+    cfg
+        Nested dictionary of metadata.
+    dataset_vars
+        List of variable dictionaries for a specific dataset.
+    data_info
         Dictionary holding dataset information.
     """
     wt_preproc, _, _ = load_wt_preprocessors(
@@ -279,11 +279,11 @@ def process_models_lwt(cfg: dict, dataset_vars: list, data_info: dict):
 
     Parameters
     ----------
-    cfg:
-        Nested dictionary of metadata
-    dataset_vars:
-        List of variable dictionaries for a specific dataset
-    data_info:
+    cfg
+        Nested dictionary of metadata.
+    dataset_vars
+        List of variable dictionaries for a specific dataset.
+    data_info
         Dictionary holding dataset information.
     """
     for ensemble_var in dataset_vars:
@@ -342,8 +342,8 @@ def run_lwt(cfg: dict):
 
     Parameters
     ----------
-    cfg:
-        Nested dictionary of metadata
+    cfg
+        Nested dictionary of metadata.
     """
     preproc_variables_dict = group_metadata(
         cfg.get("input_data").values(),
@@ -373,7 +373,7 @@ def run_my_diagnostic(cfg: dict):
 
     Parameters
     ----------
-    cfg:
+    cfg
         Nested dictionary of metadata
     """
     automatic_slwt = cfg.get("automatic_slwt")
diff --git a/esmvaltool/diag_scripts/weathertyping/wt_utils.py b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
index 3eee1eec94..cb74c64cbc 100644
--- a/esmvaltool/diag_scripts/weathertyping/wt_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
@@ -23,28 +23,33 @@
 def get_driver(data_info: dict) -> str:
     """Get driving model name and string for further use.
 
-    Args:
-    ----
-        data_info (dict): Data information dictionary.
+    Parameters
+    ----------
+    data_info
+        Data information dictionary.
 
     Returns
     -------
-        str: Driver string with leading underscore or empty string.
+    str:
+        Driver string with leading underscore or empty string.
     """
     return data_info.get("driver", "")
 
 
-def load_wt_preprocessors(dataset: str, preproc_variables_dict: dict):
+def load_wt_preprocessors(dataset: str, preproc_variables_dict: dict) -> tuple:
     """Load preprocessor cubes for calculating Lamb weathertypes.
 
-    Args:
-    ----
-        dataset (str): Name of dataset
-        preproc_variables_dict (dict): Dictionary with info on preprocessor variables
+    Parameters
+    ----------
+    dataset
+        Name of dataset
+    preproc_variables_dict
+        Dictionary with info on preprocessor variables
 
     Returns
     -------
-        tuple: Preprocessor cubes for weathertyping
+    tuple
+        Preprocessor cubes for weathertyping
     """
     wt_preproc = iris.load_cube(
         preproc_variables_dict.get(dataset)[0].get("filename"),
@@ -66,17 +71,22 @@ def load_wt_preprocessors(dataset: str, preproc_variables_dict: dict):
     return wt_preproc, wt_preproc_prcp, wt_preproc_prcp_eobs
 
 
-def get_ancestors_era5_eobs(dataset: str, preproc_variables_dict: dict):
+def get_ancestors_era5_eobs(
+    dataset: str, preproc_variables_dict: dict
+) -> tuple:
     """Get ancestors for observational data.
 
-    Args:
-    ----
-        dataset (str): Name of dataset
-        preproc_variables_dict (dict): Dictionary with info on preprocessor variables
+    Parameters
+    ----------
+    dataset
+        Name of dataset
+    preproc_variables_dict
+        Dictionary with info on preprocessor variables
 
     Returns
     -------
-        tuple: Lists of ERA5 and E-OBS ancestors
+    tuple
+        Lists of ERA5 and E-OBS ancestors
     """
     era5_ancestors = [
         preproc_variables_dict.get(dataset)[0].get("filename"),
@@ -96,17 +106,20 @@ def get_ancestors_era5_eobs(dataset: str, preproc_variables_dict: dict):
     return era5_ancestors, eobs_ancestors
 
 
-def get_model_output_filepath(dataset: str, data_info: list):
+def get_model_output_filepath(dataset: str, data_info: list) -> tuple:
     """Get output filepaths for models.
 
-    Args:
-    ----
-        dataset (str): Name of dataset
-        data_info (list): Model variables
+    Parameters
+    ----------
+    dataset
+        Name of dataset
+    data_info
+        Model variables
 
     Returns
     -------
-        tuple: Output filepath and preprocessor path for future referencing.
+    tuple
+        Output filepath and preprocessor path for future referencing.
     """
     timerange = data_info.get("timerange").replace("/", "-")
     experiment = data_info.get("exp")
@@ -122,13 +135,15 @@ def get_model_output_filepath(dataset: str, data_info: list):
 def get_preproc_lists(preproc_vars: list):
     """Put preprocessors and paths into lists for further use.
 
-    Args:
-    ----
-        preproc_vars (list): List of preprocessor variables.
+    Parameters
+    ----------
+    preproc_vars
+        List of preprocessor variables.
 
     Returns
     -------
-        tuple: Preprocessor cubes and paths.
+    tuple
+        Preprocessor cubes and paths.
     """
     preproc_path_psl = preproc_vars[-3].get("filename")
     preproc_path_prcp = preproc_vars[-2].get("filename")
@@ -147,13 +162,15 @@ def get_preproc_lists(preproc_vars: list):
 def get_preproc_lists_ensemble(preproc_vars: list):
     """Put preprocessors and paths into lists for further use.
 
-    Args:
-    ----
-        preproc_vars (list): List of preprocessor variables.
+    Parameters
+    ----------
+    preproc_vars
+        List of preprocessor variables.
 
     Returns
     -------
-        tuple: Preprocessor cubes and paths.
+    tuple
+        Preprocessor cubes and paths.
     """
     preproc_path = preproc_vars.get("filename")
 
@@ -165,13 +182,15 @@ def get_preproc_lists_ensemble(preproc_vars: list):
 def get_looping_dict(preproc_vars: list):
     """Put variable preprocessors into dict for looping.
 
-    Args:
-    ----
-        preproc_vars (list): List of preprocessor variables.
+    Parameters
+    ----------
+    preproc_vars
+        List of preprocessor variables.
 
     Returns
     -------
-        dict: Dictionary of preprocessor cubes and paths.
+    dict
+        Dictionary of preprocessor cubes and paths.
     """
     preproc, preproc_path = get_preproc_lists(preproc_vars)
 
@@ -185,14 +204,17 @@ def get_looping_dict(preproc_vars: list):
 def load_wt_files(path: str, mode="slwt"):
     """Load wt files.
 
-    Args:
-    ----
-        path (str): Path of wt file
-        mode (str, optional): Type of weathertype to load. Defaults to "slwt".
+    Parameters
+    ----------
+    path
+        Path of wt file
+    mode
+        Type of weathertype to load. Defaults to "slwt".
 
     Returns
     -------
-        list: List of weathertype cubes.
+    list
+        List of weathertype cubes.
     """
     if mode == "slwt":
         lwt_cube = iris.load_cube(path, "lwt")
@@ -218,17 +240,23 @@ def get_provenance_record(
 ) -> dict:
     """Get provenance record.
 
-    Args:
-    ----
-        caption (str): Caption of plot
-        ancestors (list): List of ancestor plots
-        long_names (list): List of variable long names
-        plot_types (list | None): Type of plot
-        statistics (list | None): Types of statistics used
+    Parameters
+    ----------
+    caption
+        Caption of plot
+    ancestors
+        List of ancestor plots
+    long_names
+        List of variable long names
+    plot_types
+        Type of plot
+    statistics
+        Types of statistics used
 
     Returns
     -------
-        dict: Provenance record
+    dict
+        Provenance record
     """
     record = {
         "caption": caption,
@@ -249,11 +277,14 @@ def get_provenance_record(
 def log_provenance(filename: str, cfg: dict, provenance_record: dict):
     """Log provenance.
 
-    Args:
-    ----
-        filename (str): Filename of xml file
-        cfg (dict): Configuration dictionary
-        provenance_record (dict): Provenance record dictionary
+    Parameters
+    ----------
+    filename:
+        Filename of xml file
+    cfg
+        Configuration dictionary
+    provenance_record
+        Provenance record dictionary
     """
     with ProvenanceLogger(cfg) as provenance_logger:
         provenance_logger.log(filename, provenance_record)
@@ -267,13 +298,15 @@ def log_provenance(filename: str, cfg: dict, provenance_record: dict):
 def turn_list_to_mapping_dict(list_: list) -> dict:
     """Turn list of combined WT to a dictionary for further use.
 
-    Args:
-    ----
-        list_ (list): List of combined WTs
+    Parameters
+    ----------
+    list_
+        List of combined WTs
 
     Returns
     -------
-        dict: Dictionary of combined WTs
+    dict
+        Dictionary of combined WTs
     """
     result_dict = {}
 
@@ -290,13 +323,15 @@ def turn_list_to_mapping_dict(list_: list) -> dict:
 def get_mapping_dict(selected_pairs: list) -> dict:
     """Get mapping dictionary from list of selected pairs.
 
-    Args:
-    ----
-        selected_pairs (list): List of selected weathertype pairs
+    Parameters
+    ----------
+    selected_pairs
+        List of selected weathertype pairs
 
     Returns
     -------
-        dict: Mapping dictionary
+    dict
+        Mapping dictionary
     """
     mapping_array = [elem[0] for elem in selected_pairs]
 
@@ -318,11 +353,14 @@ def find_intersection(m_list: list) -> list:
 def write_mapping_dict(work_dir: str, dataset: str, mapping_dict: dict):
     """Write mapping dictionary to file.
 
