A Benchmark Dataset for Satellite-Based Estimation and Detection of Rain
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914032488808448 |
|---|---|
| author | Pfreundschuh, Simon Arulraj, Malarvizhi Behrangi, Ali Bogerd, Linda Calheiros, Alan James Peixoto Casella, Daniele Dolatabadi, Neda Guilloteau, Clement Gong, Jie Kummerow, Christian D. Kirstetter, Pierre Lee, Gyuwon Maahn, Maximilian Milani, Lisa Panegrossi, Giulia Palharini, Rayana Petković, Veljko Ryu, Soorok Sanò, Paolo Tan, Jackson |
| author_facet | Pfreundschuh, Simon Arulraj, Malarvizhi Behrangi, Ali Bogerd, Linda Calheiros, Alan James Peixoto Casella, Daniele Dolatabadi, Neda Guilloteau, Clement Gong, Jie Kummerow, Christian D. Kirstetter, Pierre Lee, Gyuwon Maahn, Maximilian Milani, Lisa Panegrossi, Giulia Palharini, Rayana Petković, Veljko Ryu, Soorok Sanò, Paolo Tan, Jackson |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_08816 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Benchmark Dataset for Satellite-Based Estimation and Detection of Rain Pfreundschuh, Simon Arulraj, Malarvizhi Behrangi, Ali Bogerd, Linda Calheiros, Alan James Peixoto Casella, Daniele Dolatabadi, Neda Guilloteau, Clement Gong, Jie Kummerow, Christian D. Kirstetter, Pierre Lee, Gyuwon Maahn, Maximilian Milani, Lisa Panegrossi, Giulia Palharini, Rayana Petković, Veljko Ryu, Soorok Sanò, Paolo Tan, Jackson Atmospheric and Oceanic Physics 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. |
| title | A Benchmark Dataset for Satellite-Based Estimation and Detection of Rain |
| topic | Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2509.08816 |