Statistical approaches to error identification for plane‐parallel retrievals of optical and microphysical properties of three‐dimensional clouds: Bayesian inference

This paper addresses the effects of three‐dimensional (3‐D) radiative transfer on the retrieval of optical depth for inhomogeneous stratiform liquid water clouds from passive satellite imagery. A nonparametric Bayesian classifier is developed to identify locations in a scene where plane‐parallel retrievals fail to meet the requirements of a criterion that dictates a specified level of accuracy. Receiver operating characteristics are introduced that provide useful metrics that assess the quality of the error identification procedure as functions of illumination‐viewing geometry. By fixing droplet effective radii, distributions of errors for retrieved optical depth are estimated at a scale of 120 m. These estimates suggest the best performance that can be expected for optical depth retrievals when 3‐D radiative transfer cannot be ignored. The developments in this paper were made possible through the use of Monte Carlo radiative transfer simulations on stratiform clouds that were generated by a cloud system‐resolving model. Plane‐parallel retrievals employ the CloudSat optical depth retrieval algorithm.