Clouds and the Earth's Radiant Energy System (CERES) Algorithm Theoretical Basis Document

One of the major advances of the CERES (Clouds and the Earth's Radiant Energy System) radiation budget analysis over the ERBE (Earth Radiation Budget Experiment) is the ability to use high spectral and spatial resolution cloud imager data to determine cloud and surface properties within the relatively large CERES field of view 120-km diameter for the Earth Observing System (EOS)-AM and EOS-PM, 10 km diameter for TRMM (Tropical Rainfall Measuring Mission)]. For the first launch of the CERES broadband radiometer on TRMM in 1997, CERES will use the VIRS (Visible Infrared Scanner) cloud imager as input. For the next launches on EOS-AM (1998) and EOS-PM (2000), CERES will use the MODIS (Moderate-Resolution Imaging Spectroradiometer) cloud imager data as input. This overview summarizes the Subsystem 4 CERES algorithms which 1. Determine clear-sky radiances and detect pixels containing clouds 2. Determine well-defined cloud layers and identify multilayer pixels 3. Determine cloud properties for each imager pixel 4. Map the imager cloud properties to the CERES broadband radiance footprint 5. Use the CERES footprint cloud properties to determine an angular distribution model for the conversion of radiance to top-of-atmosphere (TOa ) flux 6. Use the TOA fluxes and parameterizations to estimate surface radiative fluxes Angular sampling errors were determined to be the largest error source for ERBE shortwave fluxes. The increased accuracy of CERES cloud property determination and the new angular models are expected to reduce these errors by a factor of 3 to 4. The cloud properties and radiative fluxes for each CERES footprint are also key to providing more accurate estimates of in-atmosphere radiative fluxes. These in-atmosphere radiative flux calculations are discussed in Subsystem 5. 4.0. Overview of Cloud Retrieval and Radiative Flux Inversion 4.0.

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