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Global Performance of a Fast Parameterization Scheme for Estimating Surface Solar Radiation From MODIS Data
A fast parameterization scheme named SUNFLUX is first used in this paper to estimate instantaneous surface solar radiation (SSR) based on products from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard both Terra and Aqua platforms. The scheme mainly takes into account the absorption and scattering processes due to clouds, aerosols, and gas in the atmosphere. The estimated instantaneous SSR is evaluated against surface observations obtained from seven stations of the surface radiation budget network (SURFRAD), four stations in the North China Plain (NCP) and 40 stations of the baseline surface radiation network (BSRN). The statistical results for evaluation against these three data sets show that the relative root-mean-square error (RMSE) values of SUNFLUX are less than 15%, 16%, and 17%, respectively. Daily SSR is derived through temporal upscaling from the MODIS-based instantaneous SSR estimates, and is validated against surface observations. The relative RMSE values for daily SSR estimates are about 16% at the seven SURFRAD stations, four NCP stations, 40 BSRN stations, and 90 China Meteorological Administration (CMA) radiation stations. The accuracy of the scheme is generally higher than those of previous algorithms, and thus can be potentially applied on geostationary satellites for mapping high-resolution SSR data in the future.
Global Performance of a Fast Parameterization Scheme for Estimating Surface Solar Radiation From MODIS Data
A fast parameterization scheme named SUNFLUX is first used in this paper to estimate instantaneous surface solar radiation (SSR) based on products from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard both Terra and Aqua platforms. The scheme mainly takes into account the absorption and scattering processes due to clouds, aerosols, and gas in the atmosphere. The estimated instantaneous SSR is evaluated against surface observations obtained from seven stations of the surface radiation budget network (SURFRAD), four stations in the North China Plain (NCP) and 40 stations of the baseline surface radiation network (BSRN). The statistical results for evaluation against these three data sets show that the relative root-mean-square error (RMSE) values of SUNFLUX are less than 15%, 16%, and 17%, respectively. Daily SSR is derived through temporal upscaling from the MODIS-based instantaneous SSR estimates, and is validated against surface observations. The relative RMSE values for daily SSR estimates are about 16% at the seven SURFRAD stations, four NCP stations, 40 BSRN stations, and 90 China Meteorological Administration (CMA) radiation stations. The accuracy of the scheme is generally higher than those of previous algorithms, and thus can be potentially applied on geostationary satellites for mapping high-resolution SSR data in the future.
Global Performance of a Fast Parameterization Scheme for Estimating Surface Solar Radiation From MODIS Data
Tang, Wenjun (author) / Yang, Kun / Sun, Zhian / Qin, Jun / Niu, Xiaolei
2017
Article (Journal)
English
Local classification TIB:
770/3710/5670
BKL:
38.03
Methoden und Techniken der Geowissenschaften
/
74.41
Luftaufnahmen, Photogrammetrie
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