MeteoSwiss Open Data — Python API, CLI & MCP server · tabular as DataFrames/Parquet, gridded as xarray/Zarr
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 via `_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
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](docs/python-api.md).
---
## 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](docs/cli.md).
---
## Gridded data
```python
ds = foehn.open_dataset("surface_derived_grid", match="rhiresd") # NetCDF climate grid
ds = foehn.open_dataset("forecast_icon_ch1", match="202605231500-0-t_2m-ctrl") # one GRIB2 field
ds = foehn.open_dataset("radar_precip", match="cpc2613000000") # one radar composite
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](docs/grids.md).
---
## 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](docs/mcp-server.md).
---
## Documentation
| | |
|---|---|
| [Collections](docs/collections.md) | All 20+ MeteoSwiss datasets, categories, and time slice conventions |
| [Python API](docs/python-api.md) | Loading data, metadata, downloading, and Parquet conversion |
| [Gridded data](docs/grids.md) | NetCDF grids as xarray Datasets and Zarr stores |
| [CLI](docs/cli.md) | All subcommands, flags, and environment variables |
| [MCP Server](docs/mcp-server.md) | Setup, configuration, and available tools |
| [Databricks Pipeline](docs/databricks.md) | Declarative Automation Bundle deployment |
---
## Data sources
| | |
|---|---|
| STAC API | https://data.geo.admin.ch/api/stac/v1 |
| Documentation | https://opendatadocs.meteoswiss.ch |
| MeteoSwiss OGD | https://github.com/MeteoSwiss/opendata |
---
## License
MIT