Characterizing the retrieval of cloud properties from optical remote sensing

Abstract

This paper presents a new approach to the formal characterization of the optical retrieval of cloud optical thickness and effective droplet radius based on a nonlinear methodology that is derived from a general stochastic inverse problem formulation similar to standard Bayesian estimation theory. The methodology includes efficient use of the precomputed radiative transfer model simulations which are already available in standard retrieval algorithms. Another important property of the methodology is that it does not require performing the retrieval with actual measurements in order to characterize the retrieval results. One utility of this analysis is the quantification of information content in the standard retrieval problem, and the increase of information through adding channels (radiances at different wavelengths) to the inversion. This was demonstrated for the five‐wavelength retrieval using airborne hyperspectral shortwave irradiance measurements. The ability of the method to evaluate the impact of observation and radiative transfer model uncertainties on the retrieved cloud properties is also demonstrated. Further benefits from this study will be in its application to the cloud retrieval algorithms to be developed for future space‐ and airborne instruments. The present study puts forth the framework necessary to quantify that increase in information and to optimize new retrieval algorithms that efficiently accommodate the enhanced measurement space.