-    Args:
-    ----
-        work_dir (str): Current working directory
-        dataset (str): Dataset name
-        mapping_dict (dict): Mapping dictionary in {lwt: slwt, ...} format
+    Parameters
+    ----------
+    work_dir
+        Current working directory
+    dataset
+        Dataset name
+    mapping_dict
+        Mapping dictionary in {lwt: slwt, ...} format
     """
     mapping_dict_reformat = convert_dict(mapping_dict)
 
@@ -335,13 +373,15 @@ def write_mapping_dict(work_dir: str, dataset: str, mapping_dict: dict):
 def convert_dict(dict_: dict) -> dict:
     """Convert dictionary from {lwt: slwt, ...} format to {slwt: [lwt1, lwt2], ...}.
 
-    Args:
-    ----
-        dict_ (dict): Mapping dictionary to be converted
+    Parameters
+    ----------
+    dict_
+        Mapping dictionary to be converted
 
     Returns
     -------
-        dict: Converted dictionary
+    dict
+        Converted dictionary
     """
     new_dict = {}
     for dataset, value in dict_.items():
@@ -351,20 +391,23 @@ def convert_dict(dict_: dict) -> dict:
     return new_dict
 
 
-def reverse_convert_dict(dict_: dict) -> dict:
+def reverse_convert_dict(originial_dict: dict) -> dict:
     """Convert mapping dictionary.
 
     From {slwt: [lwt1, lwt2], ...} format to {lwt: slwt, ...}.
 
-    Args:
-        original_dict (dict): Dict in the {slwt: [lwt1, lwt2], ...} format
+    Parameters
+    ----------
+    original_dict
+        Dict in the {slwt: [lwt1, lwt2], ...} format
 
     Returns
     -------
-        dict: Dict in the  format {lwt: slwt, ...}
+    dict
+        Dict in the  format {lwt: slwt, ...}
     """
     new_dict = {}
-    for key, value_list in dict_.items():
+    for key, value_list in originial_dict.items():
         for original_key in value_list:
             new_dict[original_key] = key
     return new_dict
@@ -378,12 +421,16 @@ def write_corr_rmse_to_csv(
 ):
     """Write correlation and rsme matrix to csv files.
 
-    Args:
-    ----
-        cfg (dict): Configuration dictionary from recipe
-        pattern_correlation_matrix (np.array): Correlation matrix
-        rmse_matrix (np.array): RSME matrix
-        dataset (str): Name of dataset
+    Parameters
+    ----------
+    cfg
+        Configuration dictionary from recipe
+    pattern_correlation_matrix
+        Correlation matrix
+    rmse_matrix
+        RSME matrix
+    dataset
+        Name of dataset
     """
     logger.info("Writing corr and rsme matrices for %s", dataset)
 
@@ -414,17 +461,22 @@ def run_predefined_slwt(
 ):
     """Run predefined slwt mapping.
 
-    Args:
-    ----
-        work_dir (str): Working directory to save mapping dict
-        dataset_name (str): Name of dataset
-        lwt (np.array): lwt array
-        predefined_slwt (dict): Mapping dictionary in {lwt: slwt, ...} format
-
+    Parameters
+    ----------
+    work_dir
+        Working directory to save mapping dict
+    dataset_name
+        Name of dataset
+    lwt
+        lwt array
+    predefined_slwt
+        Mapping dictionary in {lwt: slwt, ...} format
     Returns
     -------
-        np.array: slwt_era5 array
-        np.array: slwt_eobs array
+    np.array
+        slwt_era5 array
+    np.array
+        slwt_eobs array
     """
     predefined_slwt = check_mapping_dict_format(predefined_slwt)
     write_mapping_dict(work_dir, dataset_name, predefined_slwt)
@@ -443,12 +495,16 @@ def combine_wt_to_file(
 ):
     """Combine lwt and slwt arrays to one file.
 
-    Args:
-    ----
-        cfg (dict): Configuration dictionary from recipe
-        wt_list (list): List of weathertype arrays
-        cube (iris.cube.Cube): Cube of data to keep time coordinate
-        file_name (str): Name of output file
+    Parameters
+    ----------
+    cfg
+        Configuration dictionary from recipe
+    wt_list
+        List of weathertype arrays
+    cube
+        Cube of data to keep time coordinate
+    file_name
+        Name of output file
     """
     lwt = wt_list[0]
     slwt_era5 = wt_list[1]
@@ -491,12 +547,16 @@ def write_lwt_to_file(
 ):
     """Write only lwt to file.
 
-    Args:
-    ----
-        cfg (dict): Configuration dictionary from recipe
-        lwt (np.array): lwt array
-        cube (iris.cube.Cube): Cube of data to keep time coordinate
-        file_name (str): Name of output file
+    Parameters
+    ----------
+    cfg
+        Configuration dictionary from recipe
+    lwt
+        lwt array
+    cube
+        Cube of data to keep time coordinate
+    file_name
+        Name of output file
     """
     logger.info("Writing Lamb Weathertype to %s", file_name)
 
@@ -520,14 +580,17 @@ def write_lwt_to_file(
 def map_lwt_to_slwt(lwt: np.array, mapping_dict: dict) -> np.array:
     """Map lwt array to slwt array.
 
-    Args:
-    ----
-        lwt (np.array): lwt array
-        mapping_dict (dict): Mapping dictionary in {lwt: slwt, ...} format
+    Parameters
+    ----------
+    lwt
+        lwt array
+    mapping_dict
+        Mapping dictionary in {lwt: slwt, ...} format
 
     Returns
     -------
-        np.array: array of slwt
+    np.array
+        array of slwt
     """
     return np.array([np.int8(mapping_dict.get(value, 0)) for value in lwt])
 
@@ -535,13 +598,15 @@ def map_lwt_to_slwt(lwt: np.array, mapping_dict: dict) -> np.array:
 def check_mapping_dict_format(mapping_dict: dict) -> dict:
     """Check format of mapping dict and return in {lwt: slwt, ...} format.
 
-    Args:
-    ----
-        mapping_dict (dict): mapping dict in any format
+    Parameters
+    ----------
+    mapping_dict
+        mapping dict in any format
 
     Returns
     -------
-        dict: mapping dict in {lwt: slwt, ...} format
+    dict
+        mapping dict in {lwt: slwt, ...} format
     """
     if isinstance(mapping_dict[next(iter(mapping_dict))], list):
         return reverse_convert_dict(mapping_dict)

From 81ec1bda6bd794220b9c992cf7d8c803a852a3b1 Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Tue, 9 Dec 2025 09:06:28 +0100
Subject: [PATCH 32/57] add blank lines

---
 esmvaltool/diag_scripts/weathertyping/calc_utils.py | 1 +
 esmvaltool/diag_scripts/weathertyping/plot_utils.py | 1 +
 esmvaltool/diag_scripts/weathertyping/wt_utils.py   | 4 +++-
 3 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/calc_utils.py b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
index 21a645f2d9..4a6555ddb5 100644
--- a/esmvaltool/diag_scripts/weathertyping/calc_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
@@ -675,6 +675,7 @@ def process_prcp_mean(
         Name of dataset
     timerange
         Time range of dataset
+
     Returns
     -------
     list
diff --git a/esmvaltool/diag_scripts/weathertyping/plot_utils.py b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
index bb629ab9e1..949431a9a9 100644
--- a/esmvaltool/diag_scripts/weathertyping/plot_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
@@ -208,6 +208,7 @@ def prepare_plot_title(
         name of variable
     mode
         statistic used
+
     Returns
     -------
     str
diff --git a/esmvaltool/diag_scripts/weathertyping/wt_utils.py b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
index cb74c64cbc..1f46f16d10 100644
--- a/esmvaltool/diag_scripts/weathertyping/wt_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
@@ -72,7 +72,8 @@ def load_wt_preprocessors(dataset: str, preproc_variables_dict: dict) -> tuple:
 
 
 def get_ancestors_era5_eobs(
-    dataset: str, preproc_variables_dict: dict
+    dataset: str,
+    preproc_variables_dict: dict,
 ) -> tuple:
     """Get ancestors for observational data.
 
@@ -471,6 +472,7 @@ def run_predefined_slwt(
         lwt array
     predefined_slwt
         Mapping dictionary in {lwt: slwt, ...} format
+
     Returns
     -------
     np.array

From 18e109c61c6e720aeeca53d117611fb3507da685 Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Thu, 11 Dec 2025 09:02:08 +0100
Subject: [PATCH 33/57] latest review fixes/changes

---
 .../diag_scripts/weathertyping/calc_utils.py  | 35 ++++++++------
 .../weathertyping/weathertyping.py            |  4 +-
 .../diag_scripts/weathertyping/wt_utils.py    | 48 +++++++++++++------
 3 files changed, 55 insertions(+), 32 deletions(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/calc_utils.py b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
index 4a6555ddb5..e77369f2c7 100644
--- a/esmvaltool/diag_scripts/weathertyping/calc_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
@@ -6,6 +6,7 @@
 from pathlib import Path
 from typing import Final
 
+import dask.array as da
 import iris
 import iris.analysis.cartography
 import numpy as np
@@ -80,8 +81,8 @@ def calc_slwt_obs(
 
     wt_data_prcp = []
     for wt_ in range(1, 28):
-        target_indices = np.where(lwt == wt_)
-        if len(target_indices[0]) < 1:
+        target_indices = da.where(lwt == wt_).compute()
+        if not target_indices[0]:
             logger.info(
                 "calc_slwt_obs - CAUTION: Skipped wt %s \
                 for dataset %s!",
@@ -100,7 +101,15 @@ def calc_slwt_obs(
                 iris.Constraint(time=lambda t, d=dates: t.point in d[0]),
             )
         wt_cube_mean = extracted_cube.collapsed("time", iris.analysis.MEAN)
-        wt_data_prcp.append(wt_cube_mean.data.compressed())
+
+        data = wt_cube_mean.data
+
+        if isinstance(data, np.ma.MaskedArray):
+            wt_data_prcp.append(data.compressed())
+        else:
+            # if not MaskedArray, flatten should be fine
+            wt_data_prcp.append(data.flatten())
+
     selected_pairs = process_prcp_mean(
         cfg,
         wt_data_prcp,
@@ -372,7 +381,7 @@ def lamp_synoptic_directional_type(
     weathertypes: np.array,
     i: int,
 ) -> int:
-    """Calculate Lamp synoptic/directional hybrid weathertype.
+    """Calculate Lamb synoptic/directional hybrid weathertype.
 
