17 Surface Rain Rates from Tropical Rainfall Measuring Mission Satellite Algorithms

The Tropical Rainfall Measuring Mission (TRMM), jointly sponsored by the National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA, previously known as National Space Development Agency, or NASDA), is the first coordinated international satellite mission to monitor and study tropical and subtropical rain systems (Simpson et al., 1988). A detail description of the TRMM sensor package and a preliminary assessment of the sensor performance are given by Kummerow et al. (1998). The TRMM rain sensor package includes the first space-borne Precipitation Radar (TPR), a TRMM Microwave Imager (TMI) and a Visible and Infrared Scanner (VIRS). Rainfall estimates provided by the TRMM have found applications in climate analysis, data assimilation, water resource management, and decision support to agriculture and health issues. The TRMM data have gone through major reprocessing cycles, as improved knowledge of the sensors and algorithms leads to improved sensor calibration and algorithms. The version 5 (V5) algorithms have shown significant improvement over the previous version of data (Kummerow et al., 2000). Over land, Shin et al. (2001) showed that TMI rainfall estimates are higher than TPR in the TRMM domain (35°N 35°S) for the first two years of TRMM data. Adler et al. (2003a) pointed out that there is a distinct reversal of the algorithm bias between the tropics and the subtropics for the first three years of TRMM data. While global differences among the TRMM satellite estimates are of the order of 20%, there are large regional differences (Kummerow et al., 2000; Adler et al., 2003a). Nesbitt et al. (2004) compared V5 TMI and TPR with the Global Precipitation Climatology Center gauge analyses and TMI and TPR rain features using one year of data. Comparisons of TRMM satellite and ground validation radars have been carried out As the TRMM data are being used in research and applications, it is necessary to constantly evaluate the performance of the TRMM algorithm and quantify their relative biases for hypothesis testing, model validations, and operational decision-making, such as water management and crop yield monitoring (Teng et al., 2005).