PeakWeather

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PeakWeather is a high-resolution, benchmark-ready dataset for spatiotemporal weather modeling.

Key Features

  • High-resolution observations: 10-minute interval data spanning 2017-2025 over 302 SwissMetNet stations distributed across Switzerland

  • Multiple variables: Temperature, pressure, humidity, wind, radiation, precipitation and more

  • Topographic descriptors: Elevation, slope, aspect, and surface roughness to describe the Swiss complex terrain

  • NWP baselines: Ensemble forecasts from ICON-CH1-EPS, the state-of-the-art numerical prediction model operational at MeteoSwiss

  • Ideal for: Many tasks including time series forecasting, missing data imputation, virtual sensing, and graph structure learning

Stations

Quickstart

Install PeakWeather from PyPI:

pip install peakweather

This installs the base package with support for station measurements and NWP predictions. If access to the topographical descriptors is desired, then there are additional required libraries that can be installed via

pip install peakweather[extended]       # Topographical descriptors and NWP forecasts

Alternatively, install directly from GitHub:

pip install git+https://github.com/MeteoSwiss/PeakWeather.git                         # Base
pip install "peakweather[extended] @ git+https://github.com/MeteoSwiss/PeakWeather"   # With extras

First use

When the PeakWeatherDataset is instantiated for the first time, the weather data is downloaded.

from peakweather.dataset import PeakWeatherDataset
# Download the data in the current working directory
ds = PeakWeatherDataset(root=None)
# Or specify a custom directory
# ds = PeakWeatherDataset(root="path/to/data")

For detailed usage and parameter descriptions, please refer to the documentation of the PeakWeatherDataset class, which provides extended documentation on its functionality and options.

Authors

Citation

If you use PeakWeather in your research, please cite:

@misc{zambon2025peakweather,
   title={PeakWeather: MeteoSwiss Weather Station Measurements for Spatiotemporal Deep Learning},
   author={Zambon, Daniele and Cattaneo, Michele and Marisca, Ivan and Bhend, Jonas and Nerini, Daniele and Alippi, Cesare},
   year={2025},
   eprint={2506.13652},
   archivePrefix={arXiv},
   primaryClass={cs.LG},
   url={https://arxiv.org/abs/2506.13652},
}

Documentation

Examples

API