SATELLITE CLOUD CLASSIFICATION AND RAIN-RATE ESTIMATION USING MULTISPECTRAL RADIANCES AND MEASURES OF SPATIAL TEXTURE

Twelve months of Southern Hemisphere (maritime) midlatitudes Advanced Very High Resolution Radiometer local area coverage data at full radio metric and spatial resolution have been collocated with rain-rate data from three Doppler weather radars. Using an interactive computing env ironment, large independent samples of cloudy-altocumulus, cumulonimbu s, cirrostratus, cumulus, nimbostratus, stratocumulus, stratus-and clo ud-free scenes have been identified (labeled) in the collocated data. Accurate labeling was ensured by providing a supervising-analyst acces s to appropriate diagnostics, including difference and ratio channels, 3.7-mu m reflected and emissive components, spectral histograms, Coak ley-Bretherton spatial coherence plots, mean, standard deviation, and gray-level difference (GLD) statistics. This analysis yielded 4323 clo ud and no-cloud samples at a spatial resolution of 8 x 8 instantaneous fields of view (IFOV), from 257 NOAA-11 and NOAA-12 orbits. Bayesian cloud discriminant functions calculated from the labeled samples and u tilizing feature vectors including radiometric and GLD spatial charact eristics successfully classified scenes into one of the seven cloud an d no-cloud classes with significant skill (Kuipers' performance index 0.63). Utilizing the posterior probability of the classified samples e nabled some clouds that were classified erroneously to be identified ( and discarded), improving the skill of the discriminant functions by a n additional 10% or so. Removing the GLD statistics from the feature v ector reduced the skill of the cloud discrimination by about 20% (rela tive to the nondiscarding discriminant function), while increasing the misclassification of midlevel clouds. However, some cloud classes can only be discriminated from their multispectral signatures. Day and ni ght discriminant functions show similar skill. Within raining cloud cl asses, rain rate has been related to the spatial and radiometric chara cteristics of the cloud. The skill of the rain-rate estimates is depen dent on the cloud type. For nimbostratus and altocumulus classes 20%-2 5% of the rain-rate variation can be explained by predictors that meas ure the temperature, spatial texture, and degree of isotropy in the sa mpled clouds. Raining and nonraining Samples of altocumulus, cumulus, cirrostratus, and nimbostratus can be delineated with at least 60% acc uracy. This approach, whereby cloud classes are identified then rain r ates estimated as a function of cloud type, would seem to resolve some of the usual problems associated with rain-rate analyses from midlati tudes infrared and visible satellite data. It also extends rain-rate d iagnosis to nonconvective (frontal) cloud systems.