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. Sensitivity studies show that the results are not greatly sensitive to the threshold values and the size of pixels. The algorithm has been applied to the International Satellite Cloud Climatology Project (ISCCP) Geostationary Meteorological Satellite B2 data. A number of consistency checks are performed by comparing the cloud cover with 1) the distribution of pixels in the albedo?temperature space, 2) the daily variation of local meteorological conditions, and 3) the evolution of large-scale circulation patterns. 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, we conclude that the algorithm can yield consistent cloud data sets useful for climate studies.