foehn

[foehn]

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

[PyPI Latest Release] [Python Versions] [MIT License] [OpenSSF Scorecard] [Monthly Downloads]

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

pip install foehnfoehn download

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

Installation

From PyPI:

pip install foehn

From source:

git clone https://github.com/kayhendriksen/foehncd foehnpip install -e .

With extras:

pip install "foehn[databricks]"   # PySpark + Delta, and Polars' build for CPUs without AVX2pip install "foehn[mcp]"          # MCP serverpip install "foehn[grids]"        # xarray + Zarr for all gridded data (NetCDF, GRIB2, radar)

Requires Python 3.11 or later.

Python API

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

foehn download smn pollenfoehn 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

ds = foehn.open_dataset("surface_derived_grid", match="rhiresd")  # NetCDF climate gridds = foehn.open_dataset("forecast_icon_ch1", match="YYYYMMDDHHMM-0-t_2m-ctrl")  # one GRIB2 fieldds = 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

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

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

Documentation

Data sources

License

MIT