Year: 2026
Type: data-paper
Abstract: Accurately tracking the global distribution of precipitation is essential for both research and operational meteorology. Satellite observations remain the only means of achieving consistent, global 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. To address this, the International Precipitation Working Group has developed SatRain, the first AI benchmark dataset for satellite-based detection and estimation of rain. SatRain integrates multi-sensor satellite observations from the primary platforms used in precipitation remote sensing with high-quality reference precipitation estimates derived from gauge-corrected ground-based radar composites over the conterminous United States. It offers a standardized evaluation protocol and out-of-distribution testing data from Asia and Europe to enable robust and reproducible comparisons across machine learning approaches. In addition to 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 global precipitation estimates.
Source: Scientific Data
Authors: Simon Pfreundschuh, Malarvizhi Arulraj, Ali Behrangi, Linda Bogerd, Alan James Peixoto Calheiros, Daniele Casella, Neda Dolatabadi, Clement Guilloteau, Jie Gong, Christian D. Kummerow, Pierre Kirstetter, Gyuwon Lee, Maximilian Maahn, Lisa Milani, Giulia Panegrossi, Rayana Palharini, Veljko Petković, Soorok Ryu, Paolo Sanó, Jackson Tan
Institutions: Colorado State University, Earth System Science Interdisciplinary Center, University of Arizona, Goddard Space Flight Center, Instituto Nacional de Pesquisas Espaciais, Institute of Atmospheric Sciences and Climate, National Research Council, University of California, Irvine, University of Oklahoma, Kyungpook National University, Leipzig University, Metropolitan University of Technology, Cooperative Institute for Satellite Earth System Studies, University of Maryland, College Park, University of Maryland, Baltimore County
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
Estimated APC paid: $2,490
Award: G8734055976
OpenAlex ID: W7124266105