Foehn
Download MeteoSwiss Open Government Data — weather stations, radar, hail, forecasts and climate series — via Python API or CLI, as DataFrames or Parquet files
Install / Use
/learn @kayhendriksen/FoehnREADME
foehn downloads every MeteoSwiss OGD collection via the STAC API, converts CSV/TXT to Parquet with Polars, and optionally ingests everything into Databricks Unity Catalog Delta tables on a daily schedule.
Why foehn?
- 20+ collections in one command — weather stations, radar, hail maps, forecasts, climate scenarios, and more
- 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
pip install foehn
foehn download
Recent data (Jan 1 → yesterday) is downloaded and converted to Parquet under ./data/meteoswiss/.
Collections
MeteoSwiss organises its open data into five categories. Category B (atmosphere measurements — radio soundings, ceilometer, ozone, etc.) is not yet released (B1 radio soundings expected first half of 2026).
A — Ground-based measurements
Station-level time series in CSV, split into time slices (historical, recent, now). Converted to Parquet.
| Dataset | ID | Description | Frequencies | Stations | Parameters |
|---|---|---|---|---|---|
| smn | A1 | Automatic weather stations — the core SwissMetNet network. ~160 stations across Switzerland measuring temperature, humidity, pressure, precipitation, wind, radiation, sunshine, soil temperature, and dew point. | 10-min, hourly, daily, monthly, yearly | 158 | 181 |
| smn_precip | A2 | Automatic precipitation stations — rain-gauge-only network. Reports precipitation totals at multiple granularities. | 10-min, hourly, daily, monthly, yearly | 141 | 6 |
| smn_tower | A3 | Tower stations — tall mast measurements for temperature, humidity, wind (scalar + gusts), radiation, and sunshine at tower height. | 10-min, hourly, daily, monthly, yearly | 4 | 46 |
| nime | A5 | Manual precipitation stations — observer-read gauges reporting daily precipitation, plus fresh snow depth and snow cover. | daily, monthly, yearly | 273 | 17 |
| tot | A6 | Totaliser precipitation — remote alpine rain gauges read once per year, reporting precipitation reduced to hydrological year (Oct 1 – Sep 30). | yearly | 57 | 1 |
| pollen | A7 | Pollen stations — airborne pollen concentrations for 7 taxa: alder, birch, hazel, beech, ash, oak, and grasses (Poaceae). | hourly, daily, yearly | 16 | 28 |
| obs | A8 | Visual / meteorological observations — human-observed daily cloud cover, counts of days with rain, snowfall, hail, fog, and snow coverage. | daily, monthly, yearly | 20 | 27 |
| phenology | A9 | Phenological observations — day-of-year for lifecycle events (leaf unfolding, flowering, fruit maturity, leaf colouring, leaf drop) across 26 plant species including horse chestnut, beech, cherry, apple, grape vine, and larch. | yearly | 175 | 71 |
C — Climate data
| Key | ID | Description | Format |
|---|---|---|---|
| nbcn | C1 | Homogeneous climate stations — break-adjusted series for temperature, pressure, precipitation, sunshine, and cloud cover (29 stations). Used for long-term trend analysis. | CSV → Parquet |
| nbcn_precip | C2 | Homogeneous precipitation — break-adjusted precipitation series (46 stations). | CSV → Parquet |
| surface_derived_grid | C3 | Ground-based spatial analyses — gridded fields of precipitation, temperature, and sunshine duration derived from station interpolation. | NetCDF (opt-in) |
| satellite_derived_grid | C4 | Satellite-based spatial analyses — gridded radiation, cloud cover, and land surface temperature derived from satellite. | NetCDF (opt-in) |
| climate_normals | C6 | Station normals — 30-year reference averages for 1961–1990 and 1991–2020. Monthly values per station. | TXT → Parquet |
| climate_normals_* | C7 | Spatial normals — gridded 30-year reference maps for precipitation, sunshine, and temperature (both reference periods). | NetCDF / GeoTIFF (opt-in) |
| climate_scenarios | C8 | CH2025 local scenarios — station-level climate projections. | CSV → Parquet |
| climate_scenarios_grid | C9 | CH2025 gridded scenarios — spatially gridded climate projections. | NetCDF (opt-in) |
D — Radar data
| Key | ID | Description | Format |
|---|---|---|---|
| radar_precip | D1 | Precipitation radar — composite precipitation grids at 5–10 min intervals. | HDF5 (opt-in) |
| radar_hail | D3 | Hail radar — probability-of-hail grids at 5 min intervals. | HDF5 (opt-in) |
Radar collections are large and require --grids to download.
