foehn 0.2.1

Download MeteoSwiss Open Government Data and convert to Parquet / Delta tables

MeteoSwiss Open Data → Parquet → Databricks Delta tables

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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 Snappy 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

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Quick start

pip install foehn
foehn

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

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Collections

Key	Description	Format
smn	Automatic weather stations (A1)	CSV → Parquet
smn_precip	Automatic precipitation stations (A2)	CSV → Parquet
smn_tower	Tower stations (A3)	CSV → Parquet
nime	Manual precipitation stations (A5)	CSV → Parquet
tot	Totaliser precipitation (A6)	CSV → Parquet
obs	Visual observations (A8)	CSV → Parquet
pollen	Pollen stations (A7)	CSV → Parquet
phenology	Phenological observations (A9)	CSV → Parquet
nbcn	Homogeneous climate stations (C1)	CSV → Parquet
nbcn_precip	Homogeneous precipitation (C2)	CSV → Parquet
climate_normals	Station normals 1961–1990 / 1991–2020 (C6)	TXT → Parquet
climate_normals_*	Spatial normals (C7)	NetCDF / GeoTIFF
surface_derived_grid	Spatial analyses — precip, temp, sunshine (C3/C4)	NetCDF
satellite_derived_grid	Spatial analyses — radiation, clouds (C5)	NetCDF
climate_scenarios	CH2025 local scenarios (C8)	CSV → Parquet
climate_scenarios_grid	CH2025 gridded scenarios (C9)	NetCDF
hail_hazard_*	Hail hazard maps	NetCDF / ZIP
forecast_local	Local point forecasts (E4)	CSV → Parquet
forecast_icon_ch1/ch2	ICON-CH1/CH2-EPS (E2/E3)	GRIB2 (opt-in)
radar_precip/hail	Precipitation + hail radar (D1/D3)	HDF5 (opt-in)

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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.

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Python API

Use foehn directly from notebooks or scripts:

import foehn

# List all available collections
foehn.list_collections()
# [{'category': 'CSV', 'key': 'smn', 'collection_id': 'ch.meteoschweiz.ogd-smn'}, ...]

# Download a single collection
foehn.fetch("smn", data_dir="./data/meteoswiss")

# Download with specific time slices
foehn.fetch("smn", data_types=["historical", "recent"])

# Convert downloaded CSVs to Parquet
foehn.convert("smn", data_dir="./data/meteoswiss")

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CLI reference

foehn [options]

Time range — recent (Jan 1 this year → yesterday) is always included; flags extend it:

Flag	Description
(none)	Recent only — Jan 1 this year → yesterday, updated daily at 12 UTC
--historical	Also fetch full archive — start of measurement → Dec 31 last year
--now	Also fetch realtime slice — yesterday 12 UTC → now, 10-min updates
--all	All three slices: historical + recent + now

Behaviour:

Flag	Description
--full-refresh	Ignore incremental tracking, re-download everything
--convert-only	Convert existing CSVs to Parquet without downloading

Output:

Flag	Description
--list	List available collections and exit
--grids	Also fetch GRIB2, radar HDF5, NetCDF, GeoTIFF (large)
--no-parquet	Skip conversion, keep raw CSVs only
--data-dir PATH	Output root (default: ./data/meteoswiss)

Parquet files land in <data-dir>/parquet/<collection>/.

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Environment variables

Settings can also be configured via environment variables. CLI flags always take precedence.

Variable	Equivalent	Description
FOEHN_DATA_DIR	--data-dir	Root data directory
FOEHN_FULL_REFRESH	--full-refresh	Set to 1, true, or yes to ignore incremental tracking

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Databricks pipeline

The recommended setup uses Declarative Automation Bundles.

1. Set variables:

export BUNDLE_VAR_host=https://adb-xxx.azuredatabricks.net
export BUNDLE_VAR_alert_email=[e-mail]

2. Deploy:

pip install databricks-cli
databricks bundle validate
databricks bundle deploy -t prod

This deploys two jobs:

• foehn_daily — runs at 13:30 UTC every day; downloads recent data and refreshes Delta tables
• foehn_historical — paused by default; trigger manually for first run or on Jan 1 for the annual archive slice

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Data sources

STAC API	https://data.geo.admin.ch/api/stac/v1
Documentation	https://opendatadocs.meteoswiss.ch
MeteoSwiss OGD	https://github.com/MeteoSwiss/opendata

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License

MIT