meteo2zarr
High-Performance NWP to Cloud-Native Zarr Converter for Meteorological and Climate Data
Convert Numerical Weather Prediction outputs (AROME, ALADIN, ARPEGE, ECMWF IFS, GFS in FA, GRIB, NetCDF) into cloud-optimized, sharded, multi-dimensional Zarr stores with sliding meteorological accumulations and distributed streaming writes.
Key Features
- Distributed and Lazy Streaming: Powered by Dask and ProcessPoolExecutor for parallel ingestion and chunked writing without out-of-memory errors.
- Meteorological Accumulation Engine: Automatic decumulation of raw model precipitation and sliding window calculations (1h, 3h, 6h, 12h, 24h).
- Derived Diagnostics: Computation of vector magnitude (wind speed), direction, and unit conversions (Kelvin to Celsius, Pa to hPa).
- Cloud and Web Visualization Optimized: Calibrated chunking hierarchy designed for TiTiler-Xarray, MapLibre, and ndpyramid.
- Dual Execution Profile: Runs smoothly on local developer laptops as well as HPC clusters (Slurm).
Installation
# Standard installation
pip install meteo2zarr
# With GRIB support (eccodes / cfgrib)
pip install meteo2zarr[grib]
# With FA support (epygram)
pip install meteo2zarr[fa]
# Complete installation (all formats + pyramids)
pip install meteo2zarr[all]
Quickstart
1. Command Line Interface (CLI)
# Convert a local model run
meteo2zarr convert \
--model arome \
--run 2026030100 \
--input /path/to/raw/files \
--output ./zarr_output \
--dask-workers 4 \
--chunk-time 6
2. Python API
from datetime import datetime
from meteo2zarr import NWPConverter
# Initialize converter
converter = NWPConverter(
output_dir="./zarr_output",
dask_workers=4,
chunk_time=6,
)
# Run conversion
success = converter.convert(
input_dir="/path/to/raw/files",
model="arome",
run_date=datetime(2026, 3, 1, 0, 0),
dt_hours=1.0,
)
Architecture
graph TD
A[Raw NWP Files<br/>FA / GRIB / NetCDF] --> B[meteo2zarr Ingestion and Schema Mapper]
B --> C[Meteorological Processing Engine<br/>• Precipitation Decumulation<br/>• Sliding Windows: 1h, 3h, 6h, 12h, 24h<br/>• Derived Wind Speed and Direction]
C --> D[Dask Distributed Streaming Chunking]
D --> E[Cloud-Native Zarr Stores / Multiscale Pyramids]
Testing
pytest tests/
Acknowledgments
Special thanks to Mr. KERROUMI from Météo Algérie (ONM) for his valuable support and contributions during the development of the initial version of this code.
License
Distributed under the Apache 2.0 License. See LICENSE for more information.
1 maintainer
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