E — Forecast data
| Key | ID | Description | Format |
|---|---|---|---|
| forecast_icon_ch1 | E2 | ICON-CH1-EPS — 1 km ensemble forecast model over Switzerland. | GRIB2 (opt-in) |
| forecast_icon_ch2 | E3 | ICON-CH2-EPS — 2.1 km ensemble forecast model. | GRIB2 (opt-in) |
| forecast_local | E4 | Local point forecasts — forecasts for ~5,600 points (stations + postal codes) covering temperature, precipitation, wind, radiation, and more (32 parameters). | CSV → Parquet |
GRIB2 forecast collections are large and require --grids to download.
Hail hazard maps
Static spatial reference grids showing expected hail grain size (cm) at different return periods. These are not categorised under A–E because they are static hazard assessments, not measured or forecasted time series — they represent probabilistic climatological analyses published as fixed reference maps.
| Key | Description | Format |
|---|---|---|
| hail_hazard_10y | Hail grain size — 10-year return period | NetCDF / GeoTIFF (opt-in) |
| hail_hazard_20y | Hail grain size — 20-year return period | NetCDF / GeoTIFF (opt-in) |
| hail_hazard_50y | Hail grain size — 50-year return period | NetCDF / GeoTIFF (opt-in) |
| hail_hazard_100y | Hail grain size — 100-year return period | NetCDF / GeoTIFF (opt-in) |
Time slices
MeteoSwiss splits CSV data into three time slices, encoded in the filename:
| Slice | Range | Update frequency | Frequencies |
|---|---|---|---|
| recent | Jan 1 this year → yesterday | Daily at 12:00 UTC | 10-min, hourly, daily, monthly |
| historical | Start of measurement → Dec 31 last year | Once per year (early January) | 10-min, hourly, daily, monthly |
| now | Yesterday 12:00 UTC → now | Every 10 minutes | 10-min, hourly only |
Some collections (phenology, totaliser, yearly aggregates) don't use time slices — they publish a single file per station.
All timestamps are UTC. For 10-min and hourly data the timestamp marks the end of the interval (16:00 = 15:50:01–16:00:00). For daily, monthly, and yearly data the timestamp marks the start (2023-06-01 = the whole of June).
Installation
From PyPI:
pip install foehn
From source:
git clone https://github.com/kayhendriksen/foehn
cd foehn
pip install -e .
With Databricks extras (PySpark + Delta):
pip install "foehn[databricks]"
Requires Python ≥ 3.10.
Python API
Use foehn directly from notebooks or scripts:
import foehn
# List all available datasets
foehn.list_datasets()
# [{'dataset': 'smn', 'collection_id': 'ch.meteoschweiz.ogd-smn', 'category': 'A',
# 'subcategory': 'A1', 'description': 'Automatic weather stations',
# 'format': 'CSV', 'frequencies': ['t', 'h', 'd', 'm'],
# 'time_slices': ['historical', 'recent', 'now']}, ...]
# Load data directly into a Polars DataFrame (nothing written to disk)
df = foehn.load("smn", station="BER", frequency="d")
# Filter by multiple stations and frequencies
df = foehn.load("smn", station=["BER", "ZUR"], frequency=["d", "h"])
# Include historical data
df = foehn.load("smn", station="BER", frequency="d", time_slice=["historical", "recent"])
# Explore dataset metadata (fetched live from the API)
foehn.parameters("smn") # column name mappings: shortname, description, unit, type, ...
foehn.stations("smn") # station info: abbr, name, canton, altitude, lat, lon, ...
foehn.inventory("smn") # what each station measures and since when
# Download a single dataset to disk
foehn.download("smn", data_dir="./data/meteoswiss")
# Download with specific time slices
foehn.download("smn", time_slice=["historical", "recent"])
# Convert downloaded CSVs to Parquet
foehn.to_parquet("smn", data_dir="./data/meteoswiss")
CLI reference
The CLI uses subcommands that mirror the Python API:
foehn list
List all available datasets.
foehn list
foehn download [DATASET...]
Download datasets. Without arguments, downloads all CSV collections. Specify one or more datasets to download specific ones.
