GPROF-IR: An Improved Single-Channel Infrared Precipitation Retrieval for Merged Satellite Precipitation Products
Abstract
The Integrated Multi-Satellite Retrievals for the Global Precipitation Measurement (IMERG) product combines precipitation estimates from passive microwave (PMW) and infrared (IR) sensors to provide quasi-global precipitation estimates at half-hourly resolution. Because IR observations are primarily sensitive to cloud-top properties, however, IR-based precipitation estimates are generally less accurate than PMW estimates and can introduce artifacts into the merged precipitation fields. We introduce GPROF-IR, a neural-network-based precipitation retrieval designed to provide precipitation estimates from single-channel geostationary IR observations for the upcoming IMERG V08. GPROF-IR uses a convolutional neural network to exploit spatiotemporal information from sequences of half-hourly IR observations. We validate GPROF-IR against independent surface-based precipitation measurements over land and ocean. GPROF-IR substantially improves upon existing single-channel IR retrievals, including the IR retrieval used in IMERG V07 and more recent neural-network-based approaches. During overpasses of flagship PMW sensors, GPROF-IR is more accurate than the corresponding IMERG PMW estimates over CONUS and Austria, but not Korea. Over CONUS, GPROF-IR outperforms the merged IMERG V07 product for hourly and daily precipitation accumulations. Against shipborne disdrometer observations, GPROF-IR is more accurate than IMERG V07 in the tropics but slightly less accurate in the extratropics. Since geostationary IR observations are continuously available across much of the globe since 1983, GPROF-IR provides a promising basis for improving merged precipitation products and developing long-term, temporally consistent precipitation records.
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BibTeX
@misc{pfreundschuh2026gprofir,
title = {{GPROF-IR: An Improved Single-Channel Infrared Precipitation Retrieval for Merged Satellite Precipitation Products}},
author = {Simon Pfreundschuh and Christian D. Kummerow and Jackson Tan and George J. Huffman},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2605.07167},
eprint = {2605.07167},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2605.07167},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - GPROF-IR: An Improved Single-Channel Infrared Precipitation Retrieval for Merged Satellite Precipitation Products
AU - Simon Pfreundschuh
AU - Christian D. Kummerow
AU - Jackson Tan
AU - George J. Huffman
PY - 2026
DA - 2026/09/22
PB - arXiv
DO - 10.48550/arxiv.2605.07167
UR - https://arxiv.org/abs/2605.07167
AB - The Integrated Multi-Satellite Retrievals for the Global Precipitation Measurement (IMERG) product combines precipitation estimates from passive microwave (PMW) and infrared (IR) sensors to provide quasi-global precipitation estimates at half-hourly resolution. Because IR observations are primarily sensitive to cloud-top properties, however, IR-based precipitation estimates are generally less accurate than PMW estimates and can introduce artifacts into the merged precipitation fields. We introduce GPROF-IR, a neural-network-based precipitation retrieval designed to provide precipitation estimates from single-channel geostationary IR observations for the upcoming IMERG V08. GPROF-IR uses a convolutional neural network to exploit spatiotemporal information from sequences of half-hourly IR observations. We validate GPROF-IR against independent surface-based precipitation measurements over land and ocean. GPROF-IR substantially improves upon existing single-channel IR retrievals, including the IR retrieval used in IMERG V07 and more recent neural-network-based approaches. During overpasses of flagship PMW sensors, GPROF-IR is more accurate than the corresponding IMERG PMW estimates over CONUS and Austria, but not Korea. Over CONUS, GPROF-IR outperforms the merged IMERG V07 product for hourly and daily precipitation accumulations. Against shipborne disdrometer observations, GPROF-IR is more accurate than IMERG V07 in the tropics but slightly less accurate in the extratropics. Since geostationary IR observations are continuously available across much of the globe since 1983, GPROF-IR provides a promising basis for improving merged precipitation products and developing long-term, temporally consistent precipitation records.
ER -