CMORPH: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution

Abstract

A new technique is presented in which half-hourly global precipitation estimates derived from passive microwave satellite scans are propagated by motion vectors derived from geostationary satellite infrared data. The Climate Prediction Center morphing method (CMORPH) uses motion vectors derived from half-hourly interval geostationary satellite IR imagery to propagate the relatively high quality precipitation estimates derived from passive microwave data. In addition, the shape and intensity of the precipitation features are modified (morphed) during the time between microwave sensor scans by performing a time-weighted linear interpolation. This process yields spatially and temporally complete microwave-derived precipitation analyses, independent of the infrared temperature field. CMORPH showed substantial improvements over both simple averaging of the microwave estimates and over techniques that blend microwave and infrared information but that derive estimates of precipitation from infrared data when passive microwave information is unavailable. In particular, CMORPH outperforms these blended techniques in terms of daily spatial correlation with a validating rain gauge analysis over Australia by an average of 0.14, 0.27, 0.26, 0.22, and 0.20 for April, May, June?August, September, and October 2003, respectively. CMORPH also yields higher equitable threat scores over Australia for the same periods by an average of 0.11, 0.14, 0.13, 0.14, and 0.13. Over the United States for June?August, September, and October 2003, spatial correlation was higher for CMORPH relative to the average of the same techniques by an average of 0.10, 0.13, and 0.13, respectively, and equitable threat scores were higher by an average of 0.06, 0.09, and 0.10, respectively.