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\n $\"foehn\"$ \n

\n\n

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

\n\n

\n \n $\"PyPI$ \n \n \n $\"Python$ \n \n \n $\"MIT$ \n \n \n $\"OpenSSF$ \n \n \n $\"Monthly$ \n \n

\n\n---\n\nfoehn downloads every [MeteoSwiss OGD](https://github.com/MeteoSwiss/opendata) collection via the STAC API, converts CSV/TXT station data to Parquet with [Polars](https://pola.rs), and opens gridded collections — NetCDF climate grids, GRIB2 forecasts, and ODIM radar composites — as [xarray](https://xarray.dev) Datasets or [Zarr](https://zarr.dev) stores. It can optionally ingest everything into [Databricks](https://www.databricks.com) Unity Catalog Delta tables on a daily schedule, and ships an [MCP server](https://modelcontextprotocol.io) so LLMs can query Swiss weather data directly.\n\n

\n $\"Daily$ \n

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

\n\n## Why foehn?\n\n- **20+ collections in one command** — weather stations, radar, hail maps, forecasts, climate scenarios, and more\n- **Tabular and gridded** — CSV station data as Polars DataFrames or Parquet; NetCDF, GRIB2 and ODIM radar grids as xarray Datasets or Zarr stores\n- **MCP server for LLMs** — give your favorite LLM live access to MeteoSwiss data with the MCP server\n- **Significantly smaller on disk** — columnar Parquet with Zstandard compression vs. raw CSVs\n- **Incremental by default** — only re-downloads files that changed since your last run, tracked via `_last_run.json`\n- **No Spark required locally** — download + conversion uses Polars only; Spark is optional for Delta ingestion\n- **Ships a Declarative Automation Bundle** — ready-to-deploy daily job and historical backfill, no pipeline config needed\n\n---\n\n## Quick start\n\n```bash\npip install foehn\nfoehn download\n```\n\nRecent data (Jan 1 to yesterday) is downloaded and converted to Parquet under `./data/meteoswiss/`.\n\n $\"foehn$ \n\n---\n\n## Installation\n\n**From PyPI:**\n```bash\npip install foehn\n```\n\n**From source:**\n```bash\ngit clone https://github.com/kayhendriksen/foehn\ncd foehn\npip install -e .\n```\n\n**With extras:**\n```bash\npip install \"foehn[databricks]\" # PySpark + Delta\npip install \"foehn[mcp]\" # MCP server\npip install \"foehn[grids]\" # xarray + Zarr for all gridded data (NetCDF, GRIB2, radar)\n```\n\nRequires Python 3.11 or later.\n\n---\n\n## Python API\n\n```python\nimport foehn\n\ndf = foehn.load(\"smn\", station=\"BER\", frequency=\"d\")\n```\n\nLoad data directly into Polars DataFrames, explore metadata, download to disk, and convert to Parquet — all from Python. See the [full Python API documentation](https://github.com/kayhendriksen/foehn/blob/main/docs/python-api.md).\n\n---\n\n## CLI\n\n```bash\nfoehn download smn pollen\nfoehn load smn --station BER --frequency d\n```\n\nThe CLI mirrors the Python API with subcommands for downloading, converting, loading, and inspecting metadata. See the [full CLI documentation](https://github.com/kayhendriksen/foehn/blob/main/docs/cli.md).\n\n---\n\n## Gridded data\n\n```python\nds = foehn.open_dataset(\"surface_derived_grid\", match=\"rhiresd\") # NetCDF climate grid\nds = foehn.open_dataset(\"forecast_icon_ch1\", match=\"202605231500-0-t_2m-ctrl\") # one GRIB2 field\nds = foehn.open_dataset(\"radar_precip\", match=\"cpc2613000000\") # one radar composite\nfoehn.to_zarr(\"surface_derived_grid\", match=\"rhiresd\") # Zarr store\n```\n\nNetCDF 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](https://github.com/kayhendriksen/foehn/blob/main/docs/grids.md).\n\n---\n\n## MCP server\n\n```json\n{\n \"mcpServers\": {\n \"foehn\": {\n \"command\": \"foehn\",\n \"args\": [\"mcp\"]\n }\n }\n}\n```\n\nGive any MCP-compatible LLM live access to MeteoSwiss data. See the [full MCP server documentation](https://github.com/kayhendriksen/foehn/blob/main/docs/mcp-server.md).\n\n---\n\n## Documentation\n\n| | |\n|---|---|\n| [Collections](https://github.com/kayhendriksen/foehn/blob/main/docs/collections.md) | All 20+ MeteoSwiss datasets, categories, and time slice conventions |\n| [Python API](https://github.com/kayhendriksen/foehn/blob/main/docs/python-api.md) | Loading data, metadata, downloading, and Parquet conversion |\n| [Gridded data](https://github.com/kayhendriksen/foehn/blob/main/docs/grids.md) | NetCDF grids as xarray Datasets and Zarr stores |\n| [CLI](https://github.com/kayhendriksen/foehn/blob/main/docs/cli.md) | All subcommands, flags, and environment variables |\n| [MCP Server](https://github.com/kayhendriksen/foehn/blob/main/docs/mcp-server.md) | Setup, configuration, and available tools |\n| [Databricks Pipeline](https://github.com/kayhendriksen/foehn/blob/main/docs/databricks.md) | Declarative Automation Bundle deployment |\n\n---\n\n## Data sources\n\n| | |\n|---|---|\n| STAC API | https://data.geo.admin.ch/api/stac/v1 |\n| Documentation | https://opendatadocs.meteoswiss.ch |\n| MeteoSwiss OGD | https://github.com/MeteoSwiss/opendata |\n\n---\n\n## License\n\nMIT\n\n\n","changelogMarkdown","videoKey","requiredSecretsJson","publishedAt","priceCents","trialDays",0,"upstream",{"_103":104,"_105":106,"_107":108,"_109":110,"_111":65,"_112":113},"requiredSecrets",[],"tags",[93,94,95,96,97,98,99,100,101,102],"assets",[],"stats",{"_91":80,"_92":80},"ratingAvg","reviewCount","climate","databricks","meteoswiss","open-data","parquet","polars","python","weather","xarray","zarr","topics",[93,94,95,96,97,98,99,100,101,102],"license","MIT","language","Python","registryName","io.github.kayhendriksen/foehn","homepage","packages",[114],{"_115":116,"_117":21,"_70":71},"registryType","pypi","identifier","ENV",{"_120":121,"_122":123,"_124":125},"ENVIRONMENT","production","TURNSTILE_SITE_KEY","0x4AAAAAAB8-rEI2gNDf_QhU","SENTRY_DSN","https://45fd1229f78089d5c3ee78be128030e1@o4510583261757440.ingest.de.sentry.io/4510584253448272"]