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[2506.13652] PeakWeather: MeteoSwiss Weather Station Measurements for Spatiotemporal Deep Learning
Summary
The paper introduces PeakWeather, a comprehensive dataset of weather measurements from MeteoSwiss, aimed at enhancing spatiotemporal deep learning applications in meteorology.
Why It Matters
Accurate weather forecasting is crucial for various sectors, and the PeakWeather dataset provides a robust resource for researchers and practitioners in machine learning and meteorology. It enables the development of advanced predictive models and supports diverse applications in weather prediction and analysis.
Key Takeaways
- PeakWeather dataset includes 10-minute weather observations over 8 years from 302 Swiss stations.
- The dataset supports various spatiotemporal tasks including time series forecasting and graph structure learning.
- Ensemble forecasts from operational NWP models serve as a baseline for evaluating new approaches.
- Rich dataset context includes topographical indices to enhance predictive modeling.
- PeakWeather aims to advance foundational machine learning research and sensor-based applications.
Computer Science > Machine Learning arXiv:2506.13652 (cs) [Submitted on 16 Jun 2025 (v1), last revised 12 Feb 2026 (this version, v2)] Title:PeakWeather: MeteoSwiss Weather Station Measurements for Spatiotemporal Deep Learning Authors:Daniele Zambon, Michele Cattaneo, Ivan Marisca, Jonas Bhend, Daniele Nerini, Cesare Alippi View a PDF of the paper titled PeakWeather: MeteoSwiss Weather Station Measurements for Spatiotemporal Deep Learning, by Daniele Zambon and 5 other authors View PDF HTML (experimental) Abstract:Accurate weather forecasts are essential for supporting a wide range of activities and decision-making processes, as well as mitigating the impacts of adverse weather events. While traditional numerical weather prediction (NWP) remains the cornerstone of operational forecasting, machine learning is emerging as a powerful alternative for fast, flexible, and scalable predictions. We introduce PeakWeather, a high-quality dataset of surface weather observations collected every 10 minutes over more than 8 years from the ground stations of the Federal Office of Meteorology and Climatology MeteoSwiss's measurement network. The dataset includes a diverse set of meteorological variables from 302 station locations distributed across Switzerland's complex topography and is complemented with topographical indices derived from digital height models for context. Ensemble forecasts from the currently operational high-resolution NWP model are provided as a baseline forecast agai...
