meteolibre-dev/weather_france_radar_satellite_gs

<img src="https://cdn-uploads.huggingface.co/production/uploads/6229c4e279337db2b0bf0c2e/O2JSZdbc_aEnHoLGtbcRg.png" alt="drawing" width="200"/>

This dataset is a ready-to-use fusion of multiple datasets for the France region, including:

• —Ground station data: Sourced from data.gouv.fr with a 1-hour resolution. It includes 7 weather KPIs: "RR1", "FF", "DD", "T", "U", "PMER", and "VV".

• —Radar imagery: Radar rainfall accumulation images from the Météo-France open data initiative.

• —Geospatial data: Land cover and ground height information from EarthEnv and OpenTopography.

• —Satellite imagery: Sourced from the EUMETSAT platform, containing a subset of channels: vis_04, vis_09, nir_13, and nir_16.

Time Period

The data covers the period from January 2025 to July 2025.

Resolution and Projection

All data have a 1km spatial resolution (using EPSG:32630) and a 30-minute temporal resolution.

Data Description

The final dataset is structured in .zip files as follows:

• —ground_height_image/: Contains ground height information as NumPy .npz files (256x256 images).

• —groundstation/: Contains ground station readings as arrays of shape (9, 256, 256, 7), where 9 is the number of time steps and 7 is the number of KPIs.

• —index.json: A JSON file that indexes the entire dataset.

• —landcover/, landcover_image/: Contain land cover data (256x256 images).

• —radar/: Contains radar rainfall accumulation imagery as arrays of shape (9, 256, 256), where 9 is the number of time steps.

• —satellite/: Contains satellite imagery as arrays of shape (9, 256, 256, 4), where 9 is the number of time steps and 4 is the number of selected channels.

Each subdirectory contains .npz files for different timestamps and locations, all indexed by index.json.

Data Visualization

Here's a quick visualization of the data:

We extracted full images of France and segmented them into smaller patches for easier ingestion into a training pipeline.

Quick start (remote)

import numpy as np
from datasets import load_dataset

# The command is exactly the same!
# The library handles the sharded files behind the scenes.
ds = load_dataset("meteolibre-dev/mtg_meteofrance_256", streaming=True)

# You can iterate over it as if it were a single file
for example in ds["train"]:

    # Get the byte string
    satellite_bytes = example['satellite']
    # Convert bytes back to a NumPy array
    # You need to specify the correct dtype and shape
    satellite_array = np.frombuffer(satellite_bytes, dtype=np.int16).reshape(9, 4, 256, 256)

    hour = example['hour']
    minute = example['minute']
    datetime = example['datetime']
    radar =  np.frombuffer(example['radar'], dtype=np.float32).reshape(9, 256, 256)
    groundstation =  np.frombuffer(example['groundstation'], dtype=np.float32).reshape(9, 256, 256, 7)
    ground_height =  np.frombuffer(example['ground_height'], dtype=np.float32).reshape(256, 256)
    landcover =  np.frombuffer(example['landcover'], dtype=np.float32).reshape(256, 256, 4)

Quick start (local)

First the command :

hf download meteolibre-dev/mtg_meteofrance_256 --repo-type dataset --local-dir data/