     Parameters
     ----------
@@ -696,11 +705,7 @@ def process_prcp_mean(
                 "correlation_threshold",
             ) and rmse_matrix[i][j] <= cfg.get("rmse_threshold"):
                 selected_pairs.append(
-                    (
-                        (i + 1, j + 1),
-                        pattern_correlation_matrix[i][j],
-                        rmse_matrix[i][j],
-                    ),
+                    ((i + 1, j + 1),),
                 )
 
     # write matrices to csv
@@ -755,7 +760,7 @@ def calc_wt_means(
     wt_array, tcoord = get_wt_array(wt_string, wt_cubes)
 
     for wt in range(1, len(np.unique(wt_array)) + 1):
-        target_indices = np.where(wt_array == wt)
+        target_indices = da.where(wt_array == wt).compute()
         if len(target_indices[0]) < 1:
             logger.info(
                 "calc_wt_means - CAUTION: Skipped %s %s \
@@ -835,7 +840,7 @@ def get_wt_array(wt_string: str, wt_cubes: iris.cube.CubeList) -> tuple:
             error_str,
         )
 
-    return wt_array, tcoord
+    return wt_array.core_data(), tcoord
 
 
 def calc_wt_anomalies(
@@ -870,8 +875,8 @@ def calc_wt_anomalies(
     wt_array, tcoord = get_wt_array(wt_string, wt_cubes)
 
     for wt in range(1, len(np.unique(wt_array)) + 1):
-        target_indices = np.where(wt_array == wt)
-        if len(target_indices[0]) < 1:
+        target_indices = da.where(wt_array == wt).compute()
+        if not target_indices[0]:
             logger.info(
                 "calc_wt_anomalies - CAUTION: Skipped wt %s \
                 for dataset %s!",
@@ -947,8 +952,8 @@ def calc_wt_std(
     wt_array, tcoord = get_wt_array(wt_string, wt_cubes)
 
     for wt in range(1, len(np.unique(wt_array)) + 1):
-        target_indices = np.where(wt_array == wt)
-        if len(target_indices[0]) < 1:
+        target_indices = da.where(wt_array == wt).compute()
+        if not target_indices[0]:
             logger.info(
                 "calc_slwt_obs - CAUTION: Skipped wt %s \
                 for dataset %s!",
diff --git a/esmvaltool/diag_scripts/weathertyping/weathertyping.py b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
index c535a21849..50fd47d42d 100644
--- a/esmvaltool/diag_scripts/weathertyping/weathertyping.py
+++ b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
@@ -368,7 +368,7 @@ def run_lwt(cfg: dict):
             process_models_lwt(cfg, dataset_vars, data_info)
 
 
-def run_my_diagnostic(cfg: dict):
+def run_weathertyping_diagnostic(cfg: dict):
     """Run the weathertyping diagnostic.
 
     Parameters
@@ -392,4 +392,4 @@ def run_my_diagnostic(cfg: dict):
 if __name__ == "__main__":
     with run_diagnostic() as config:
         # main function for running the diagnostic
-        run_my_diagnostic(config)
+        run_weathertyping_diagnostic(config)
diff --git a/esmvaltool/diag_scripts/weathertyping/wt_utils.py b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
index 1f46f16d10..590fe5b794 100644
--- a/esmvaltool/diag_scripts/weathertyping/wt_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
@@ -64,7 +64,9 @@ def load_wt_preprocessors(dataset: str, preproc_variables_dict: dict) -> tuple:
             preproc_variables_dict.get("E-OBS")[0].get("filename"),
         )
     except (IndexError, KeyError, TypeError):
-        print("ERA5 precipitation preprocessor not found for automatic slwt.")
+        logger.info(
+            "ERA5 precipitation preprocessor not found for automatic slwt."
+        )
         wt_preproc_prcp = None
         wt_preproc_prcp_eobs = None
 
@@ -101,7 +103,9 @@ def get_ancestors_era5_eobs(
             preproc_variables_dict.get("E-OBS")[0].get("filename"),
         ]
     except (IndexError, KeyError, TypeError):
-        print("No ancestors for ERA5 and E-OBS precipitation preprocessor.")
+        logger.info(
+            "No ancestors for ERA5 and E-OBS precipitation preprocessor."
+        )
         eobs_ancestors = []
 
     return era5_ancestors, eobs_ancestors
@@ -315,8 +319,6 @@ def turn_list_to_mapping_dict(list_: list) -> dict:
         for elem in s:
             if elem not in result_dict:
                 result_dict[elem] = i + 1
-            else:
-                result_dict[elem].append(i)
 
     return result_dict
 
@@ -334,21 +336,37 @@ def get_mapping_dict(selected_pairs: list) -> dict:
     dict
         Mapping dictionary
     """
-    mapping_array = [elem[0] for elem in selected_pairs]
 
-    s = [set(i) for i in mapping_array if i]
+    # selected pairs is of form [(wt1, wt2), (wt3, wt4), ...]
+    sets_list = [set(i) for i in selected_pairs if i]
+
+    # 3 loops to check all sets against each other until no more merges happen
+    # merged_flag indicates if a merge happened in the last full pass -> we need to restart then
+    merged_flag = True
+
+    while merged_flag:
+        merged_flag = False
+        i = 0
+
+        # check each set against each other
+        while i < len(sets_list):
+            set1 = sets_list[i]
+            j = i + 1
+
+            while j < len(sets_list):
+                set2 = sets_list[j]
 
-    def find_intersection(m_list: list) -> list:
-        for i, v in enumerate(m_list):
-            for j, k in enumerate(m_list[i + 1 :], i + 1):
-                if v & k:
-                    s[i] = v.union(m_list.pop(j))
-                    return find_intersection(m_list)
-        return m_list
+                if set1.isdisjoint(set2) is False:
+                    # this means there is an overlap -> merge sets
+                    set1.update(set2)
+                    sets_list.pop(j)
+                    merged_flag = True
+                else:
+                    j += 1
 
-    merged_tuples = find_intersection(s)
+            i += 1
 
-    return turn_list_to_mapping_dict(merged_tuples)
+    return turn_list_to_mapping_dict(sets_list)
 
 
 def write_mapping_dict(work_dir: str, dataset: str, mapping_dict: dict):

From 1335bdc6c6f749fb4f153a76802fa1233a1597a0 Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Thu, 11 Dec 2025 09:05:43 +0100
Subject: [PATCH 34/57] trailing comma

---
 esmvaltool/diag_scripts/weathertyping/wt_utils.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/wt_utils.py b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
index 590fe5b794..7a83ec091d 100644
--- a/esmvaltool/diag_scripts/weathertyping/wt_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
@@ -65,7 +65,7 @@ def load_wt_preprocessors(dataset: str, preproc_variables_dict: dict) -> tuple:
         )
     except (IndexError, KeyError, TypeError):
         logger.info(
-            "ERA5 precipitation preprocessor not found for automatic slwt."
+            "ERA5 precipitation preprocessor not found for automatic slwt.",
         )
         wt_preproc_prcp = None
         wt_preproc_prcp_eobs = None
@@ -104,7 +104,7 @@ def get_ancestors_era5_eobs(
         ]
     except (IndexError, KeyError, TypeError):
         logger.info(
-            "No ancestors for ERA5 and E-OBS precipitation preprocessor."
+            "No ancestors for ERA5 and E-OBS precipitation preprocessor.",
         )
         eobs_ancestors = []
 
@@ -336,7 +336,6 @@ def get_mapping_dict(selected_pairs: list) -> dict:
     dict
         Mapping dictionary
     """
-
     # selected pairs is of form [(wt1, wt2), (wt3, wt4), ...]
     sets_list = [set(i) for i in selected_pairs if i]
 

From d91df5057f2c1f886eb23c5907ca0fdaea632199 Mon Sep 17 00:00:00 2001
From: thomaskroi1996 <131807984+thomaskroi1996@users.noreply.github.com>
Date: Wed, 17 Dec 2025 11:27:36 +0100
Subject: [PATCH 35/57] Update esmvaltool/references/maraun21jgr.bibtex

