swiss-weatherdata 0.0.4

An API to easily download open access weather recordings from MeteoSwiss

Overview

This package provides a set of convenience tools to seamlessly download meteorological data collected and made available to the public as Open Government Data (OGD) by the Federal Office for Meteorology and Climatology MeteoSwiss. More information about the Open Data from MeteoSwiss is available here.

Currently, the data available through this interface concerns ground-base measurements (gbm) coming from

1. the automatic weather stations network SwissMetNet (SMN)

2. the swiss national basic climatological network (NBCN).

Important notice:

This package was not developed and is not suppported by MeteoSwiss. It however relies on the MeteoSwiss Open Data API which allows public access to meteorological data provided by the Federal Office for Meteorology and Climatology, and which can be used independently of this package.

Installation

pip install swiss-weatherdata

Usage

Import all functions at once:

import swiss_weatherdata.gbm as gbm

Information about weather stations and meteorological parameters

Get information about all SwissMetNet (SMN) weather stations in a pd.DataFrame:

gbm.get_stations_info(network='smn')

Look for a meteorological parameter:

gbm.get_meteo_parameters_info(network='smn')

The lookup argument allows to filter meteorological parameters by category, e.g., the following lists all parameters related to Wind:

gbm.get_meteo_parameters_info(lookup='wind', language='en', network='smn')

Same output but in french:

gbm.get_meteo_parameters_info(lookup='vent', language='fr', network='smn')

Currently, meteorological parameters are grouped into the following categories:

language='en'	language='fr'
Wind	Vent
Evaporation	évaporation
Radiation	Rayonnement
Snow	neige
Pressure	Pression
Humidity	Humidité
Precipitation	Précipitations
Sunshine	Ensoleillement
Temperature	Température

Get the parameter description from its shortname:

gbm.get_param_description(shortname='rre150d0', language='fr', network='smn')

Get the list of available parameters for a given weather station. For example, at Genève-Cointrin (GVE):

gbm.get_parameters_by_station('GVE', network='smn')

N.B. To get information about stations/parameters belonging to the Swiss National Basic Climatological Network, change the network to 'nbcn'.

Time-granularity of recordings

Recordings are generally available at different time granularities: 10 minutes, hourly, daily, monthly or yearly.

Each parameter has its own granularity, as specified in the output of get_meteo_parameters_info(), for instance:

parameter_shortname	parameter_description_en
tre200s0	'Air temperature 2 m above ground; current value'
tre200h0	'Air temperature 2 m above ground; hourly mean'
tre200d0	'Air temperature 2 m above ground; daily mean'
tre200m0	'Air temperature 2 m above ground; monthly mean'
tre200y0	'Air temperature 2 m above ground; annual mean'

Download recordings from a given weather station:

The function get_smn_measures() downloads data recorded at a given weather station for several meteorological parameters and for a specified period. It returns data organized in a Pandas pd.DataFrame.

Example 1: get daily maximum temperature and daily sum of precipitation recorded at Genève-Cointrin from May 15. 2025 and to the latest available record:

df = gbm.get_smn_measures(
    sta='GVE',
    parameters=['tre200dx', 'rka150d0'],
    beg='202405150000'
)

Example 2: get hourly mean temperature and hourly sum of precipitation recorded at Payerne from January 15. 2010 at 06:00 UTC to March 1. 2010 at 18:00 UTC:

df = gbm.get_smn_measures(
    sta='PAY',
    parameters=['tre200h0', 'rre150h0'],
    beg='201001150600',
    end='201003011800'
)

Example 3: get monthly temperature deviation from the climatological mean (1991-2020) at Genève-Cointrin from January 1983 to December 2025:

df = gbm.get_nbcn_measures(
    sta='GVE',
    parameters=['th9120mv'],
    beg='1983001010000',
    end='202512310000'
)

Examples

All previous examples are available in a Jupyter Notebook on GitHub.