A simple method for removing 3-D radiative effects in satellite retrievals of surface irradiance

Abstract As the spatial resolution of satellite sensors increase, estimates of surface solar irradiance ( I SFC ) from space borne observations of top of the atmosphere reflected radiance ( R TOA ) can actually become less accurate because of enhanced three-dimensional (3-D) radiative effects that are not generally considered in most retrieval algorithms. An elementary approach to improving retrievals is to incorporate some form of spatial averaging through the use of superpixels. Determining the best approach for averaging and the optimum size of the superpixel is the objective of this study. It is achieved by examining the bias, correlation coefficient, and root-mean-square error (RMSE) between the true and estimated I SFC for a set of simulated cloud scenes and their radiative fields. Because of nonlinear effects, averaging within the superpixel should only be performed after I SFC is retrieved from R TOA independently for each pixel . For the combined set of stratocumulus and convective cloud fields that have a cloud fraction ranging from 0.25 to 1.0 used in this study, the optimal superpixel size is about 25 km. It is found that the bias is a function of solar and viewing angles and a correction is provided that improves the bias while leaving the correlation coefficient and root-mean-square error unchanged.

[1]  Tim P. Barnett,et al.  On the Space–Time Scales of the Surface Solar Radiation Field , 1998 .

[2]  Catherine Gautier.  Surface Solar Irradiance in the Central Pacific during Tropic Heat: Comparisons between in Situ Measurements and Satellite Estimates , 1988 .

[3]  Robert Frouin,et al.  A review of satellite methods to derive surface shortwave irradiance , 1995 .

[4]  Charles N. Long,et al.  Surface measurements of solar irradiance: A study of the spatial correlation between simultaneous measurements at separated sites , 1995 .

[5]  James K. B. Bishop,et al.  Spatial and temporal variability of global surface solar irradiance , 1991 .

[6]  Johannes Schmetz,et al.  Towards a surface radiation climatology: retrieval of downward irradiances from satellites , 1989 .

[7]  R. Pinker,et al.  Modeling Surface Solar Irradiance for Satellite Applications on a Global Scale , 1992 .

[8]  C. F. Ratto,et al.  Solar irradiance estimation from geostationary satellite data: I. Statistical models☆ , 1993 .

[9]  Richard Perez,et al.  COMPARING SATELLITE REMOTE SENSING AND GROUND NETWORK MEASUREMENTS FOR THE PRODUCTION OF SITE/TIME SPECIFIC IRRADIANCE DATA , 1997 .

[10]  G. Grell,et al.  A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5) , 1994 .

[11]  R. Perez,et al.  Effective Accuracy of Satellite-Derived Hourly Irradiances , 1999 .

[12]  V. Pugachev.  STATISTICAL MODELS, II , 1984 .

[13]  Jan Asle Olseth,et al.  Solar irradiance, sunshine duration and daylight illuminance derived from METEOSAT data for some European sites , 2001 .

[14]  C. Gautier,et al.  A Simple Physical Model to Estimate Incident Solar Radiation at the Surface from GOES Satellite Data , 1980 .

[15]  C. Gautier,et al.  A Three-Dimensional Radiative Transfer Model to Investigate the Solar Radiation within a Cloudy Atmosphere. Part II: Spectral Effects , 1998 .

[16]  Catherine Gautier,et al.  Surface Solar Radiation Flux and Cloud Radiative Forcing for the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP): A Satellite, Surface Observations, and Radiative Transfer Model Study , 1997 .

[17]  C. Gautier,et al.  Remote sensing of surface solar irradiance with corrections for 3-D cloud effects , 2002 .