A study on radiative transfer effects in 3-D cloudy atmosphere using satellite data

Abstract

This study evaluates 3-D cloud effects on the radiation budget with a combined use of active sensor cloud profiling radar/CloudSat and imager Moderate Resolution Imaging Spectroradiometer/Aqua data on the A-train. An algorithm is devised for constructing 3-D cloud fields based on satellite-observed cloud information. The 3-D cloud fields thus constructed are used to calculate the broadband solar and thermal radiative fluxes with a 3-D radiative transfer code developed by the authors. The aim of this study is to investigate the effects of cloud morphology on solar radiative transfer in cloudy atmosphere. For this purpose, 3-D cloud fields are constructed with the new satellite-based method, to which full 3D-RT (radiative transfer) simulations are applied. The simulated 3-D radiation fields are then used to examine and quantify errors of existing typical plane-parallel approximations, i.e., Plane-Parallel Approximation, Independent Pixel Approximation and Tilted Independent Pixel Approximation. Such 3D-RT simulations also serve to address another objective of this study, i.e., to devise an accurate approximation and to characterize the observed specific 3D-RT effects by the cloud morphology based on knowledge of idealized 3D-RT effects. We introduce a modified approach based on an optimum value of diffusivity factor to better approximate the radiative fluxes for arbitrary solar zenith angle determined from the results of 3-D radiative transfer simulations to redeem the overcorrections of these approximations for large solar zenith angles (SZAs). This new approach, called Slant path Independent Pixel Approximation, is found to be better than other approximations when SZA is large for some cloud cases. Based on the SZA dependence of the errors of these approximations relative to 3-D computations, satellite-observed real cloud cases are found to fall into either of three types of different morphologies, i.e., isolated cloud type, upper cloud-roughened type and lower cloud-roughened type. Such a classification offers a novel insight into error characteristics of the approximations that are interpreted in the context of specific cloud morphology.