.. PeakWeather documentation master file, created by
sphinx-quickstart on Tue Jun 24 21:18:50 2025.
You can adapt this file completely to your liking, but it should at least
contain the root `toctree` directive.
PeakWeather
=========================
.. raw:: html
**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
.. image:: ./_static/stations.png
:width: 400
:alt: Stations
Related Resources
-----------------
- **Dataset** access on **Hugging Face**:
| https://huggingface.co/datasets/meteoswiss/PeakWeather
- **GitHub** repository of the **library**:
| https://github.com/meteoswiss/peakweather
- **Paper** introducing the dataset:
| PeakWeather: MeteoSwiss Weather Station Measurements for Spatiotemporal Deep Learning.
| *Daniele Zambon, Michele Cattaneo, Ivan Marisca, Jonas Bhend, Daniele Nerini, Cesare Alippi.*
| Preprint 2025.
| https://arxiv.org/abs/2506.13652
- Code for an **application to forecasting**:
| https://github.com/Graph-Machine-Learning-Group/peakweather-forecasting
- Read the Docs **documentation**:
| https://peakweather.readthedocs.io/
Quickstart
----------
Install PeakWeather from PyPI:
.. code-block:: bash
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
.. code-block:: bash
pip install peakweather[extended] # Topographical descriptors and NWP forecasts
Alternatively, install directly from GitHub:
.. code-block:: bash
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.
.. code-block:: python
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
-------
- `Daniele Zambon `_
- `Michele Cattaneo `_
- `Ivan Marisca `_
Citation
--------
If you use PeakWeather in your research, please cite:
.. code-block:: bibtex
@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
-------------
.. toctree::
:maxdepth: 2
:caption: Examples
examples/peakweather_demo
.. toctree::
:maxdepth: 2
:caption: API
modules/index
genindex