Development of a Global Cloud Model for Simulating Earth-Viewing Space Missions

Abstract

Existing global cloud models are improved and expanded so that they might be more effectively used in planning and evaluating potential earth-viewing space missions. A relationship is developed between the ground and satellite-derived cloud-amount frequency distributions. This relationship is used to improve internal consistency between the ground-observed unconditional statistics (independent observations) and the satellite-derived conditional statistics (temporally and spatially dependent observations) as tabulated in previous models. A statistical technique is developed which permits the scaling of the conditional statistics to time and distance separations other than those for which the data base is tabulated. A new technique to increase or decrease the representative area size of the statistical data base is developed. Both the scaling and area adjustment techniques are shown to represent significant improvements over the techniques previously employed. The problem of adjusting the simulation results to account for the varying spatial resolutions of different satellite-borne sensors is discussed. The current status of the global cloud model Is surveyed and recommendations are made for the continuing improvement and upgrading of the model.