Wetterdienst - Open weather data for humans

Hohenpeißenberg, Germany: 245 years of mean air temperature, one stripe per year from 1781 to 2025 — fetched and drawn with wetterdienst.

Global warming is not an opinion.

Erderwärmung ist keine Meinung.

Warning

This library is a work in progress! Breaking changes should be expected until a 1.0 release, so version pinning is recommended.

Wetterdienst gives you weather, climate and hydrology data from 22 national services through one interface: one way to find a station, one way to ask for values, one shape of result. It is a polars-based Python library, a command line client, a REST API, an MCP endpoint and a web app, all serving the same data.

Contributions and feedback are very welcome — we do not use most of this data ourselves, so what you need is what tells us what to build next. Hand in an issue or a PR.

What we support

Provider	Country	What you get
DWD	🇩🇪 Germany	observations, MOSMIX/DMO forecasts, radar, warnings, road weather, derived indices
AEMET	🇪🇸 Spain	observations (API key)
CHMI	🇨🇿 Czechia	observations
DMI	🇩🇰 Denmark	observations, incl. Greenland and the Faroe Islands (API key)
EA	🇬🇧 England	hydrology
Eaufrance	🇫🇷 France	hydrology
ECCC	🇨🇦 Canada	observations
FMI	🇫🇮 Finland	observations
GeoSphere	🇦🇹 Austria	observations
IMGW	🇵🇱 Poland	meteorology, hydrology
IPMA	🇵🇹 Portugal	observations
KNMI	🇳🇱 Netherlands	observations (API key)
LHMT	🇱🇹 Lithuania	observations
Met Office	🇬🇧 UK	MIDAS Open observations (CEDA account)
Météo-France	🇫🇷 France	observations, SYNOP
MeteoSwiss	🇨🇭 Switzerland	observations
met.no	🇳🇴 Norway	Frost observations (API key)
NOAA	🌍 worldwide	GHCN daily and hourly, stations across the globe
NWS	🇺🇸 USA	observations
RMI	🇧🇪 Belgium	observations
SMHI	🇸🇪 Sweden	observations
WSV	🇩🇪 Germany	hydrology (Pegelonline)

Across those: 514 canonical parameters, resolutions from 1 minute to annual, and archives reaching back centuries where the service keeps them. Every provider is reached the same way, and a parameter means the same thing whichever service reports it — temperature_air_mean_2m is the mean air temperature at 2 m, converted to the same unit, everywhere.

Licenses and usage requirements differ per provider, so check the data chapter before you publish anything built on them. It also lists every dataset and parameter per provider.

Features

• Stations, values and station history (metadata changes) through one request model
• Find stations by name, id, distance from a point, bounding box or rank
• Request by parameters, periods, start_date, end_date; tune the rest through Settings
• Unit conversion, interpolation and summarization for a point between stations
• DWD weather alerts (CAP warnings) with GeoJSON geometry, by community or district
• SQL queries over results, export to CSV/JSON/Excel/Parquet/Zarr and to SQLite, PostgreSQL, CrateDB, InfluxDB and DuckDB
• Command line client, REST API and a public Docker image
• MCP (Model Context Protocol) endpoint, so LLM agents can query the data as tools
• Web app with map-based explorer, forecasts, climate stripes and a glossary, at wetterdienst.eobs.org

Setup

pip install wetterdienst           # from PyPI
pip install wetterdienst[export]   # with an extra
pip install git+https://github.com/earthobservations/wetterdienst  # most recent

Extras: bufr, cratedb, duckdb, eccodes, excel, export, influxdb, interpolation, knmi, mcp, mysql, pdf, plotting, postgresql, radar, radarplus, restapi, sql. Check the installation with wetterdienst --help.

Prefer Docker? The image ships with the optional dependencies included:

docker pull ghcr.io/earthobservations/wetterdienst
docker run -ti ghcr.io/earthobservations/wetterdienst wetterdienst --version

See the Docker chapter for running the REST API and the app from the image.

Example

Daily precipitation for Zinnwald-Georgenfeld, August 2002 — the flood:

from wetterdienst.provider.dwd.observation import DwdObservationRequest

request = DwdObservationRequest(
    parameters=[("daily", "climate_summary", "precipitation_height")],
    start_date="2002-08-11",
    end_date="2002-08-13",
).filter_by_station_id(station_id=(5779,))

stations = request.df
stations.head()
# ┌────────────┬─────────────────┬────────────┬─────────────────────────┬───┬───────────┬────────┬──────────────────────┬─────────┐
# │ resolution ┆ dataset         ┆ station_id ┆ start_date              ┆ … ┆ longitude ┆ height ┆ name                 ┆ state   │
# │ ---        ┆ ---             ┆ ---        ┆ ---                     ┆   ┆ ---       ┆ ---    ┆ ---                  ┆ ---     │
# │ str        ┆ str             ┆ str        ┆ datetime[μs, UTC]       ┆   ┆ f64       ┆ f64    ┆ str                  ┆ str     │
# ╞════════════╪═════════════════╪════════════╪═════════════════════════╪═══╪═══════════╪════════╪══════════════════════╪═════════╡
# │ daily      ┆ climate_summary ┆ 05779      ┆ 1971-01-01 00:00:00 UTC ┆ … ┆ 13.7516   ┆ 877.0  ┆ Zinnwald-Georgenfeld ┆ Sachsen │
# └────────────┴─────────────────┴────────────┴─────────────────────────┴───┴───────────┴────────┴──────────────────────┴─────────┘

values = request.values.all().df
values.head()
# ┌────────────┬────────────┬─────────────────┬──────────────────────┬─────────────────────────┬───────┬─────────┐
# │ station_id ┆ resolution ┆ dataset         ┆ parameter            ┆ date                    ┆ value ┆ quality │
# ╞════════════╪════════════╪═════════════════╪══════════════════════╪═════════════════════════╪═══════╪═════════╡
# │ 05779      ┆ daily      ┆ climate_summary ┆ precipitation_height ┆ 2002-08-11 00:00:00 UTC ┆ 67.9  ┆ 10.0    │
# │ 05779      ┆ daily      ┆ climate_summary ┆ precipitation_height ┆ 2002-08-12 00:00:00 UTC ┆ 312.0 ┆ 10.0    │
# │ 05779      ┆ daily      ┆ climate_summary ┆ precipitation_height ┆ 2002-08-13 00:00:00 UTC ┆ 26.3  ┆ 10.0    │
# └────────────┴────────────┴─────────────────┴──────────────────────┴─────────────────────────┴───────┴─────────┘

values.to_pandas()  # if you would rather have pandas

The same thing from the command line:

wetterdienst stations --provider=dwd --network=observation --parameters=daily/kl --all
wetterdienst values --provider=dwd --network=observation --parameters=daily/kl --station=1048,4411

More in examples and in the usage chapter.

Links

• App: wetterdienst.eobs.org
• Documentation: wetterdienst.readthedocs.io (usage, contribution, changelog)
• Examples and benchmarks

What we stand for

• 🏳️‍🌈🏳️‍⚧️ We stand with the LGBTQI+ community.
• ✊ No place for Nazis. FCKNZS.
• 🌡️ Global warming is not an opinion.
• 🔓 Weather data belongs to everyone.

Acknowledgements

We want to acknowledge all environmental agencies which provide their data open and free of charge first and foremost for the sake of endless research possibilities.

We want to acknowledge all contributors for being part of the improvements to this library that make it better and better every day.

Supported by

   

Special thanks to the kind people at JetBrains s.r.o. for a PyCharm licence through their open-source support programme, and to Anthropic for a Claude Max subscription for open-source maintainers.