Application of the Getis statistic to hemispheric and regional scale passive microwave derived snow water equivalent imagery

Satellite passive microwave data have been utilized to map snow cover because of all-weather imaging capabilities, wide swath width, and rapid scene revisit time. To exploit this growing time series of data, innovative processing techniques are needed to identify the evolution of spatial patterns in passive microwave derived snow water equivalent (SWE) imagery, and to improve understanding of passive microwave response to snow covered surfaces during the winter season. In this study, five day averaged Special Sensor Microwave/Imager (SSM/I) derived SWE imagery are analyzed with the Getis statistic (G_i^* ), a local indicator of spatial autocorrelation. Northern Hemisphere and North American Prairie imagery were investigated in order to evaluate Getis statistic performance, and to identify SWE clustering patterns at different spatial scales. Hemispheric scale Getis statistic analysis produces results which allow identification of maximum seasonal snow cover extent, and the degree of seasonal snow cover variability. The dependence of SWE algorithm performance on surface cover was also investigated through the application of the Getis statistic to SSM/I brightness temperatures. The Getis analysis for the Prairie subscene can be interpreted from climatological and hydrological perspectives because of the operational accuracy of the SWE retrieval algorithm for this region