GPROF-IR: An Improved Single-Channel Infrared Precipitation Retrieval for Merged Satellite Precipitation Products
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.