An efficient physically based parameterization to derive surface solar irradiance based on satellite atmospheric products

Abstract

Surface solar irradiance (SSI) is required in a wide range of scientific researches and practical applications. Many parameterization schemes are developed to estimate it using routinely measured meteorological variables, since SSI is directly measured at a very limited number of stations. Even so, meteorological stations are still sparse, especially in remote areas. Remote sensing can be used to map spatiotemporally continuous SSI. Considering the huge amount of satellite data, coarse-resolution SSI has been estimated for reducing the computational burden when the estimation is based on a complex radiative transfer model. On the other hand, many empirical relationships are used to enhance the retrieval efficiency, but the accuracy cannot be guaranteed out of regions where they are locally calibrated. In this study, an efficient physically based parameterization is proposed to balance computational efficiency and retrieval accuracy for SSI estimation. In this parameterization, the transmittances for gases, aerosols, and clouds are all handled in full band form and the multiple reflections between the atmosphere and surface are explicitly taken into account. The newly proposed parameterization is applied to estimate SSI with both Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric and land products as inputs. These retrievals are validated against in situ measurements at the Surface Radiation Budget Network and at the North China Plain on an instantaneous basis, and moreover, they are validated and compared with Global Energy and Water Exchanges–Surface Radiation Budget and International Satellite Cloud Climatology Project–flux data SSI estimates at radiation stations of China Meteorological Administration on a daily mean basis. The estimation results indicates that the newly proposed SSI estimation scheme can effectively retrieve SSI based on MODIS products with mean root-mean-square errors of about 100 Wm-1 and 35 Wm-1 on an instantaneous and daily mean basis, respectively.