Co-authored-by: Bettina Gier <gier@uni-bremen.de>
---
 esmvaltool/references/maraun21jgr.bibtex | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/esmvaltool/references/maraun21jgr.bibtex b/esmvaltool/references/maraun21jgr.bibtex
index 613e5d1bb9..d160c9e78f 100644
--- a/esmvaltool/references/maraun21jgr.bibtex
+++ b/esmvaltool/references/maraun21jgr.bibtex
@@ -1,4 +1,4 @@
-@article{https://doi.org/10.1029/2020JD032824,
+@article{maraun21jgr,
     author = {Maraun, Douglas and Truhetz, Heimo and Schaffer, Armin},
     title = {Regional Climate Model Biases, Their Dependence on Synoptic Circulation Biases and the Potential for Bias Adjustment: A Process-Oriented Evaluation of the Austrian Regional Climate Projections},
     journal = {Journal of Geophysical Research: Atmospheres},

From dc7e954a8887deab129009531672d1231f45abf4 Mon Sep 17 00:00:00 2001
From: thomaskroi1996 <131807984+thomaskroi1996@users.noreply.github.com>
Date: Wed, 17 Dec 2025 11:28:08 +0100
Subject: [PATCH 36/57] Update
 doc/sphinx/source/recipes/recipe_weathertyping.rst

Co-authored-by: Bettina Gier <gier@uni-bremen.de>
---
 doc/sphinx/source/recipes/recipe_weathertyping.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/sphinx/source/recipes/recipe_weathertyping.rst b/doc/sphinx/source/recipes/recipe_weathertyping.rst
index e36f2afff6..80c2f73684 100755
--- a/doc/sphinx/source/recipes/recipe_weathertyping.rst
+++ b/doc/sphinx/source/recipes/recipe_weathertyping.rst
@@ -111,4 +111,4 @@ Example plots
 .. figure::  /recipes/figures/weathertyping/ERA5__lwt_rel_occurrence_1958-2014.png
    :align:   center
 
-   Stackplot of seasonal relative occureences of each WT for ERA5.
+   Stackplot of seasonal relative occurrences of each WT for ERA5.

From 70d34e43874375186a4e77ca594ca08e5a1d5557 Mon Sep 17 00:00:00 2001
From: thomaskroi1996 <131807984+thomaskroi1996@users.noreply.github.com>
Date: Wed, 17 Dec 2025 11:28:30 +0100
Subject: [PATCH 37/57] Update esmvaltool/config-references.yml

Co-authored-by: Bettina Gier <gier@uni-bremen.de>
---
 esmvaltool/config-references.yml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/esmvaltool/config-references.yml b/esmvaltool/config-references.yml
index 1710804fcf..2e9586c21f 100644
--- a/esmvaltool/config-references.yml
+++ b/esmvaltool/config-references.yml
@@ -363,7 +363,6 @@ authors:
   kroissenbrunner_thomas:
     name: Kroissenbrunner, Thomas
     institute: WEGC, Austria
-    email: thomas.kroissenbrunner@uni-graz.at
     github: thomaskroi1996
     orcid:
   kuehbacher_birgit:

From f46b7f634f19baf46683307f4d49d1a440c5be59 Mon Sep 17 00:00:00 2001
From: thomaskroi1996 <131807984+thomaskroi1996@users.noreply.github.com>
Date: Wed, 17 Dec 2025 11:28:48 +0100
Subject: [PATCH 38/57] Update esmvaltool/references/jones93ijc.bibtex

Co-authored-by: Bettina Gier <gier@uni-bremen.de>
---
 esmvaltool/references/jones93ijc.bibtex | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/esmvaltool/references/jones93ijc.bibtex b/esmvaltool/references/jones93ijc.bibtex
index 360850ffdf..c3f0ee590d 100644
--- a/esmvaltool/references/jones93ijc.bibtex
+++ b/esmvaltool/references/jones93ijc.bibtex
@@ -1,4 +1,4 @@
-@article{https://doi.org/10.1002/joc.3370130606,
+@article{jones93ijc,
     author = {Jones, P. D. and Hulme, M. and Briffa, K. R.},
     title = {A comparison of Lamb circulation types with an objective classification scheme},
     journal = {International Journal of Climatology},

From 50006331ab787b70e9cb1522e4278fc89e49e4fd Mon Sep 17 00:00:00 2001
From: thomaskroi1996 <131807984+thomaskroi1996@users.noreply.github.com>
Date: Wed, 17 Dec 2025 11:29:10 +0100
Subject: [PATCH 39/57] Update
 esmvaltool/recipes/recipe_weathertyping_CMIP6.yml

Co-authored-by: Bettina Gier <gier@uni-bremen.de>
---
 esmvaltool/recipes/recipe_weathertyping_CMIP6.yml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/esmvaltool/recipes/recipe_weathertyping_CMIP6.yml b/esmvaltool/recipes/recipe_weathertyping_CMIP6.yml
index 737a00e16b..f7977f42a7 100644
--- a/esmvaltool/recipes/recipe_weathertyping_CMIP6.yml
+++ b/esmvaltool/recipes/recipe_weathertyping_CMIP6.yml
@@ -62,7 +62,6 @@ datasets_psl_CMIP6: &datasets_psl_cmip6
   - {institute: NCC, dataset: NorCPM1, ensemble: r1i1p1f1, grid: gn, esgf_version: v20200724}
   - {institute: NCC, dataset: NorESM2-LM, ensemble: r1i1p1f1, grid: gn, esgf_version: v20190815}
   - {institute: NCC, dataset: NorESM2-MM, ensemble: r1i1p1f1, grid: gn, esgf_version: v20191108}
-  # - {institute: NIMS-KMA, dataset: KACE-1-0-G, ensemble: r1i1p1f1, grid: gr, esgf_version: v20190911} #discontinous longitude coordinate warning
   - {institute: NOAA-GFDL, dataset: GFDL-CM4, ensemble: r1i1p1f1, grid: gr1, esgf_version: v20180701}
   - {institute: NOAA-GFDL, dataset: GFDL-CM4, ensemble: r1i1p1f1, grid: gr2, esgf_version: v20180701}
   - {institute: NOAA-GFDL, dataset: GFDL-ESM4, ensemble: r1i1p1f1, grid: gr1, esgf_version: v20190726}

From 7e06bc44982aa457ba87912a029a7b4d71f6c39e Mon Sep 17 00:00:00 2001
From: thomaskroi1996 <131807984+thomaskroi1996@users.noreply.github.com>
Date: Wed, 17 Dec 2025 11:29:24 +0100
Subject: [PATCH 40/57] Update
 esmvaltool/recipes/recipe_weathertyping_CMIP6.yml

Co-authored-by: Bettina Gier <gier@uni-bremen.de>
---
 esmvaltool/recipes/recipe_weathertyping_CMIP6.yml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/esmvaltool/recipes/recipe_weathertyping_CMIP6.yml b/esmvaltool/recipes/recipe_weathertyping_CMIP6.yml
index f7977f42a7..ee4b00a01f 100644
--- a/esmvaltool/recipes/recipe_weathertyping_CMIP6.yml
+++ b/esmvaltool/recipes/recipe_weathertyping_CMIP6.yml
@@ -57,7 +57,6 @@ datasets_psl_CMIP6: &datasets_psl_cmip6
   - {institute: NASA-GISS, dataset: GISS-E2-2-G, ensemble: r1i1p1f1, grid: gn, esgf_version: v20191120, start_year: 1970}
   - {institute: NCAR, dataset: CESM2-FV2, ensemble: r1i1p1f1, grid: gn, esgf_version: v20191120}
   - {institute: NCAR, dataset: CESM2-WACCM-FV2, ensemble: r1i1p1f1, grid: gn, esgf_version: v20191120}
-  # - {institute: NCAR, dataset: CESM2-WACCM, ensemble: r1i1p1f1, grid: gn, esgf_version: v20190227} #concatenate error
   - {institute: NCAR, dataset: CESM2, ensemble: r1i1p1f1, grid: gn, esgf_version: v20190308}
   - {institute: NCC, dataset: NorCPM1, ensemble: r1i1p1f1, grid: gn, esgf_version: v20200724}
   - {institute: NCC, dataset: NorESM2-LM, ensemble: r1i1p1f1, grid: gn, esgf_version: v20190815}

From 1c0d8a6ac13b1871c1cceaf97ecf3f889f2e365f Mon Sep 17 00:00:00 2001
From: thomaskroi1996 <131807984+thomaskroi1996@users.noreply.github.com>
Date: Wed, 17 Dec 2025 11:30:29 +0100
Subject: [PATCH 41/57] Update esmvaltool/config-references.yml

Co-authored-by: Bettina Gier <gier@uni-bremen.de>
---
 esmvaltool/config-references.yml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/esmvaltool/config-references.yml b/esmvaltool/config-references.yml
index 2e9586c21f..939696123f 100644
--- a/esmvaltool/config-references.yml
+++ b/esmvaltool/config-references.yml
@@ -325,7 +325,6 @@ authors:
   jury_martin:
     name: Jury, Martin
     institute: WEGC, Austria
-    e-mail: martin.jury@uni-graz.at
     orcid: https://orcid.org/0000-0003-0590-7843
   kadygrov_nikolay:
     name: Kadygrov, Nikolay

From 23eccebc4a80bfd62e6c8f5341ebcc59f911660d Mon Sep 17 00:00:00 2001
From: thomaskroi1996 <131807984+thomaskroi1996@users.noreply.github.com>
Date: Wed, 17 Dec 2025 11:30:55 +0100
Subject: [PATCH 42/57] Update
 esmvaltool/diag_scripts/weathertyping/plot_utils.py

Co-authored-by: Bettina Gier <gier@uni-bremen.de>
---
 esmvaltool/diag_scripts/weathertyping/plot_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/plot_utils.py b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
index 949431a9a9..8605fb6a70 100644
--- a/esmvaltool/diag_scripts/weathertyping/plot_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
@@ -26,7 +26,7 @@
 
 
 def generate_grayscale_hex_values(x):
-    """Generate grayscale values for plotting seasonal occurences.
+    """Generate grayscale values for plotting seasonal occurrences.
 
