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.
@article{arxiv.2509.08816,
title = {A Benchmark Dataset for Satellite-Based Estimation and Detection of Rain},
author = {Simon Pfreundschuh and Malarvizhi Arulraj and Ali Behrangi and Linda Bogerd and Alan James Peixoto Calheiros and Daniele Casella and Neda Dolatabadi and Clement Guilloteau and Jie Gong and Christian D. Kummerow and Pierre Kirstetter and Gyuwon Lee and Maximilian Maahn and Lisa Milani and Giulia Panegrossi and Rayana Palharini and Veljko Petković and Soorok Ryu and Paolo Sanò and Jackson Tan},
journal= {arXiv preprint arXiv:2509.08816},
year = {2025}
}