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 against which to evaluate new approaches. The dataset's richness supports a broad spectrum of spatiotemporal tasks, including time series forecasting at various scales, graph structure learning, imputation, and virtual sensing. As such, PeakWeather serves as a real-world benchmark to advance both foundational machine learning research, meteorology, and sensor-based applications.

翻译：准确的天气预报对于支持广泛的活动与决策过程以及减轻恶劣天气事件的影响至关重要。虽然传统的数值天气预报（NWP）仍是业务预报的基石，但机器学习正成为一种快速、灵活且可扩展的预测替代方案。我们推出PeakWeather——一个高质量的地面气象观测数据集，该数据集采集自瑞士联邦气象与气候办公室（MeteoSwiss）测量网络的地面站点，时间跨度超过8年，每10分钟记录一次。数据集包含来自瑞士复杂地形中302个站点位置的多类气象变量，并辅以数字高程模型导出的地形指数作为环境背景。同时提供当前业务化高分辨率NWP模式的集合预报作为基准预测，用于评估新方法的性能。该数据集的丰富性支持广泛的时空任务，包括多尺度时间序列预测、图结构学习、数据插补和虚拟传感。因此，PeakWeather可作为推进基础机器学习研究、气象学及传感器应用的现实世界基准。