Multichannel Satellite Retrieval of Cloud Parameter Probability Distribution Functions

Abstract

A retrieval method has been developed to directly retrieve statistics of cloud parameters from Geostationary Operational Environmental Satellite (GOES) visible and infrared imager data. This method retrieves cloud parameter probability density functions (PDFs) directly from distributions of multichannel satellite-observed radiances. For example, the joint probability distribution of ice water content and effective radius can be retrieved from a four-dimensional histogram of GOES radiances. A forward radiative transfer model creates a mapping from cloud parameter space to satellite radiance space. The cloud parameter space is described by vertically inhomogeneous liquid and ice cloud layers with variable liquid water path, effective radius, height, and thickness. A Monte Carlo procedure uses the mapping to transform probability density from the observed satellite radiance histogram, or radiance PDF, to a two-dimensional cloud property PDF. An estimate of the uncertainty in the retrieved PDF is calculated from random realizations of the radiance to cloud PDF transformation given the uncertainty of the observed radiances and the nonunique mapping to cloud parameter space. The algorithm is tested with simulations from numerical cloud model output and applied to imagery from the tropical eastern Pacific.