Multi-Sensor Precipitation Estimation from Space: Data Sources, Methods and Validation

Satellite remote sensing complements rain gauges and ground radars as the primary sources of precipitation data. While significant advancements have been made in spaceborne precipitation estimation since the 1960s, the emergence of multi-sensor precipitation estimation (MPE) in the early 1990s revolutionized global precipitation data generation by integrating infrared and microwave observations. Among others, Global Precipitation Measurement (GPM) plays a crucial role in providing invaluable data sources for MPE by utilizing passive microwave sensors and geostationary infrared sensors. MPE represents the current state-of-the-art approach for generating high-quality, high-resolution global satellite precipitation products (SPPs), employing various methods such as cloud motion analysis, probability matching, adjustment ratios, regression techniques, neural networks, and weighted averaging. International collaborations, such as the International Precipitation Working Group and the Precipitation Virtual Constellation, have significantly contributed to enhancing our understanding of the uncertainties associated with MPEs and their corresponding SPPs. It has been observed that SPPs exhibit higher reliability over tropical oceans compared to mid- and high-latitudes, particularly during cold seasons or in regions with complex terrains. To further advance MPE research, future efforts should focus on improving accuracy for extremely low- and high-precipitation events, solid precipitation measurements, as well as orographic precipitation estimation.