foehn
Allows optional ingestion of MeteoSwiss datasets into Databricks Unity Catalog Delta tables on a daily schedule, with support for deploying a daily job and historical backfill via a Declarative Automation Bundle.
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
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.
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.jsonNo 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
Related MCP server: Weather MCP Server
Quick start
pip install foehn
foehn downloadRecent data (Jan 1 to yesterday) is downloaded and converted to Parquet under ./data/meteoswiss/.
Installation
From PyPI:
pip install foehnFrom source:
git clone https://github.com/kayhendriksen/foehn
cd foehn
pip install -e .With extras:
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
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 pollen
foehn load smn --station BER --frequency dThe 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 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 storeNetCDF 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
All 20+ MeteoSwiss datasets, categories, and time slice conventions | |
Loading data, metadata, downloading, and Parquet conversion | |
NetCDF grids as xarray Datasets and Zarr stores | |
All subcommands, flags, and environment variables | |
Setup, configuration, and available tools | |
Declarative Automation Bundle deployment |
Data sources
STAC API | |
Documentation | |
MeteoSwiss OGD |
License
MIT
This server cannot be deployed
Maintenance
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