Revisiting satellite radiative flux computations at the top of the atmosphere

Most satellite observations of radiative fluxes at the top of the atmosphere (TOA) are at narrow spectral intervals and at particular viewing angles. Critical elements in the formulation of TOA shortwave (SW) radiative fluxes are (1) the transformation from narrowband to broadband values (n/b) and (2) the application of angular distribution models (ADMs) to correct for anisotropy. In this article, the n/b transformations are based on theoretical simulations with a radiative transfer model Moderate Resolution Atmospheric Transmission (MODTRAN) 3.7 using land classification types based on the International Geosphere-Biosphere Programme (IGBP) scheme and a range of realistic atmospheric conditions. The newly developed ADMs are a combination of MODTRAN-3.7 simulations and the Clouds and the Earth's Radiant Energy System (CERES)-observed ADMs. To evaluate the impact of the proposed corrections, they are implemented with observations from the Spinning Enhanced Visible Infrared Imager (SEVIRI) on the Meteorological Satellite (METEOSAT) 8 to derive TOA fluxes and compared to similar quantities from CERES. It is shown that the estimated TOA radiative fluxes have –3% bias and 7% root mean square error (RMSE) when compared with CERES observations at a monthly timescale.

[1]  Lucien Wald,et al.  Simulating Meteosat-7 broadband radiances using two visible channels of Meteosat-8 , 2006 .

[2]  A. Chedin,et al.  The Improved Initialization Inversion Method: A High Resolution Physical Method for Temperature Retrievals from Satellites of the TIROS-N Series. , 1985 .

[3]  A. Berk MODTRAN : A moderate resolution model for LOWTRAN7 , 1989 .

[4]  K. Stamnes,et al.  Numerically stable algorithm for discrete-ordinate-method radiative transfer in multiple scattering and emitting layered media. , 1988, Applied optics.

[5]  Paul W. Stackhouse,et al.  Comparison of Different Global Information Sources Used in Surface Radiative Flux Calculation: Radiative Properties of the Surface , 2007 .

[6]  J. Schmetz,et al.  AN INTRODUCTION TO METEOSAT SECOND GENERATION (MSG) , 2002 .

[7]  Patrick Minnis,et al.  Comparison of regional clear-sky albedos inferred from satellite-observations and model computations , 1986 .

[8]  S. Dewitte,et al.  Angular distribution models anisotropic correction factors and sun glint: a sensitivity study , 2006 .

[9]  S. Dewitte,et al.  The Geostationary Earth Radiation Budget Edition 1 data processing algorithms , 2008 .

[10]  Judith C. Chow,et al.  Spatial and seasonal distributions of carbonaceous aerosols over China , 2007 .

[11]  G. Gutman,et al.  The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models , 1998 .

[12]  Catherine Gautier,et al.  SBDART: A Research and Teaching Software Tool for Plane-Parallel Radiative Transfer in the Earth's Atmosphere. , 1998 .

[13]  A. Henderson‐sellers,et al.  The influence of the spectral response of satellite sensors on estimates of broadband albedo , 1984 .

[14]  A. Lacis,et al.  Calculation of radiative fluxes from the surface to top of atmosphere based on ISCCP and other global data sets: Refinements of the radiative transfer model and the input data , 2004 .

[15]  A. Ipe,et al.  Unfiltering of the Geostationary Earth Radiation Budget (GERB) Data. Part I: Shortwave Radiation , 2008 .

[16]  Luis Gonzalez,et al.  Angular distribution models, anisotropic correction factors, and mixed clear-scene types: a sensitivity study , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Bernhard Vogel,et al.  Relationship of visibility, aerosol optical thickness and aerosol size distribution in an ageing air mass over South-West Germany , 2008 .

[18]  Bruce A. Wielicki,et al.  Top-of-Atmosphere Radiative Fluxes: Validation of ERBE Scanner Inversion Algorithm Using Nimbus-7 ERB Data , 1992 .

[19]  D. F. Young,et al.  Angular Distribution Models for Top-of-Atmosphere Radiative Flux Estimation from the Clouds and the Earth's Radiant Energy System Instrument on the Tropical Rainfall Measuring Mission Satellite. Part II; Validation , 2003 .

[20]  D. Oglesby,et al.  Earthquake nucleation on dip‐slip faults , 2004 .

[21]  David R. Doelling,et al.  Angular Distribution Models for Top-of-Atmosphere Radiative Flux Estimation from the Clouds and the Earth’s Radiant Energy System Instrument on the Terra Satellite. Part II: Validation , 2005 .

[22]  G. Potter,et al.  Narrow- and broad-band satellite measurements of shortwave radiation - Conversion simulations with a general circulation model , 1986 .

[23]  J. Townshend,et al.  Global land cover classi(cid:142) cation at 1 km spatial resolution using a classi(cid:142) cation tree approach , 2004 .

[24]  Bruce A. Wielicki,et al.  Angular Distribution Models for Top-of-Atmosphere Radiative Flux Estimation from the Clouds and the Earth's Radiant Energy System Instrument on the Tropical Rainfall Measuring Mission Satellite. Part II; Validation , 2003 .

[25]  Yu Qin,et al.  Vertical profile and origin of wintertime tropospheric ozone over China during the PEACE-A period , 2004 .

[26]  Shuichi Rokugawa,et al.  A temperature and emissivity separation algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images , 1998, IEEE Trans. Geosci. Remote. Sens..

[27]  R. Pinker,et al.  Simulations of the GOES visible sensor to changing surface and atmospheric conditions , 1987 .

[28]  Rachel T. Pinker,et al.  Effect of surface properites on the narrow to broadband spectral relationship in clear sky satellite observations , 1986 .