Evaluating the Influence of Surface and Precipitation Characteristics on TMI and GMI Precipitation Retrievals.
Nicholas Carr, University of Oklahoma Norman Campus, Norman, OK, United States, Pierre Kirstetter, University of Oklahoma Norman Campus, Norman, United States, Yang Hong, University of Oklahoma, School of Civil Engineering and Environmental Science, Norman, United States, Jonathan J Gourley, NOAA/OAR National Severe Storms Laboratory, Oklahoma City, United States, Ralph R Ferraro, NOAA/NESDIS, College Park, United States, Christian Kummerow, Colorado State University, Department of Atmospheric Science, Fort Collins, United States, Walter Arthur Petersen, NASA MSFC, Science Research and Projects Office, Huntsville, AL, United States, Mathew Schwaller, NASA GSFC, Greenbelt, MD, United States and Nai-Yu Wang, NOAA NESDIS, Space Weather Observations (SWO), Lanham, United States
Abstract:
To evaluate the influence of surface and precipitation characteristics on Passive microwave (PMW) precipitation retrievals, precipitation products obtained from both the TRMM Microwave Imager (TMI) and the GPM Microwave Imager (GMI) were evaluated relative to independent high-resolution reference precipitation products obtained using the NOAA/NSSL ground radar-based Multi-Radar Multi-Sensor (MRMS) system. Specifically the ability of each sensor to detect, classify, and quantify instantaneous surface precipitation at its native pixel resolution is examined and linked to surface and precipitation characteristics. Surface characteristics were derived optically using NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). Precipitation mesoscale characteristics such as convective-stratiform classification and spatial structure were obtained from the high-resolution reference data.
The quality of both PMW sensors’ retrievals varied considerably with surface characteristics; both sensors displayed decreased detection and quantification statistics over sparsely vegetated and dry surfaces. Similarly, the quality of the precipitation retrievals was affected by precipitation characteristics and high relative errors were evident in isolated and small-scale precipitation events as well as in mixed stratiform-convective events. The error characteristics of the two sensors also differed in several significant aspects, namely TMI tended to overestimate precipitation relative to the reference, while GMI underestimated precipitation. The influence of the precipitation and surface characteristics was less evident in the more sophisticated GMI retrievals. An additional outcome of the study was the adaptation of the comparison framework between space and ground precipitation estimates to accommodate the new probabilistic features of the GPM-era PMW precipitation retrievals.
