H208-05
Characterizing the Variable Land Surface Emissivity for Passive Microwave-Based Precipitation Estimation during the Global Precipitation Measurement (GPM) Era
Wednesday, 16 December 2020: 11:46
Virtual
Joe J Turk1, Sarah Ringerud2,3, Yalei You4, Nobuyuki Utsumi5, Hyungjun Kim6 and Christa Peters-Lidard2, (1)NASA Jet Propulsion Laboratory, Pasadena, CA, United States, (2)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (3)University of Maryland College Park, College Park, MD, United States, (4)University of Maryland College Park, Earth System Science Interdisciplinary Center, College PARK, MD, United States, (5)Kyoto Universty of Advanced Science, Kyoto, Japan, (6)University of Tokyo, Tokyo, Japan
Abstract:
The current Global Precipitation Measurement (GPM) and its predecessor Tropical Rainfall Measuring Mission (TRMM) are equipped with both a radar and a passive microwave imaging sensor specifically dedicated to precipitation measurement. The synergy between these active and passive instruments has guided a new framework for passive microwave precipitation retrieval algorithms, whereby the information carried by the single precipitation radar is exploited to recover precipitation-profile information from a constellation of multiple passive microwave imagers and sounders. While the current GPM constellation offers almost complete daily global coverage, its observations further highlight shortcomings in passive microwave-based precipitation estimation over complex surfaces such as snow- and ice-covered regions, mixed-surface pixels, and surface conditions that are rapidly transitioning from one surface state to another.
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 non-oceanic Earth surfaces. The various constellation satellites observe a wide variety of precipitation, each associated with different environmental characteristics, surface temperature and the microwave surface emissivity associated with the Earth surface conditions. These background conditions are highly variable throughout the seasons, weather patterns, and exhibit a wide range of space/time variability. Different approaches for passive microwave precipitation estimation and improvements in physical and semi-physical based precipitation retrieval techniques are highlighted, specifically over non-oceanic backgrounds.
