Wetterdienst - Open weather data for humans

"What do we want? Climate Justice! When do we want it? Now!" - FFF

Warning

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

Badges

CI

Meta

Downloads

Citation

Overview

Welcome to Wetterdienst, your friendly weather service library for Python.

We are a group of like-minded people trying to make access to weather data in Python feel like a warm summer breeze, similar to other projects like rdwd for the R language, which originally drew our interest in this project. Our long-term goal is to provide access to multiple weather services as well as other related agencies such as river measurements. With wetterdienst we try to use modern Python technologies all over the place. The library is based on polars (we <3 pandas, it is still part of some IO processes) across the board, uses uv for package administration and GitHub Actions for all things CI. Our users are an important part of the development as we are not currently using the data we are providing and only implement what we think would be the best. Therefore, contributions and feedback whether it be data related or library related are very welcome! Just hand in a PR or Issue if you think we should include a new feature or data source.

Data

For an overview of the data we have currently made available and under which license it is published take a look at the data section. Detailed information on datasets and parameters is given at the coverage subsection. Licenses and usage requirements may differ for each provider so check this out before including the data in your project to be sure that you fulfill copyright requirements!

For a closer look on the DWD data, you can use the interactive map or the table provided by the rdwd package.

Features

• APIs for stations, values and history (station metadata changes)
• DWD weather alerts (CAP warnings) with GeoJSON geometry, on community or district basis
• Get stations nearby a selected location
• Define your request by arguments such as parameters, periods, start date, end date
• Define general settings in Settings context
• Command line interfaced
• Modern app with interactive data explorer, settings interface, visualization and separate REST API, hosted on wetterdienst.eobs.org
• Optional MCP (Model Context Protocol) endpoint so LLM agents can query the data as tools
• Run SQL queries on the results
• Export results to databases and other data sinks
• Public Docker image
• Interpolation and Summary of station values

App

The modern app provides an interactive interface for exploring weather data:

• Explorer: Interactive data exploration with map-based station selection, parameter filtering, and real-time data visualization
• Settings Interface: Comprehensive controls for all backend API parameters including unit conversion, data shape, interpolation options, and more
• Climate Stripes: Visual representation of temperature trends over time
• Customization: Theme color picker and dark mode support

Visit the live instance at wetterdienst.eobs.org

Setup

Native

Via PyPi (standard):

pip install wetterdienst

Via Github (most recent):

pip install git+https://github.com/earthobservations/wetterdienst

There are some extras available for wetterdienst. Use them like:

pip install wetterdienst[sql]

• cratedb: Install support for CrateDB.
• duckdb: Install support for DuckDB.
• export: Install openpyxl for Excel export and pyarrow for writing files in Feather- and Parquet-format.
• influxdb: Install support for InfluxDB.
• interpolation: Install support for station interpolation.
• mysql: Install support for MySQL.
• matplotlib: Install support for plotting (matplotlib), only used in radar examples.
• pdf: Install support for PDF to get extra information on DWD Observation metadata.
• plotting: Install support for plotting (plotly).
• postgresql: Install support for PostgreSQL.
• sql: Install DuckDB for querying data using SQL.

To check the installation, invoke:

wetterdienst --help

Docker

Docker images for each stable release will get pushed to GitHub Container Registry.

wetterdienst serves a full environment, including all the optional dependencies of Wetterdienst.

Pull the Docker image:

docker pull ghcr.io/earthobservations/wetterdienst

Library

Use the latest stable version of wetterdienst:

$ docker run -ti ghcr.io/earthobservations/wetterdienst
Python 3.8.5 (default, Sep 10 2020, 16:58:22)
[GCC 8.3.0] on linux

import wetterdienst

wetterdienst.__version__

Command line script

The wetterdienst command is also available:

# Make an alias to use it conveniently from your shell.
alias wetterdienst='docker run -ti ghcr.io/earthobservations/wetterdienst wetterdienst'

wetterdienst --help
wetterdienst --version
wetterdienst info

Raspberry Pi / LINUX ARM

Running wetterdienst on Raspberry Pi, you need to install numpy and lxml prior to installing wetterdienst by running the following lines:

# not all installations may be required to get lxml running
sudo apt-get install gfortran
sudo apt-get install libopenblas-base
sudo apt-get install libopenblas-dev
sudo apt-get install libatlas-base-dev
sudo apt-get install python3-lxml

Additionally expanding the Swap to 2048 mb may be required and can be done via swap-file:

sudo nano /etc/dphys-swapfile

Thanks chr-sto for reporting back to us!

Example

Task

Get historical climate summary for two German stations between 1990 and 2020

Library

from wetterdienst import Settings
from wetterdienst.provider.dwd.observation import DwdObservationRequest

settings = Settings(  # default
    ts_shape="long",  # tidy data
    ts_humanize=True,  # humanized parameters
    ts_convert_units=True  # convert values to SI units
)

request = DwdObservationRequest(
    parameters=[
        ("daily", "climate_summary", "precipitation_height"),
    ],
    start_date="2002-08-11",  # if not given timezone defaulted to UTC
    end_date="2002-08-13",  # if not given timezone defaulted to UTC
    settings=settings
).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 │
# │ ---        ┆ ---        ┆ ---             ┆ ---                  ┆ ---                     ┆ ---   ┆ ---     │
# │ enum       ┆ enum       ┆ enum            ┆ enum                 ┆ datetime[μs, UTC]       ┆ f64   ┆ f64     │
# ╞════════════╪════════════╪═════════════════╪══════════════════════╪═════════════════════════╪═══════╪═════════╡
# │ 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    │
# └────────────┴────────────┴─────────────────┴──────────────────────┴─────────────────────────┴───────┴─────────┘

# to get a pandas DataFrame and e.g. create some matplotlib plots    
values.to_pandas()

Client

# Get list of all stations for daily climate summary data in JSON format
wetterdienst stations --provider=dwd --network=observation --parameters=daily/kl --all

# Get daily climate summary data for specific stations
wetterdienst values --provider=dwd --network=observation --parameters=daily/kl --station=1048,4411

Other

Checkout examples for more examples.

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.

Important Links

• App: https://wetterdienst.eobs.org/

• Documentation: https://wetterdienst.readthedocs.io/

  • Usage: https://wetterdienst.readthedocs.io/en/latest/usage/
  • Contribution: https://wetterdienst.readthedocs.io/en/latest/contribution/
  • Changelog: https://wetterdienst.readthedocs.io/en/latest/changelog.html

• Examples (runnable scripts): https://github.com/earthobservations/wetterdienst/tree/main/examples

• Benchmarks: https://github.com/earthobservations/wetterdienst/tree/main/benchmarks

Supported by

Special thanks to the kind people at JetBrains s.r.o. for supporting us with excellent development tooling.