Cloud cover estimation using bispectral satellite measurements

Abstract

An algorithm has been developed for cloud cover estimation using bispectral satellite measurements. Based on the distribution of pixels in albedo-brightness temperature space, a number of threshold values are applied to identify those pixels which are most likely totally cloud filled. Mean cloudy-column albedo in a region much larger than a single pixel is then estimated and cloud cover computed. The algorithm has been applied to the International Satellite Cloud Climatology Project Geostationary Meteorological Satellite B2 data. Locations of tropical convective cells and the passage of fronts in higher latitudes are identified. Since these features represent the states of large-scale atmospheric circulations, it is concluded that the algorithm can yield consistent cloud data sets useful for climate studies.