Evaluating the Influence of Surface and Precipitation Characteristics on TMI and GMI Precipitation Retrievals.

Monday, 15 December 2014
Nicholas Carr, University of Oklahoma Norman Campus, Norman, OK, United States, Pierre Kirstetter, University of Oklahoma Norman Campus, CIMMS, Norman, OK, United States, Yang Hong, University of Oklahoma, Norman, OK, United States, Jonathan J Gourley, National Severe Storms Lab, Oklahoma City, OK, United States, Ralph R Ferraro, Univ Maryland-ESSIC/CICS and NOAA/NESDIS, College Park, MD, United States, Christian D Kummerow, Colorado State Univ, Fort Collins, CO, United States, Walter Arthur Petersen, NASA GSFC/WFF Code 610.W, Wallops Island, VA, United States, Mathew Schwaller, NASA GSFC, Greenbelt, MD, United States and Nai-Yu Wang, University of Maryland, College Park, MD, United States
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.