Title: A Benchmark Dataset for Satellite-Based Estimation and Detection of Rain Show Chinese title

Title: 基于卫星的降雨估计和检测的基准数据集

Abstract: Accurately tracking the global distribution and evolution of precipitation is essential for both research and operational meteorology. Satellite observations remain the only means of achieving consistent, global-scale precipitation monitoring. While machine learning has long been applied to satellite-based precipitation retrieval, the absence of a standardized benchmark dataset has hindered fair comparisons between methods and limited progress in algorithm development. To address this gap, the International Precipitation Working Group has developed SatRain, the first AI-ready benchmark dataset for satellite-based detection and estimation of rain, snow, graupel, and hail. SatRain includes multi-sensor satellite observations representative of the major platforms currently used in precipitation remote sensing, paired with high-quality reference estimates from ground-based radars corrected using rain gauge measurements. It offers a standardized evaluation protocol to enable robust and reproducible comparisons across machine learning approaches. In addition to supporting algorithm evaluation, the diversity of sensors and inclusion of time-resolved geostationary observations make SatRain a valuable foundation for developing next-generation AI models to deliver more accurate, detailed, and globally consistent precipitation estimates. Show Chinese abstract

Abstract: 准确跟踪降水的全球分布和演变对于科研和业务气象学都是至关重要的。卫星观测仍然是实现一致的全球尺度降水监测的唯一手段。虽然机器学习早已应用于基于卫星的降水反演，但缺乏标准化的基准数据集阻碍了方法之间的公平比较，并限制了算法开发的进展。为解决这一差距，国际降水工作组开发了SatRain，这是第一个面向人工智能的基准数据集，用于基于卫星的降雨、降雪、冰雹和冰粒的检测和估计。SatRain包含代表当前降水遥感中主要平台的多传感器卫星观测，并与通过雨量计测量校正的高质量地面雷达参考估计配对。它提供了一个标准化的评估协议，以实现机器学习方法之间的稳健和可重复比较。除了支持算法评估外，传感器的多样性以及包括时间分辨的静止轨道观测，使SatRain成为开发下一代人工智能模型的宝贵基础，以提供更准确、详细和全球一致的降水估计。