     Parameters
     ----------

From fca1031b1989360be67c5c19f8f41afc0935c3eb Mon Sep 17 00:00:00 2001
From: thomaskroi1996 <131807984+thomaskroi1996@users.noreply.github.com>
Date: Wed, 17 Dec 2025 11:31:16 +0100
Subject: [PATCH 43/57] Update
 esmvaltool/diag_scripts/weathertyping/plot_utils.py

Co-authored-by: Bettina Gier <gier@uni-bremen.de>
---
 esmvaltool/diag_scripts/weathertyping/plot_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/plot_utils.py b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
index 8605fb6a70..093c01521b 100644
--- a/esmvaltool/diag_scripts/weathertyping/plot_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
@@ -51,7 +51,7 @@ def plot_seasonal_occurrence(
     wt_cubes: iris.cube.Cube,
     data_info: dict,
 ):
-    """Plot seasonal occurences of weathertypes.
+    """Plot seasonal occurrences of weathertypes.
 
     Parameters
     ----------

From 242d774607e1b637ffcf51da310eab2ffe84c33a Mon Sep 17 00:00:00 2001
From: thomaskroi1996 <131807984+thomaskroi1996@users.noreply.github.com>
Date: Wed, 17 Dec 2025 11:31:43 +0100
Subject: [PATCH 44/57] Update
 esmvaltool/diag_scripts/weathertyping/plot_utils.py

Co-authored-by: Bettina Gier <gier@uni-bremen.de>
---
 esmvaltool/diag_scripts/weathertyping/plot_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/plot_utils.py b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
index 093c01521b..2334596877 100644
--- a/esmvaltool/diag_scripts/weathertyping/plot_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
@@ -115,7 +115,7 @@ def plot_seasonal_occurrence(
         _, ax_ = plt.subplots(figsize=(10, 10))
 
         ax_.set_title(
-            f"Seasonal occurence of {wt_string}, \
+            f"Seasonal occurrence of {wt_string}, \
             {data_info.get('dataset')}, {data_info.get('timerange')}",
         )
 

From 302f1a777a1d1bea287e193e460b9a35290f3650 Mon Sep 17 00:00:00 2001
From: thomaskroi1996 <131807984+thomaskroi1996@users.noreply.github.com>
Date: Wed, 17 Dec 2025 11:32:06 +0100
Subject: [PATCH 45/57] Update
 esmvaltool/diag_scripts/weathertyping/wt_utils.py

Co-authored-by: Bettina Gier <gier@uni-bremen.de>
---
 esmvaltool/diag_scripts/weathertyping/wt_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/wt_utils.py b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
index 7a83ec091d..1c0e909592 100644
--- a/esmvaltool/diag_scripts/weathertyping/wt_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
@@ -437,7 +437,7 @@ def write_corr_rmse_to_csv(
     rmse_matrix: np.array,
     dataset: str,
 ):
-    """Write correlation and rsme matrix to csv files.
+    """Write correlation and rmse matrix to csv files.
 
     Parameters
     ----------

From f5b39359c5c51a4b62c5d61a5a2a6ccfa175115a Mon Sep 17 00:00:00 2001
From: thomaskroi1996 <131807984+thomaskroi1996@users.noreply.github.com>
Date: Wed, 17 Dec 2025 11:32:35 +0100
Subject: [PATCH 46/57] Update
 esmvaltool/diag_scripts/weathertyping/wt_utils.py

Co-authored-by: Bettina Gier <gier@uni-bremen.de>
---
 esmvaltool/diag_scripts/weathertyping/wt_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/wt_utils.py b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
index 1c0e909592..4f5a8e99ca 100644
--- a/esmvaltool/diag_scripts/weathertyping/wt_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/wt_utils.py
@@ -446,7 +446,7 @@ def write_corr_rmse_to_csv(
     pattern_correlation_matrix
         Correlation matrix
     rmse_matrix
-        RSME matrix
+        RMSE matrix
     dataset
         Name of dataset
     """

From f2579c34dfad429f3f5330c01a56f02923619eb8 Mon Sep 17 00:00:00 2001
From: thomaskroi1996 <131807984+thomaskroi1996@users.noreply.github.com>
Date: Wed, 17 Dec 2025 11:32:52 +0100
Subject: [PATCH 47/57] Update
 esmvaltool/recipes/recipe_weathertyping_CMIP6.yml

Co-authored-by: Bettina Gier <gier@uni-bremen.de>
---
 esmvaltool/recipes/recipe_weathertyping_CMIP6.yml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/esmvaltool/recipes/recipe_weathertyping_CMIP6.yml b/esmvaltool/recipes/recipe_weathertyping_CMIP6.yml
index ee4b00a01f..484d6e7380 100644
--- a/esmvaltool/recipes/recipe_weathertyping_CMIP6.yml
+++ b/esmvaltool/recipes/recipe_weathertyping_CMIP6.yml
@@ -28,7 +28,6 @@ datasets_psl_CMIP6: &datasets_psl_cmip6
   - {institute: BCC, dataset: BCC-ESM1, ensemble: r1i1p1f1, grid: gn, esgf_version: v20181220}
   - {institute: CAS, dataset: FGOALS-f3-L, ensemble: r1i1p1f1, grid: gr, esgf_version: v20191019}
   - {institute: CAS, dataset: FGOALS-g3, ensemble: r1i1p1f1, grid: gn, esgf_version: v20190826}
-  # - {institute: CCCR-IITM, dataset: IITM-ESM, ensemble: r1i1p1f1, grid: gn, esgf_version: v20210203} #discontinous longitude warning
   - {institute: CCCma, dataset: CanESM5, ensemble: r1i1p1f1, grid: gn, esgf_version: v20190429}
   - {institute: CMCC, dataset: CMCC-CM2-HR4, ensemble: r1i1p1f1, grid: gn, esgf_version: v20200904}
   - {institute: CMCC, dataset: CMCC-CM2-SR5, ensemble: r1i1p1f1, grid: gn, esgf_version: v20200616}

From 835fcf11d94f8ccddf8b66801d9ce24c72cdbabb Mon Sep 17 00:00:00 2001
From: thomaskroi1996 <131807984+thomaskroi1996@users.noreply.github.com>
Date: Wed, 17 Dec 2025 11:33:13 +0100
Subject: [PATCH 48/57] Update
 esmvaltool/recipes/recipe_weathertyping_CMIP6.yml

Co-authored-by: Bettina Gier <gier@uni-bremen.de>
---
 esmvaltool/recipes/recipe_weathertyping_CMIP6.yml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/esmvaltool/recipes/recipe_weathertyping_CMIP6.yml b/esmvaltool/recipes/recipe_weathertyping_CMIP6.yml
index 484d6e7380..432e821ad2 100644
--- a/esmvaltool/recipes/recipe_weathertyping_CMIP6.yml
+++ b/esmvaltool/recipes/recipe_weathertyping_CMIP6.yml
@@ -44,7 +44,6 @@ datasets_psl_CMIP6: &datasets_psl_cmip6
   - {institute: INM, dataset: INM-CM4-8, ensemble: r1i1p1f1, grid: gr1, esgf_version: v20190530}
   - {institute: INM, dataset: INM-CM5-0, ensemble: r1i1p1f1, grid: gr1, esgf_version: v20190610}
   - {institute: IPSL, dataset: IPSL-CM5A2-INCA, ensemble: r1i1p1f1, grid: gr, esgf_version: v20200729}
-  # - {institute: IPSL, dataset: IPSL-CM6A-LR-INCA, ensemble: r1i1p1f1, grid: gr, esgf_version: v20210216} #???
   - {institute: IPSL, dataset: IPSL-CM6A-LR, ensemble: r1i1p1f1, grid: gr, esgf_version: v20180803}
   - {institute: KIOST, dataset: KIOST-ESM, ensemble: r1i1p1f1, grid: gr1, esgf_version: v20210601}
   - {institute: MIROC, dataset: MIROC-ES2L, ensemble: r1i1p1f2, grid: gn, esgf_version: v20191129}

From 79844d388ff97a85fc7ed7ea9bfa3b8c5a5ab5e6 Mon Sep 17 00:00:00 2001
From: thomaskroi1996 <131807984+thomaskroi1996@users.noreply.github.com>
Date: Wed, 17 Dec 2025 11:33:29 +0100
Subject: [PATCH 49/57] Update
 esmvaltool/recipes/recipe_weathertyping_CMIP6.yml

Co-authored-by: Bettina Gier <gier@uni-bremen.de>
---
 esmvaltool/recipes/recipe_weathertyping_CMIP6.yml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/esmvaltool/recipes/recipe_weathertyping_CMIP6.yml b/esmvaltool/recipes/recipe_weathertyping_CMIP6.yml
index 432e821ad2..c745ff5b3e 100644
--- a/esmvaltool/recipes/recipe_weathertyping_CMIP6.yml
+++ b/esmvaltool/recipes/recipe_weathertyping_CMIP6.yml
@@ -48,7 +48,6 @@ datasets_psl_CMIP6: &datasets_psl_cmip6
   - {institute: KIOST, dataset: KIOST-ESM, ensemble: r1i1p1f1, grid: gr1, esgf_version: v20210601}
   - {institute: MIROC, dataset: MIROC-ES2L, ensemble: r1i1p1f2, grid: gn, esgf_version: v20191129}
   - {institute: MIROC, dataset: MIROC6, ensemble: r1i1p1f1, grid: gn, esgf_version: v20191016}
-  # - {institute: MPI-M, dataset: ICON-ESM-LR, ensemble: r1i1p1f1, grid: gn, esgf_version: v20210215} #boundary values not 0 or 2
   - {institute: MPI-M, dataset: MPI-ESM1-2-HR, ensemble: r1i1p1f1, grid: gn, esgf_version: v20190710}
   - {institute: MPI-M, dataset: MPI-ESM1-2-LR, ensemble: r1i1p1f1, grid: gn, esgf_version: v20190710}
   - {institute: MRI, dataset: MRI-ESM2-0, ensemble: r1i1p1f1, grid: gn, esgf_version: v20190603}

From 46956efae10e7bbce711fb9d3cd15a9fb7fa1f25 Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Wed, 17 Dec 2025 15:44:16 +0100
Subject: [PATCH 50/57] bugfix!

