A Benchmark Dataset for Satellite-Based Estimation and Detection of Rain.

Journal: Scientific data
Published Date: Jan 15, 2026

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.

Authors

  • Simon Pfreundschuh
    Department of Atmospheric Science, Colorado State University, Fort Collins, USA. [email protected].
  • Malarvizhi Arulraj
    Earth System Science Interdisciplinary Center, University of Maryland, Maryland, USA.
  • Ali Behrangi
    Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, USA.
  • Linda Bogerd
    Earth System Science Interdisciplinary Center, University of Maryland, Maryland, USA.
  • Alan James Peixoto Calheiros
    Instituto Nacional de Pesquisas Espaciais, State of São Paulo, São Paulo, Brazil.
  • Daniele Casella
    Institute of Atmospheric Sciences and Climate, Italian National Research Council, Roma, Italy.
  • Neda Dolatabadi
    Department of Civil and Environmental Engineering, University of California Irvine, Irvine, USA.
  • Clement Guilloteau
    Department of Civil and Environmental Engineering, University of California Irvine, Irvine, USA.
  • Jie Gong
    Institute of Geological Survey, China University of Geosciences, Wuhan 430074, China.
  • Christian D Kummerow
    Department of Atmospheric Science, Colorado State University, Fort Collins, USA.
  • Pierre Kirstetter
    School of Meteorology & School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, USA.
  • Gyuwon Lee
    Department of Atmospheric Sciences, Kyungpook National University, Buk-gu, Daegu, South Korea.
  • Maximilian Maahn
    Institute for Meteorology, Leipzig University, Leipzig, Germany.
  • Lisa Milani
    Earth System Science Interdisciplinary Center, University of Maryland, Maryland, USA.
  • Giulia Panegrossi
    Institute of Atmospheric Sciences and Climate, Italian National Research Council, Roma, Italy.
  • Rayana Palharini
    Departamento de Prevención de Riegos y Medio Ambiente, Universidad Tecnológica Metropolitana, Santiago, Chile.
  • Veljko Petković
    Earth System Science Interdisciplinary Center, University of Maryland, Maryland, USA.
  • Soorok Ryu
    Department of Atmospheric Sciences, Kyungpook National University, Buk-gu, Daegu, South Korea.
  • Paolo Sanó
    Institute of Atmospheric Sciences and Climate, Italian National Research Council, Roma, Italy.
  • Jackson Tan
    NASA Goddard Space Flight Center, Greenbelt, USA.

Keywords

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