Adapting passive microwave-based precipitation algorithms to variable microwave land surface emissivity to improve precipitation estimation from the GPM constellation

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.