Performance Evaluation of Level-2 TRMM Rain Profile Algorithms by Intercomparison and Hypothesis Testing

Abstract

Currently, satellite algorithms are the methodology showing most promise for obtaining more accurate global precipitation estimates. However, a general problem with satellite methods is that they do not measure precipitation directly, but through inversion of radiation—rain relationships. Because of this, procedures are needed to verify algorithm-generated results. The most common method of verifying satellite rain estimates is by direct comparison with ground truth data derived from measurements obtained by rain gauge networks, ground-based weather radar, or a combination of the two. However, these types of comparisons generally shed no light on the physical causes of the differences. Moreover, ground validation measurements often have uncertainty magnitudes on the order of or greater than the satellite algorithms, motivating the search for alternate approaches. The purpose of this research is to explore a new type of approach for evaluating and validating the level-2 Tropical Rainfall Measuring Mission (TRMM) facility rain profile algorithms. This is done by an algorithm-to-algorithm intercomparison analysis in the context of physical hypothesis testing.