Adapting Passive Microwave-Based Precipitation Algorithms to Variable Microwave Land Surface Emissivity to Improve Precipitation Estimation from the GPM Constellation

Future projections of global precipitation show substantial variability in location, duration, intensity and occurrence. A fully global satellite-based precipitation estimate that can transition across changing Earth surface conditions and complex land/water boundaries is an important capability for proper evaluation of the precipitation produced or diagnosed in weather and climate models. This capability inherently challenging owing to the complexity of the surface geophysical properties upon which the satellite-based instruments view. To date, these satellite observations originate from wide-swath passive microwave imagers and sounders. In contrast to open ocean and large water bodies, the surface emissivity contribution to passive microwave measurements is much more variable for land surfaces, with corresponding different sensitivities to near-surface precipitation.The NASA/JAXA Global Precipitation Measurement (GPM) spacecraft (2014-current) is equipped with a dual-frequency precipitation radar and a multichannel passive MW imaging sensor specifically dedicated to precipitation measurement, covering substantially more land surface than its predecessor (1997-2014) Tropical Rainfall Measuring Mission (TRMM). The synergy between GPM’s instruments has guided a number of new frameworks for passive microwave precipitation retrieval algorithms, whereby the information carried by the single narrow-swath precipitation radar is exploited to recover precipitation from a disparate constellation of passive MW imagers and sounders. With over six 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 physical and semi-physical based precipitation retrieval techniques that adapt to variable Earth surface conditions. In this manuscript, the workings and capabilities of several of these approaches are highlighted.

全球降水的未来预估在发生位置、持续时长、强度与出现频次上均存在显著差异。可适配地球表面条件变化与复杂水陆边界的全域星基降水估算方案，是精准评估天气与气候模式中生成或诊断的降水的核心支撑能力。但该方案因卫星仪器观测所依托的地表地球物理属性的复杂性，本就具备固有挑战性。截至目前，此类卫星观测数据主要来自宽幅被动微波成像仪与被动微波探空仪（passive microwave sounders）。与开阔洋面及大型水体不同，陆地表面的地表发射率对被动微波测量的贡献波动幅度显著更大，且对近地面降水的敏感性也存在相应差异。美国国家航空航天局（National Aeronautics and Space Administration, NASA）与日本宇宙航空研究开发机构（Japan Aerospace Exploration Agency, JAXA）联合研制的全球降水测量（Global Precipitation Measurement, GPM）卫星（2014年至今），搭载了双频降水雷达与多通道被动微波成像传感器，专为降水测量任务设计，其覆盖的陆地表面范围远超其前代任务——1997年至2014年运行的热带降雨测量任务（Tropical Rainfall Measuring Mission, TRMM）。GPM搭载的各类仪器之间的协同机制，催生了多款全新的被动微波降水反演算法框架：即利用单条窄幅扫描带降水雷达所携带的观测信息，从分散的被动微波成像仪与探空仪星座中反演获取降水数据。依托GPM六年多来新增的陆地表面观测覆盖数据，学界对陆地及冰雪覆盖地表的微波地表发射率特性取得了新的认知，进而推动了多款适配可变地球表面条件的物理基与半物理基降水反演技术的优化升级。本文将重点介绍其中若干种反演方法的工作原理与性能表现。