---
 .../diag_scripts/weathertyping/calc_utils.py    | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/calc_utils.py b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
index e77369f2c7..3e1c029d76 100644
--- a/esmvaltool/diag_scripts/weathertyping/calc_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
@@ -758,10 +758,11 @@ def calc_wt_means(
     )
 
     wt_array, tcoord = get_wt_array(wt_string, wt_cubes)
+    counts = da.bincount(wt_array.flatten().astype(int)).compute()
 
-    for wt in range(1, len(np.unique(wt_array)) + 1):
-        target_indices = da.where(wt_array == wt).compute()
-        if len(target_indices[0]) < 1:
+    for wt in range(1, len(counts)):
+        target_indices = da.where(wt_array == wt)[0].compute()
+        if len(target_indices) < 1:
             logger.info(
                 "calc_wt_means - CAUTION: Skipped %s %s \
                 for dataset %s!",
@@ -774,7 +775,7 @@ def calc_wt_means(
             tcoord.units.num2date(tcoord.points[i]) for i in target_indices
         ]
         extracted_cube = cube.extract(
-            iris.Constraint(time=lambda t, d=dates: t.point in d[0]),
+            iris.Constraint(time=lambda t, d=dates: t.point in d),
         )
         wt_cube_mean = extracted_cube.collapsed("time", iris.analysis.MEAN)
         plot_maps(wt, cfg, wt_cube_mean, data_info, "mean")
@@ -825,22 +826,22 @@ def get_wt_array(wt_string: str, wt_cubes: iris.cube.CubeList) -> tuple:
     if wt_string == "slwt_ERA5":
         slwt_era5_cube = wt_cubes[1]
         tcoord = slwt_era5_cube.coord("time")
-        wt_array = slwt_era5_cube.data[:]
+        wt_array = slwt_era5_cube.core_data()[:]
     elif wt_string == "slwt_EOBS":
         slwt_eobs_cube = wt_cubes[2]
         tcoord = slwt_eobs_cube.coord("time")
-        wt_array = slwt_eobs_cube.data[:]
+        wt_array = slwt_eobs_cube.core_data()[:]
     elif wt_string == "lwt":
         lwt_cube = wt_cubes[0]
         tcoord = lwt_cube.coord("time")
-        wt_array = lwt_cube.data[:]
+        wt_array = lwt_cube.core_data()[:]
     else:
         error_str = "wt_array does not exist for calc_wt_means, calc_wt_anomalies or calc_wt_std."
         raise NameError(
             error_str,
         )
 
-    return wt_array.core_data(), tcoord
+    return wt_array, tcoord
 
 
 def calc_wt_anomalies(

From ea6a27bae8f34373e6b1eb74fc96d35ab4266c01 Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Wed, 17 Dec 2025 15:58:58 +0100
Subject: [PATCH 51/57] added info about OBS

---
 doc/sphinx/source/recipes/recipe_weathertyping.rst | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/doc/sphinx/source/recipes/recipe_weathertyping.rst b/doc/sphinx/source/recipes/recipe_weathertyping.rst
index 80c2f73684..f7fab453c2 100755
--- a/doc/sphinx/source/recipes/recipe_weathertyping.rst
+++ b/doc/sphinx/source/recipes/recipe_weathertyping.rst
@@ -70,6 +70,8 @@ Observations and reformat scripts
 (2) see headers of reformat scripts for non-obs4MIPs data for download
 instructions.*
 
+This recipe currently only works with the following reanalysis and observation datasets:
+
 * E-OBS: European Climate Assessment & Dataset gridded daily precipitation sum
 * ERA5: ECMWF reanalysis
 

From fd32c2f317028ff324cc5d887705ed3db7e39dc4 Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Thu, 18 Dec 2025 07:55:38 +0100
Subject: [PATCH 52/57] include provenance for seasonal occurrence

---
 .../diag_scripts/weathertyping/calc_utils.py  | 114 +++++++++---------
 .../diag_scripts/weathertyping/plot_utils.py  |  21 ++++
 2 files changed, 78 insertions(+), 57 deletions(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/calc_utils.py b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
index 3e1c029d76..5870c098bb 100644
--- a/esmvaltool/diag_scripts/weathertyping/calc_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
@@ -780,27 +780,27 @@ def calc_wt_means(
         wt_cube_mean = extracted_cube.collapsed("time", iris.analysis.MEAN)
         plot_maps(wt, cfg, wt_cube_mean, data_info, "mean")
 
-    ancestors = [
-        f"{data_info.get('preproc_path')}",
-        f"{cfg.get('work_dir')}/ERA5.nc",
-    ]
-    provenance_record = get_provenance_record(
-        f"{var_name} means for {wt_string}",
-        ancestors,
-        [var_name],
-        ["map"],
-        ["mean"],
-    )
+        ancestors = [
+            f"{data_info.get('preproc_path')}",
+            f"{cfg.get('work_dir')}/ERA5.nc",
+        ]
+        provenance_record = get_provenance_record(
+            f"{var_name} means for {wt_string}, wt: {wt}, ",
+            ancestors,
+            [var_name],
+            ["map"],
+            ["mean"],
+        )
 
-    local_path = f"{cfg.get('plot_dir')}/mean"
+        local_path = f"{cfg.get('plot_dir')}/mean"
 
-    log_provenance(
-        f"{local_path}/{wt_string}_{wt}{driver}_"
-        f"{data_info.get('dataset')}_{data_info.get('ensemble', '')}"
-        f"_{var_name}_mean_{data_info.get('timerange')}",
-        cfg,
-        provenance_record,
-    )
+        log_provenance(
+            f"{local_path}/{wt_string}_{wt}{driver}_"
+            f"{data_info.get('dataset')}_{data_info.get('ensemble', '')}"
+            f"_{var_name}_mean_{data_info.get('timerange')}",
+            cfg,
+            provenance_record,
+        )
 
 
 def get_wt_array(wt_string: str, wt_cubes: iris.cube.CubeList) -> tuple:
@@ -900,25 +900,25 @@ def calc_wt_anomalies(
             "anomaly",
         )
 
-    ancestors = [
-        f"{data_info.get('preproc_path')}",
-        f"{cfg.get('work_dir')}/ERA5.nc",
-    ]
-    provenance_record = get_provenance_record(
-        f"{var_name} anomaly for \
-                                              {wt_string}",
-        ancestors,
-        [var_name],
-        ["map"],
-        ["anomaly"],
-    )
+        ancestors = [
+            f"{data_info.get('preproc_path')}",
+            f"{cfg.get('work_dir')}/ERA5.nc",
+        ]
+        provenance_record = get_provenance_record(
+            f"{var_name} anomaly for, wt: {wt} \
+                                                {wt_string}",
+            ancestors,
+            [var_name],
+            ["map"],
+            ["anomaly"],
+        )
 
-    log_provenance(
-        f"{cfg.get('plot_dir')}/anomaly/{wt_string}_{wt}_{data_info.get('dataset')}_{data_info.get('ensemble', '')}"
-        f"_{var_name}_anomaly__{data_info.get('timerange')}",
-        cfg,
-        provenance_record,
-    )
+        log_provenance(
+            f"{cfg.get('plot_dir')}/anomaly/{wt_string}_{wt}_{data_info.get('dataset')}_{data_info.get('ensemble', '')}"
+            f"_{var_name}_anomaly__{data_info.get('timerange')}",
+            cfg,
+            provenance_record,
+        )
 
 
 def calc_wt_std(
@@ -971,28 +971,28 @@ def calc_wt_std(
         wt_cube_std = extracted_cube.collapsed("time", iris.analysis.STD_DEV)
         plot_maps(wt, cfg, wt_cube_std, data_info, "stddev")
 
-    ancestors = [
-        f"{data_info.get('preproc_path')}",
-        f"{cfg.get('work_dir')}/ERA5.nc",
-    ]
-    provenance_record = get_provenance_record(
-        f"{var_name} standard \
-                                              deviation for \
-                                              {wt_string}",
-        ancestors,
-        [var_name],
-        ["map"],
-        ["stddev"],
-    )
+        ancestors = [
+            f"{data_info.get('preproc_path')}",
+            f"{cfg.get('work_dir')}/ERA5.nc",
+        ]
+        provenance_record = get_provenance_record(
+            f"{var_name} standard, wt: {wt}\
+                                                deviation for \
+                                                {wt_string}",
+            ancestors,
+            [var_name],
+            ["map"],
+            ["stddev"],
+        )
 
