Emmanouil N. Anagnostou
Department of Civil and Environmental Engineering and Iowa Institute of Hydraulic Research, University of Iowa, Iowa City, Iowa
Mesoscale Atmospheric Processes Branch, Goddard Space Flight Center, National Aeronautics and Space Administration, Greenbelt, Maryland
A better understanding of global climate calls for more accurate estimates of liquid and ice water content profiles of precipitating clouds and their associated latent heating profiles. Convective and stratiform precipitation regimes have different latent heating and therefore impact the earth’s climate differently. Classification of clouds over oceans has traditionally been part of more general rainfall retrieval schemes. These schemes are based on individual or combined visible and infrared, and microwave satellite observations. However, none of these schemes report validations of their cloud classification with independent ground observations. The objective of this study is to develop a scheme to classify convective and stratiform precipitating clouds using satellite brightness temperature observations. The proposed scheme probabilistically relates a quantity called variability index (VI) to the stratiform fractional precipitation coverage over the satellite field of view (FOV). The VI for a satellite pixel is the mean absolute 85-GHz brightness temperature difference between the pixel and the eight surrounding neighbor pixels. The classification scheme has been applied to four different rainfall regimes. All four regimes show that the frequency of stratiform rainfall in the satellite FOV increases as the satellite-based VI decreases. The results of this study demonstrate that the satellite-based VI is consistently related to the probability of occurrence of three classes (0%–40%, 40%–70%, and 70%–100%) of FOV stratiform coverage.