https://github.com/wichmann/retrowetter
A simple app to visualize weather data to show climate trends.
https://github.com/wichmann/retrowetter
climate pandas plotly python streamlit visualization weather webapp
Last synced: about 2 months ago
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A simple app to visualize weather data to show climate trends.
- Host: GitHub
- URL: https://github.com/wichmann/retrowetter
- Owner: wichmann
- License: mit
- Created: 2025-06-21T14:56:28.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-29T08:59:32.000Z (12 months ago)
- Last Synced: 2025-06-29T09:37:45.073Z (12 months ago)
- Topics: climate, pandas, plotly, python, streamlit, visualization, weather, webapp
- Language: Python
- Homepage: https://retrowetter.de
- Size: 291 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Climate analysis from DWD weather data
A simple app to visualize weather data to show climate trends.
The weather and climate data for this app are provided by the Deutscher
Wetterdienst (DWD) and contain mainly data for Germany, other locations may be
supported. All spatial data including weather and climate information are
provided under the Creative Commons licence CC BY 4.0 as stated in the
[legal notices](https://www.dwd.de/EN/service/legal_notice/legal_notice.html).
More information about the DWD can be found on their [homepage](https://www.dwd.de/).
## Build
Run app for development:
streamlit run app.py
Build Docker container for development:
docker build -t retrowetter .
docker run -p 8501:8501 -d retrowetter
Update and create translation files:
pybabel extract . -o locales/base.pot
pybabel init -l de_DE en_US -i locales/base.pot -d locales
pybabel update -i locales/base.pot -d locales
pybabel compile -d locales
## Todo
- Reimplement with Dash (https://dash.plotly.com/minimal-app) to compare with Streamlit.
- Use Bokeh for plots.
- Run app directly in browser with PyScript (https://pyscript.com/@examples/bokeh/latest).
## Notes
Possible visualization libraries:
- Dash
- Streamlit
- Bokeh
- Plotly
- PyScript
- Jupyter Notebook
Data source:
- https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/daily/kl/historical/
Sources:
- https://www.dwd.de/DE/leistungen/cdc/cdc_ueberblick-klimadaten.html
- https://github.com/jdemaeyer/dwdparse
- Open weather data for humans. (wetterdienst.eobs.org, https://github.com/earthobservations/wetterdienst)