Overview

Introduction

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 pandas across the board, uses Poetry 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.

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 Jetbrains and their open source team for providing us with licenses for Pycharm Pro, which we are using for the development.

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

Coverage

DWD (Deutscher Wetterdienst / German Weather Service / Germany)

• Historical Weather Observations

  • Historical (last ~300 years), recent (500 days to yesterday), now (yesterday up to last hour)

  • Every minute to yearly resolution

  • Time series of stations in Germany

• Mosmix - statistical optimized scalar forecasts extracted from weather models

  • Point forecast

  • 5400 stations worldwide

  • Both MOSMIX-L and MOSMIX-S is supported

  • Up to 115 parameters

• Radar

  • 16 locations in Germany

  • All of Composite, Radolan, Radvor, Sites and Radolan_CDC

  • Radolan: calibrated radar precipitation

  • Radvor: radar precipitation forecast

ECCC (Environnement et Changement Climatique Canada / Environment and Climate Change Canada / Canada)

• Historical Weather Observations

  • Historical (last ~180 years)

  • Hourly, daily, monthly, (annual) resolution

  • Time series of stations in Canada

NOAA (National Oceanic And Atmospheric Administration / National Oceanic And Atmospheric Administration / United States Of America)

• Global Historical Climatology Network

  • Historical, daily weather observations from around the globe

  • more then 100k stations

  • data for weather services which don’t publish data themselves

WSV (Wasserstraßen- und Schifffahrtsverwaltung des Bundes / Federal Waterways and Shipping Administration)

• Pegelonline

  • data of river network of Germany

  • coverage of last 30 days

  • parameters like stage, runoff and more related to rivers

EA (Environment Agency)

• Hydrology

  • data of river network of UK

  • parameters flow and ground water stage

To get better insight on which data we have currently made available and under which license those are published take a look at the data section.

Features

• API(s) for stations (metadata) and values

• Get station(s) nearby a selected location

• Define your request by arguments such as parameter, period, resolution, start date, end date

• Command line interface

• Web-API via FastAPI

• Run SQL queries on the results

• Export results to databases and other data sinks

• Public Docker image

Setup

wetterdienst can be used by either installing it on your workstation or within a Docker container.

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[http,sql]

• docs: Install the Sphinx documentation generator.

• ipython: Install iPython stack.

• export: Install openpyxl for Excel export and pyarrow for writing files in Feather- and Parquet-format.

• http: Install HTTP API prerequisites.

• sql: Install DuckDB for querying data using SQL.

• duckdb: Install support for DuckDB.

• influxdb: Install support for InfluxDB.

• cratedb: Install support for CrateDB.

• mysql: Install support for MySQL.

• postgresql: Install support for PostgreSQL.

In order to check the installation, invoke:

wetterdienst --help

Docker

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

There are images in two variants, wetterdienst-standard and wetterdienst-full.

wetterdienst-standard will contain a minimum set of 3rd-party packages, while wetterdienst-full will try to serve a full environment by also including packages like GDAL and wradlib.

Pull the Docker image:

docker pull ghcr.io/earthobservations/wetterdienst-standard

Library

Use the latest stable version of wetterdienst:

$ docker run -ti ghcr.io/earthobservations/wetterdienst-standard
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-standard wetterdienst'

wetterdienst --help
wetterdienst version
wetterdienst info

Example

Acquisition of historical data for specific stations using wetterdienst as library:

Load required request class:

>>> import pandas as pd
>>> pd.options.display.max_columns = 8
>>> from wetterdienst.provider.dwd.observation import DwdObservationRequest
>>> from wetterdienst import Settings

Alternatively, though without argument/type hinting:

>>> from wetterdienst import Wetterdienst
>>> API = Wetterdienst("dwd", "observation")

Get data:

>>> Settings.tidy = True  # default, tidy data
>>> Settings.humanize = True  # default, humanized parameters
>>> Settings.si_units = True  # default, convert values to SI units
>>> request = DwdObservationRequest(
...    parameter=["climate_summary"],
...    resolution="daily",
...    start_date="1990-01-01",  # if not given timezone defaulted to UTC
...    end_date="2020-01-01",  # if not given timezone defaulted to UTC
... ).filter_by_station_id(station_id=(1048, 4411))
>>> request.df.head()  # station list
    station_id                 from_date                   to_date  height  \
...      01048 1934-01-01 00:00:00+00:00 ... 00:00:00+00:00   228.0
...      04411 1979-12-01 00:00:00+00:00 ... 00:00:00+00:00   155.0

     latitude  longitude                    name    state
...   51.1278    13.7543       Dresden-Klotzsche  Sachsen
...   49.9195     8.9671  Schaafheim-Schlierbach   Hessen

>>> request.values.all().df.head()  # values
  station_id          dataset      parameter                      date  value  \
0      01048  climate_summary  wind_gust_max 1990-01-01 00:00:00+00:00    NaN
1      01048  climate_summary  wind_gust_max 1990-01-02 00:00:00+00:00    NaN
2      01048  climate_summary  wind_gust_max 1990-01-03 00:00:00+00:00    5.0
3      01048  climate_summary  wind_gust_max 1990-01-04 00:00:00+00:00    9.0
4      01048  climate_summary  wind_gust_max 1990-01-05 00:00:00+00:00    7.0

   quality
0      NaN
1      NaN
2     10.0
3     10.0
4     10.0

Receiving of stations for defined parameters using the wetterdienst client:

# Get list of all stations for daily climate summary data in JSON format
wetterdienst dwd observations stations --parameter=kl --resolution=daily --period=recent

# Get daily climate summary data for specific stations
wetterdienst dwd observations values --station=1048,4411 --parameter=kl --resolution=daily --period=recent

Further examples (code samples) can be found in the examples folder.