ZonalClimateAnalyzer

Analyze the climate history for any area, line or point inside Germany with nothing more than a shapefile.

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1 | Project Purpose

This script automates an end‑to‑end workflow to

1. Download annual gridded climate rasters (1951 → latest) from the DWD Climate Data Center (CDC).
2. Clip & analyse those rasters for a user‑supplied area (polygon shapefile).
3. Summarise the results as zonal statistics (min / mean / max) in a tidy JSON file.
4. Visualise long‑term trends with ready‑made PNG plots. Also creates an interactive map of the analyzed area.

The goal is to give municipalities, researchers and students a quick way to quantify and visualise local climate change indicators without manual GIS work.

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2 | Key Features

Stage	What happens	Where in code
Input	Interactive prompt asks for your .shp path	get_shp()
Data download	All relevant .asc.gz / .zip rasters fetched from the DWD open‑data mirror	download_dwd_data()
Pre‑processing	Decompress → rename → add CRS → re‑encode to GeoTIFF	decompress_file(), asc_to_tif_add_crs()
Clean‑up	Source archives removed to keep disk footprint small	delete_raster_files()
Analysis	Per‑polygon zonal stats with rasterstats	calculate_zonal_stats()
Output	- area_rasterstats.json containing the calculated data, - 9 plots showing 17 different climate parameter long term trends, - map of the analyzed area	visualiser section

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3 | Quick‑start

Linux:

# 1. Clone & enter
git clone https://github.com/LevinGiersch/ZonalClimateAnalyzer
cd ZonalClimateAnalyzer

# 2. Create & activate venv (recommended)
python3 -m venv venv
source venv/bin/activate

# 3. Install Python ≥3.9 deps
pip install -r requirements.txt

# 4. Run the script
python ZonalClimateAnalyzer.py

# 5. Follow the prompt:
# Enter the path to the shapefile: /path/to/my_area.shp

# 6. Wait until the process is finished

Windows:

# 1. Clone & enter
git clone https://github.com/LevinGiersch/ZonalClimateAnalyzer
cd ZonalClimateAnalyzer

# 2. Install Python ≥3.9 deps (recommended: venv/conda)
pip install -r requirements.txt

# 3. Run the script
python ZonalClimateAnalyzer.py

# 4. Follow the prompt:
# Enter the path to the shapefile: C:\path\to\my_area.shp

# 5. Wait until the process is finished

• Runtime hint: the first execution downloads ≈ 1 GB of rasters and can take 10–20 min (depending on your connection).

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4 | Data Source & Coordinate System

Aspect	Details
Provider	Deutscher Wetterdienst (DWD) – Climate Data Center
URL root	https://opendata.dwd.de/climate_environment/CDC/grids_germany/annual/
Parameters pulled	air_temperature_*, frost_days, hot_days, ice_days, drought_index, precipitation, snowcover_days, precipGE{10,20,30}mm_days, sunshine_duration, vegetation_{begin,end}
Spatial grid	1 × 1 km GK3 / DHDN Zone 3 (EPSG 31467)
Temporal coverage	1951 – present (updated yearly)

• A small gk3.prj file ships with the repo; shapefiles are re‑projected into this CRS so raster overlays line up exactly.
• Detailled informations on the data (as.pdf) can be found inside the 'ZonalClimateAnalyzer/climate_environment_CDC_grids_germany_annual' folder after executing the script.

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5 | Outputs Explained

File	What it shows
map.html	Interactive map of the analyzed area. Shows filename, area and perimeter on hover.
min_mean_max_temp_plot.png	Annual maximum, mean and minimum air temperature. Filled bands visualise the spread between the three series.
ice_frost_days_plot.png	Counts of frost days (Tmin < 0 °C) and ice days (Tmax < 0 °C).
snowcover_days_plot.png	Days per year with snow depth > 1 cm.
summer_hot_days_plot.png	Counts of summer days (Tmax ≥ 25 °C) and hot days (Tmax ≥ 30 °C).
precipitation_drought_plot.png	Annual precipitation totals (bars) with drought index overlay (line, scaled).
precip_days_plot.png	Number of days with heavy precipitation ≥ 10 mm, ≥ 20 mm, ≥ 30 mm.
sunshine_duration_plot.png	Average daily sunshine hours for each year.
vegetativ_phase_plot.png	Start and end dates of the vegetative season plus reference lines for astronomical seasons.
vegetativ_phase_len_plot.png	Length of the vegetative season (days between start and end) for each year.

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6 | Known Limitations

• Only works for shapefiles. Support for eg. geojson may be added in the future.
• Re‑downloads rasters each year; archive copies yourself for full reproducibility.

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7 | Citation

Always credit the DWD Climate Data Center when publishing derived work.

Deutscher Wetterdienst (2025): Grids Germany – Annual. https://opendata.dwd.de/climate_environment/CDC/grids_germany/annual/