-    local_path = f"{cfg.get('plot_dir')}/stddev"
+        local_path = f"{cfg.get('plot_dir')}/stddev"
 
-    log_provenance(
-        f"{local_path}/{wt_string}_{wt}_{data_info.get('dataset')}_{data_info.get('ensemble', '')}"
-        f"_{var_name}_stddev_{data_info.get('timerange')}",
-        cfg,
-        provenance_record,
-    )
+        log_provenance(
+            f"{local_path}/{wt_string}_{wt}_{data_info.get('dataset')}_{data_info.get('ensemble', '')}"
+            f"_{var_name}_stddev_{data_info.get('timerange')}",
+            cfg,
+            provenance_record,
+        )
 
 
 def calc_lwt_model(cfg: dict, cube: iris.cube.Cube, data_info: dict):
diff --git a/esmvaltool/diag_scripts/weathertyping/plot_utils.py b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
index 2334596877..6c404bcdfa 100644
--- a/esmvaltool/diag_scripts/weathertyping/plot_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
@@ -152,6 +152,27 @@ def plot_seasonal_occurrence(
             f"{wt_string}_rel_occurrence_{data_info.get('timerange')}.pdf",
         )
         plt.close()
+        # ancestors here are just the wt_cubes i guess
+        ancestors = [
+            f"{data_info.get('preproc_path')}",
+            f"{cfg.get('work_dir')}/ERA5.nc",
+        ]
+        provenance_record = get_provenance_record(
+            f"Seasonal occurrences for {wt_string}, ",
+            ancestors,
+            ["wt occurrences"],
+            ["stackplot"],
+        )
+
+        local_path = f"{cfg.get('plot_dir')}/mean"
+
+        log_provenance(
+            f"{local_path}/{wt_string}_{driver}_"
+            f"{data_info.get('dataset')}_{data_info.get('ensemble', '')}"
+            f"_seasonal_occurrence_{data_info.get('timerange')}",
+            cfg,
+            provenance_record,
+        )
 
 
 def set_gridlines(ax: plt.Axes):

From 5c52fa1a10770b6eeac0bd7abb99661ca2c0a817 Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Thu, 8 Jan 2026 10:47:01 +0100
Subject: [PATCH 53/57] update ancestors for seasonal occurrence provenance

---
 .../diag_scripts/weathertyping/calc_utils.py  | 21 +++++----
 .../diag_scripts/weathertyping/plot_utils.py  | 44 ++++++++++++++++---
 .../weathertyping/weathertyping.py            | 43 ++++++++++--------
 3 files changed, 76 insertions(+), 32 deletions(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/calc_utils.py b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
index 5870c098bb..86e8ba2584 100644
--- a/esmvaltool/diag_scripts/weathertyping/calc_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/calc_utils.py
@@ -81,8 +81,8 @@ def calc_slwt_obs(
 
     wt_data_prcp = []
     for wt_ in range(1, 28):
-        target_indices = da.where(lwt == wt_).compute()
-        if not target_indices[0]:
+        target_indices = da.where(lwt == wt_)[0].compute()
+        if target_indices.size == 0:
             logger.info(
                 "calc_slwt_obs - CAUTION: Skipped wt %s \
                 for dataset %s!",
@@ -98,7 +98,7 @@ def calc_slwt_obs(
             extracted_cube = cube[target_indices]
         else:
             extracted_cube = cube.extract(
-                iris.Constraint(time=lambda t, d=dates: t.point in d[0]),
+                iris.Constraint(time=lambda t, d=dates: t.point in d),
             )
         wt_cube_mean = extracted_cube.collapsed("time", iris.analysis.MEAN)
 
@@ -597,16 +597,19 @@ def calc_lwt_slwt_model(
             f"{cfg.get('work_dir')}/wt_mapping_dict_ERA5.json",
         ).open("r", encoding="utf-8") as file:
             mapping_dict_era5_f = json.load(file)
+            mapping_dict_era5 = reverse_convert_dict(mapping_dict_era5_f)
 
         with Path(
             f"{cfg.get('work_dir')}/wt_mapping_dict_E-OBS.json",
         ).open("r", encoding="utf-8") as file:
             mapping_dict_eobs_f = json.load(file)
-        mapping_dict_era5 = reverse_convert_dict(mapping_dict_era5_f)
-        mapping_dict_eobs = reverse_convert_dict(mapping_dict_eobs_f)
+            mapping_dict_eobs = reverse_convert_dict(mapping_dict_eobs_f)
 
         slwt_era5 = map_lwt_to_slwt(lwt, mapping_dict_era5)
         slwt_eobs = map_lwt_to_slwt(lwt, mapping_dict_eobs)
+
+        print("ERA5: ", slwt_era5)
+        print("EOBS: ", slwt_eobs)
     else:
         predefined_slwt = check_mapping_dict_format(predefined_slwt)
         write_mapping_dict(cfg.get("work_dir"), "ERA5", predefined_slwt)
@@ -705,7 +708,7 @@ def process_prcp_mean(
                 "correlation_threshold",
             ) and rmse_matrix[i][j] <= cfg.get("rmse_threshold"):
                 selected_pairs.append(
-                    ((i + 1, j + 1),),
+                    (i + 1, j + 1),
                 )
 
     # write matrices to csv
@@ -762,7 +765,7 @@ def calc_wt_means(
 
     for wt in range(1, len(counts)):
         target_indices = da.where(wt_array == wt)[0].compute()
-        if len(target_indices) < 1:
+        if target_indices.size == 0:
             logger.info(
                 "calc_wt_means - CAUTION: Skipped %s %s \
                 for dataset %s!",
@@ -877,7 +880,7 @@ def calc_wt_anomalies(
 
     for wt in range(1, len(np.unique(wt_array)) + 1):
         target_indices = da.where(wt_array == wt).compute()
-        if not target_indices[0]:
+        if target_indices.size == 0:
             logger.info(
                 "calc_wt_anomalies - CAUTION: Skipped wt %s \
                 for dataset %s!",
@@ -954,7 +957,7 @@ def calc_wt_std(
 
     for wt in range(1, len(np.unique(wt_array)) + 1):
         target_indices = da.where(wt_array == wt).compute()
-        if not target_indices[0]:
+        if target_indices.size == 0:
             logger.info(
                 "calc_slwt_obs - CAUTION: Skipped wt %s \
                 for dataset %s!",
diff --git a/esmvaltool/diag_scripts/weathertyping/plot_utils.py b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
index 6c404bcdfa..6f669dfb3f 100644
--- a/esmvaltool/diag_scripts/weathertyping/plot_utils.py
+++ b/esmvaltool/diag_scripts/weathertyping/plot_utils.py
@@ -50,6 +50,7 @@ def plot_seasonal_occurrence(
     cfg: dict,
     wt_cubes: iris.cube.Cube,
     data_info: dict,
+    cube_path: str,
 ):
     """Plot seasonal occurrences of weathertypes.
 
@@ -61,6 +62,8 @@ def plot_seasonal_occurrence(
         List of cubes of lwt, slwt_ERA5 and slwt_EOBS
     data_info
         Dictionary with info to dataset
+    cube_path
+        Paths to weathertype cubes (ancestors)
     """
     driver = get_driver(data_info)
 
@@ -154,14 +157,13 @@ def plot_seasonal_occurrence(
         plt.close()
         # ancestors here are just the wt_cubes i guess
         ancestors = [
-            f"{data_info.get('preproc_path')}",
-            f"{cfg.get('work_dir')}/ERA5.nc",
+            f"{cube_path}",
         ]
         provenance_record = get_provenance_record(
             f"Seasonal occurrences for {wt_string}, ",
             ancestors,
             ["wt occurrences"],
-            ["stackplot"],
+            ["seas"],
         )
 
         local_path = f"{cfg.get('plot_dir')}/mean"
@@ -381,8 +383,8 @@ def plot_corr_rmse_heatmaps(
 
     Parameters
     ----------
-    cf
-        gConfiguration dictionary from recipe
+    cfg
+        Configuration dictionary from recipe
     pattern_correlation_matrix
         Pattern correlation matrix
     rmse_matrix
@@ -458,6 +460,38 @@ def plot_corr_rmse_heatmaps(
         plt.savefig(f"{output_path}/rmse_matrix_{dataset}_{timerange}.png")
         plt.savefig(f"{output_path}/rmse_matrix_{dataset}_{timerange}.pdf")
         plt.close()
+    # log provenance
+    # rmse matrix
+    ancestors = [
+        f"{cfg.get('work_dir')}/ERA5.nc",
+    ]
+    provenance_record = get_provenance_record(
+        "rmse matrix",
+        ancestors,
+        ["rmse"],
+        ["other"],
+    )
+
+    local_path = f"{cfg.get('plot_dir')}/"
+
+    log_provenance(
+        f"{local_path}/rmse_matrix_{dataset}_{timerange}.png",
+        cfg,
+        provenance_record,
+    )
+    # correlation matrix
+    provenance_record = get_provenance_record(
+        "correlation matrix",
+        ancestors,
+        ["correlation"],
+        ["other"],
+    )
+
+    log_provenance(
+        f"{local_path}/correlation_matrix_{dataset}_{timerange}.png",
+        cfg,
+        provenance_record,
+    )
 
 
 def get_colormap(colormap_string: str) -> ListedColormap:
diff --git a/esmvaltool/diag_scripts/weathertyping/weathertyping.py b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
index 50fd47d42d..df06718695 100644
--- a/esmvaltool/diag_scripts/weathertyping/weathertyping.py
+++ b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
@@ -78,12 +78,13 @@ def process_models_automatic_slwt(
             # plot means
             if cfg.get("plotting", False):
                 # load wt files
+                wt_cube_path = f"{cfg.get('work_dir')}/{output_file_path}"
+                f"/{data_info['dataset']}"
+                f"{data_info['driver']}_"
+                f"{data_info['timerange']}.nc"
+
                 wt_cubes = load_wt_files(
-                    f"{cfg.get('work_dir')}/{output_file_path}"
-                    f"/{data_info['dataset']}"
-                    f"{data_info['driver']}_"
-                    f"{data_info['ensemble']}_"
-                    f"{data_info['timerange']}.nc",
+                    wt_cube_path, f"{data_info['ensemble']}_"
                 )
 
                 var_dict = {
@@ -96,7 +97,9 @@ def process_models_automatic_slwt(
                     data_info["preproc_path"] = var_data[1]
 
                     plot_means(cfg, var_data[0], wt_cubes, data_info)
-                plot_seasonal_occurrence(cfg, wt_cubes, data_info)
+                plot_seasonal_occurrence(
+                    cfg, wt_cubes, data_info, wt_cube_path
+                )
 
 
 def process_era5_automatic_slwt(
@@ -163,9 +166,8 @@ def process_era5_automatic_slwt(
     combine_wt_to_file(cfg, wt_list, wt_preproc, data_info["dataset"])
 
