MeteoSwiss Open Data — Python API, CLI & MCP server · tabular as DataFrames/Parquet, gridded as xarray/Zarr

foehn downloads every MeteoSwiss OGD collection via the STAC API, converts CSV/TXT station data to Parquet with Polars, and opens gridded collections — NetCDF climate grids, GRIB2 forecasts, and ODIM radar composites — as xarray Datasets or Zarr stores. It can optionally ingest everything into Databricks Unity Catalog Delta tables on a daily schedule, and ships an MCP server so LLMs can query Swiss weather data directly.

Daily weather in Bern, powered by foehn

Daily weather in Bern, powered by foehn's MCP server and MeteoSwiss open data.

Why foehn?

20+ collections in one command — weather stations, radar, hail maps, forecasts, climate scenarios, and more
Tabular and gridded — CSV station data as Polars DataFrames or Parquet; NetCDF, GRIB2 and ODIM radar grids as xarray Datasets or Zarr stores
MCP server for LLMs — give your favorite LLM live access to MeteoSwiss data with the MCP server
Significantly smaller on disk — columnar Parquet with Zstandard compression vs. raw CSVs
Incremental by default — only re-downloads files that changed since your last run, tracked per dataset and time slice in _last_run.json
No Spark required locally — download + conversion uses Polars only; Spark is optional for Delta ingestion
Ships a Declarative Automation Bundle — ready-to-deploy daily job and historical backfill, no pipeline config needed

Quick start

bash

pip install foehn
foehn download

Recent data (Jan 1 to yesterday) is downloaded and converted to Parquet under ./data/meteoswiss/.

Installation

From PyPI:

bash

pip install foehn

From source:

bash

git clone https://github.com/kayhendriksen/foehn
cd foehn
pip install -e .

With extras:

bash

pip install "foehn[databricks]"   # PySpark + Delta, and Polars' build for CPUs without AVX2
pip install "foehn[mcp]"          # MCP server
pip install "foehn[grids]"        # xarray + Zarr for all gridded data (NetCDF, GRIB2, radar)

Requires Python 3.11 or later.

Python API

python

import foehn

df = foehn.load("smn", station="BER", frequency="d")

Load data directly into Polars DataFrames, explore metadata, download to disk, and convert to Parquet — all from Python. See the full Python API documentation.

CLI

bash

foehn download smn pollen
foehn load smn --station BER --frequency d

The CLI mirrors the Python API with subcommands for downloading, converting, loading, and inspecting metadata. See the full CLI documentation.

Gridded data

python

ds = foehn.open_dataset("surface_derived_grid", match="rhiresd")  # NetCDF climate grid
ds = foehn.open_dataset("forecast_icon_ch1", match="YYYYMMDDHHMM-0-t_2m-ctrl")  # one GRIB2 field
ds = foehn.open_dataset("radar_precip", match="cpcYYDDDHHMM")  # one radar composite
# Forecast and radar files roll off after a day / two weeks: fill in a recent time.
# A match that finds nothing lists the times the collection holds now.
foehn.to_zarr("surface_derived_grid", match="rhiresd")  # Zarr store

NetCDF climate grids/normals/scenarios, GRIB2 forecasts (ICON-CH1/CH2, KENDA), and HDF5/ODIM radar composites all open as xarray Datasets instead of DataFrames. One extra covers them: pip install "foehn[grids]". See the gridded data documentation.

MCP server

json

{
  "mcpServers": {
    "foehn": {
      "command": "foehn",
      "args": ["mcp"]
    }
  }
}

Give any MCP-compatible LLM live access to MeteoSwiss data. See the full MCP server documentation.

Documentation


Collections	All 20+ MeteoSwiss datasets, categories, and time slice conventions
Python API	Loading data, metadata, downloading, and Parquet conversion
Gridded data	NetCDF grids as xarray Datasets and Zarr stores
CLI	All subcommands, flags, and environment variables
MCP Server	Setup, configuration, and available tools
Databricks Pipeline	Declarative Automation Bundle deployment