A fully global satellite-based precipitation estimate that can transition across the changing Earth surface and complex land/water conditions is an important capability formany hydrological applications, and for independent evaluation of the precipitation derived from weather and climate models. This capability is inherently challenging owing to the complexity of the surface geophysical properties upon which the satellite-based instruments view. To date, these satellite observations originate primarily from a variety of wide-swath passive microwave (MW) imagers and sounders. In contrast to open ocean and large water bodies, the surface emissivity contribution to passive MW measurements is much more variable for land surfaces, with varying sensitivities to near-surface precipitation. TheNASA-JAXAGlobal PrecipitationMeasurement (GPM) spacecraft (2014-present) is equipped with a dual-frequency precipitation radar and a multichannel passiveMWimaging radiometer specifically designed for precipitation measurement, covering substantially more land area than its predecessor Tropical Rainfall Measuring Mission (TRMM). The synergy between GPM’s instruments has guided a number of new frameworks for passive MW precipitation retrieval algorithms, whereby the information carried by the single narrow-swath precipitation radar is exploited to recover precipitation from a disparate constellation of passiveMWimagers and sounders. With over 6 years of increased land surface coverage provided by GPM, new insight has been gained into the nature of the microwave surface emissivity over land and ice/snow-covered surfaces, leading to improvements in a number of physically and semiphysically based precipitation retrieval techniques that adapt to variableEarth surface conditions. In thismanuscript, theworkings and capabilities of several of these approaches are highlighted. SIGNIFICANCE STATEMENT: High-resolution satellite-based precipitation data products are currently produced by combining data products from many individual satellites as they orbit Earth. However, the signals recorded by the sensors on board these satellites are not directly related to the precipitation falling near Earth’s surface, but rather to a mixture of the precipitation and the underlying Earth surface conditions. The challenge for the algorithms is to be able to effectively separate and extract the desired portion of the signal representing the precipitation, from the undesired portion that is attributed to Earth’s surface. A review of a number of methods for carrying out this procedure are described and demonstrated, which capitalize on many years of satellite observations collected over many different Earth surface conditions.
Bibliographical noteFunding Information:
Acknowledgments. The authors acknowledge the support from NASA under the Precipitation Measurements Mission (PMM) science team. NU acknowledges support from JSPS KAKENHI Grant Number JP19K15096 and KUAS Interdisciplinary Research Activity Support. YYL would like to thank the support from NASA Grant 80NSSC20K0903 from the Weather and Atmospheric Dynamics program. GP, PS and DC would like to thank the support from the RainCast study (ESA Contract 4000125959/18/NL/NA) and from the EUMETSAT Satellite Application Facility for Operational Hydrology and Water management (H SAF) Third Continuous and Operations Phase (CDOP-3). AC is supported by the Ph.D. program in Infrastructures, Transport Systems and Geomatics at the Department of Civil, Constructional, and Environmental Engineering at Sapienza University of Rome. The PMM Research Program is acknowledged for supporting H SAF and GPM scientific collaboration through the approval of the no-cost proposal ‘‘H SAF and GPM: Precipitation algorithm development and validation activity.’’ These efforts were developed under the auspices of the PMM Land Surface Working Group (LSWG), one of several working groups tasked with investigating specific aspects of the PMM precipitation algorithms. The efforts from the NASA Precipitation Processing System (PPS) for the production and distribution of the GPM constellation datasets is gratefully acknowledged. The work by FJT was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA.
© 2021 American Meteorological Society.
- Algorithms;microwave observations
- Land surface
- Radars/radar observations
- Satellite observations
- Winter/cool season