     # load weathertype files as cubes
-    wt_cubes = load_wt_files(
-        f"{cfg.get('work_dir')}/{data_info['dataset']}.nc",
-    )
+    wt_cube_path = f"{cfg.get('work_dir')}/{data_info['dataset']}.nc"
+    wt_cubes = load_wt_files(wt_cube_path)
 
     if cfg.get("plotting", False):
         var_dict = get_looping_dict(
@@ -177,7 +179,7 @@ def process_era5_automatic_slwt(
             data_info["preproc_path"] = var_data[1]
 
             plot_means(cfg, var_data[0], wt_cubes, data_info)
-        plot_seasonal_occurrence(cfg, wt_cubes, data_info)
+        plot_seasonal_occurrence(cfg, wt_cubes, data_info, wt_cube_path)
 
 
 def run_automatic_slwt(cfg: dict):
@@ -256,8 +258,9 @@ def process_era5_lwt(
     write_lwt_to_file(cfg, lwt, wt_preproc, data_info["dataset"])
 
     # load wt files
+    wt_cube_path = f"{cfg.get('work_dir')}/{data_info['dataset']}.nc"
     wt_cubes = load_wt_files(
-        f"{cfg.get('work_dir')}/{data_info['dataset']}.nc",
+        wt_cube_path,
         mode="lwt",
     )
 
@@ -271,7 +274,7 @@ def process_era5_lwt(
             data_info["preproc_path"] = var_data[1]
 
             plot_means(cfg, var_data[0], wt_cubes, data_info, "lwt")
-        plot_seasonal_occurrence(cfg, wt_cubes, data_info)
+        plot_seasonal_occurrence(cfg, wt_cubes, data_info, wt_cube_path)
 
 
 def process_models_lwt(cfg: dict, dataset_vars: list, data_info: dict):
@@ -308,12 +311,14 @@ def process_models_lwt(cfg: dict, dataset_vars: list, data_info: dict):
             calc_lwt_model(cfg, wt_preproc, data_info)
 
             # load wt files
+            wt_cube_path = f"{cfg.get('work_dir')}/{output_file_path}"
+            f"/{data_info['dataset']}"
+            f"{data_info['driver']}"
+            f"_{data_info['ensemble']}_"
+            f"{data_info['timerange']}.nc"
+
             wt_cubes = load_wt_files(
-                f"{cfg.get('work_dir')}/{output_file_path}"
-                f"/{data_info['dataset']}"
-                f"{data_info['driver']}"
-                f"_{data_info['ensemble']}_"
-                f"{data_info['timerange']}.nc",
+                wt_cube_path,
                 mode="lwt",
             )
 
@@ -334,7 +339,9 @@ def process_models_lwt(cfg: dict, dataset_vars: list, data_info: dict):
                         data_info,
                         "lwt",
                     )
-                plot_seasonal_occurrence(cfg, wt_cubes, data_info)
+                plot_seasonal_occurrence(
+                    cfg, wt_cubes, data_info, wt_cube_path
+                )
 
 
 def run_lwt(cfg: dict):

From 2021b99338794f98eed8396b3db91cfa28da3c6f Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Thu, 8 Jan 2026 11:14:13 +0100
Subject: [PATCH 54/57] .

---
 esmvaltool/diag_scripts/weathertyping/weathertyping.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/weathertyping.py b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
index df06718695..2c0b6aa2d0 100644
--- a/esmvaltool/diag_scripts/weathertyping/weathertyping.py
+++ b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
@@ -74,7 +74,6 @@ def process_models_automatic_slwt(
                 data_info,
                 cfg.get("predefined_slwt"),
             )
-
             # plot means
             if cfg.get("plotting", False):
                 # load wt files

From 02136098a4c266e1c8115fccb57f469a40888d8b Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Thu, 8 Jan 2026 11:20:33 +0100
Subject: [PATCH 55/57] minor issues

---
 esmvaltool/diag_scripts/weathertyping/weathertyping.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/weathertyping.py b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
index 2c0b6aa2d0..667cf9bdc2 100644
--- a/esmvaltool/diag_scripts/weathertyping/weathertyping.py
+++ b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
@@ -83,7 +83,8 @@ def process_models_automatic_slwt(
                 f"{data_info['timerange']}.nc"
 
                 wt_cubes = load_wt_files(
-                    wt_cube_path, f"{data_info['ensemble']}_"
+                    wt_cube_path,
+                    f"{data_info['ensemble']}_",
                 )
 
                 var_dict = {
@@ -97,7 +98,10 @@ def process_models_automatic_slwt(
 
                     plot_means(cfg, var_data[0], wt_cubes, data_info)
                 plot_seasonal_occurrence(
-                    cfg, wt_cubes, data_info, wt_cube_path
+                    cfg,
+                    wt_cubes,
+                    data_info,
+                    wt_cube_path,
                 )
 
 

From 1ec731f0ab7fe4ec3afd331d485c5867a5e7ce42 Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Thu, 8 Jan 2026 11:29:22 +0100
Subject: [PATCH 56/57] minor fix!

---
 esmvaltool/diag_scripts/weathertyping/weathertyping.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/weathertyping.py b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
index 667cf9bdc2..8ef79ec613 100644
--- a/esmvaltool/diag_scripts/weathertyping/weathertyping.py
+++ b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
@@ -343,7 +343,10 @@ def process_models_lwt(cfg: dict, dataset_vars: list, data_info: dict):
                         "lwt",
                     )
                 plot_seasonal_occurrence(
-                    cfg, wt_cubes, data_info, wt_cube_path
+                    cfg,
+                    wt_cubes,
+                    data_info,
+                    wt_cube_path,
                 )
 
 

From f830b237be0175125e65b34fc4630ab5fbaea70c Mon Sep 17 00:00:00 2001
From: Thomas Kroissenbrunner <thomaskroi1996@gmail.com>
Date: Thu, 8 Jan 2026 13:01:46 +0100
Subject: [PATCH 57/57] bug

---
 .../weathertyping/weathertyping.py            | 27 ++++++++++---------
 1 file changed, 14 insertions(+), 13 deletions(-)

diff --git a/esmvaltool/diag_scripts/weathertyping/weathertyping.py b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
index 8ef79ec613..0f8da4d277 100644
--- a/esmvaltool/diag_scripts/weathertyping/weathertyping.py
+++ b/esmvaltool/diag_scripts/weathertyping/weathertyping.py
@@ -77,16 +77,15 @@ def process_models_automatic_slwt(
             # plot means
             if cfg.get("plotting", False):
                 # load wt files
-                wt_cube_path = f"{cfg.get('work_dir')}/{output_file_path}"
-                f"/{data_info['dataset']}"
-                f"{data_info['driver']}_"
-                f"{data_info['timerange']}.nc"
-
-                wt_cubes = load_wt_files(
-                    wt_cube_path,
-                    f"{data_info['ensemble']}_",
+                wt_cube_path = (
+                    f"{cfg.get('work_dir')}/{output_file_path}"
+                    f"/{data_info['dataset']}"
+                    f"{data_info['driver']}_"
+                    f"{data_info['timerange']}.nc"
                 )
 
+                wt_cubes = load_wt_files(wt_cube_path)
+
                 var_dict = {
                     f"{ensemble_var.get('short_name')}": get_preproc_lists_ensemble(
                         ensemble_var,
@@ -314,11 +313,13 @@ def process_models_lwt(cfg: dict, dataset_vars: list, data_info: dict):
             calc_lwt_model(cfg, wt_preproc, data_info)
 
             # load wt files
-            wt_cube_path = f"{cfg.get('work_dir')}/{output_file_path}"
-            f"/{data_info['dataset']}"
-            f"{data_info['driver']}"
-            f"_{data_info['ensemble']}_"
-            f"{data_info['timerange']}.nc"
+            wt_cube_path = (
+                f"{cfg.get('work_dir')}/{output_file_path}"
+                f"/{data_info['dataset']}"
+                f"{data_info['driver']}"
+                f"_{data_info['ensemble']}_"
+                f"{data_info['timerange']}.nc"
+            )
 
             wt_cubes = load_wt_files(
                 wt_cube_path,