TRMM 2A12 Land Precipitation Product - Status and Future Plans

Nai-Yu Wang

Earth System Science Interdisciplinary Center (ESSIC), University of Maryland, College Park, Maryland, USA

Chuntao Liu

Department of Meteorology, University of Utah, Salt Lake City, Utah, USA

Ralph Ferraro

NOAA/NESDIS, College Park, Maryland, USA

Dave Wolff

NASA/GSFC, Greenbelt, Maryland, USA

Ed Zipser

Department of Meteorology, University of Utah, Salt Lake City, Utah, USA

Christian Kummerow

Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado, USA

ABSTRACT

The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) 2A12 product consists of unique components configured for land and oceanic precipitation retrievals. This design was based on the vastly different physical characteristics of the retrieval, involving primarily emission over ocean and entirely scattering over land. This paper describes the current status of the TRMM Version 6 (V6) 2A12 product over land and envisioned improvements for TRMM TMI V7 and GPM GMI V1. On a global scale, the 2A12 land algorithm exhibits biases when compared with the TRMM 2A25 (Precipitation Radar (PR) based) and rain gauges. These range from 6 percent for GPCC to 20 percent for 2A25. Closer comparison also revelas regional and seasonal biases, with the largest positive biases found in warm-season convective zones and over semi-arid regions. Some negative biases are found in warm-rain precipitation regimes where scattering at 85 GHz is unable to detect a precipitation signal. On an instantaneous time scale, 2A12 land also produces a positive bias when compaed with high-quality radar data from Melbourne, Florida, a TRMM ground valication site. The largest discrepancies occur for rain rates ofless than 2 mm/h. A number of known "anomalies" are highlighted, including overestimation of rainfall in deep convective systems, underestimation in warm-rain regimes, and a number of features associated with the screening of the algorithm (e.g., snow cover, deserts, etc.). Future improvements for TRMM TMI V7 are described and include the use of ancillary data to determine the underlying surface characteristics and the development of improved birghtness-temperature to rain-rate relationships with a more robust data set of TMI and PR matchups, stratified by atmospheric parameters (i.e., surface temperataure, atmospheric moisture, etc.) obtained from Numerical Weather Prediction (NWP) model fields. Finally, the promise of an improved land algorithm through the use of high-frequency microwave measurements is described. This will form the basis for the Global Precipitation Measurement (GPM) Global Microwave Imager (GMI) V1 algorithm.