VALIDATION AND INTERCOMPARISON OF SSM I RAIN-RATE RETRIEVAL METHODS OVER THE CONTINENTAL UNITED-STATES/

An important source of error or ambiguity in the satellite passive mic rowave detection and estimation of precipitation rate over land is var iable background emission, reflecting differences in surface temperatu re and moisture, soil type, and vegetation cover. Three experimental a lgorithms for the Special Sensor Microwave/Imager (SSM/I) are describe d that attempt to improve the precipitation signal-to-noise ratio by s electively responding to transient brightness temperature perturbation s relative to maps of seven-channel monthly mean radiances. These algo rithms are validated and intercompared along with two quasi-standard S SM/I algorithms developed by Grody and Ferraro and by Adler and Huffma n.For ground truth, nine months of 10-cm radar data taken at six sites and of hourly rain gauge reports from approximately 2700 locations in the United States were used. The radar data were carefully quality co ntrolled and calibrated against coincident gauge reports. The required calibration adjustment of the radar rain rates was found to be as lar ge as a factor of 2-5. The satellite estimates from snow-free pixels a re validated both against the calibrated radar data within the zones o f radar coverage and directly against the gauges for the entire lower 48 states. An adaptation of the Heidke skill score is introduced as a statistical calibration and validation tool that may avoid some of the pitfalls of standard validation statistics. Under the relatively homo geneous background conditions characteristic of the study region, the five algorithms are found to yield similar results, and no algorithm e merges as clearly superior. The hypothesis that maps of monthly mean s urface emission can aid in the detection of light precipitation requir es further testing over more varied land surfaces.