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Comparison of CERES surface radiation fluxes with surface observations over...

Yan, H., J. Huang, P. Minnis, T. Wang, and J. Bi (2011), Comparison of CERES surface radiation fluxes with surface observations over Loess Plateau, Remote Sensing of Environment, 115, 1489-1500, doi:10.1016/j.rse.2011.02.008.

Surface energy budget is an important factor in weather and climate processes. To estimate the errors in satellite-retrieved surface radiation budget over the interior of China, instantaneous-footprint surface radiation fluxes from the Terra/Aqua FLASHFlux SSF product are compared with the measurements taken at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) from July 2008 to March 2010. Validation is performed separately for different conditions: clear-sky and cloudy-sky, daytime and nighttime for four seasons. Differences between the FLASHFlux CERES shortwave radiation flux and surface measurements have larger standard deviations in cloudy-sky conditions than in clear-sky conditions, indicating that cloud contamination increases uncertainty in the retrieval algorithm. Upward shortwave radiation flux (USW) is overestimated in cloudy conditions suggesting that the cloud parameters and surface scene type in the retrieval process are not optimal for northwestern China. The CERES downward longwave radiation fluxes (DLW) accurately follow the variation of surface measurements during daytime, but are slightly underestimated during nighttime due to the coarse sounding profile and undetected low clouds at nighttime. The CERES upwelling longwave radiation fluxes (ULW) are strongly underestimated during daytime but are slightly underestimated during nighttime regardless of cloud coverage. This large bias could be caused by an underestimate of surface skin temperature and/or surface emissivity, or spatial inhomogeneity around the site. Generally, except for diurnal ULW, other components of the surface radiative fluxes obtained from CERES SSF datasets are close to meeting the accuracy requirements for climate research.

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Modeling Analysis and Prediction Program (MAP)
Radiation Science Program (RSP)