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Cloud parameters (cloud mask, effective particle radius, and liquid/ice waterpath) are the important inputs in estimating surface solar radiation (SSR).These parameters can be derived from MODIS with high accuracy, but theirtemporal resolution is too low to obtain high-temporal-resolution SSRretrievals. In order to obtain hourly cloud parameters, an artificial neuralnetwork (ANN) is applied in this study to directly construct a functionalrelationship between MODIS cloud products and Multifunctional TransportSatellite (MTSAT) geostationary satellite signals. In addition, an efficientparameterization model for SSR retrieval is introduced and, when driven withMODIS atmospheric and land products, its root mean square error (RMSE) isabout 100 W m−2 for 44 Baseline Surface Radiation Network (BSRN)stations. Once the estimated cloud parameters and other information (such asaerosol, precipitable water, ozone) are input to the model, we canderive SSR at high spatiotemporal resolution. The retrieved SSR is firstevaluated against hourly radiation data at three experimental stations in theHaihe River basin of China. The mean bias error (MBE) and RMSE in hourly SSRestimate are 12.0 W m−2 (or 3.5 %) and 98.5 W m−2 (or28.9 %), respectively. The retrieved SSR is also evaluated against dailyradiation data at 90 China Meteorological Administration (CMA) stations. TheMBEs are 9.8 W m−2 (or 5.4 %); the RMSEs in daily and monthly meanSSR estimates are 34.2 W m−2 (or 19.1 %) and 22.1 W m−2 (or12.3 %), respectively. The accuracy is comparable to or even higher thantwo other radiation products (GLASS and ISCCP-FD), and the present method ismore computationally efficient and can produce hourly SSR data at a spatialresolution of 5 km

Retrieving high-resolution surface solar radiation with cloud parameters derived by combining MODIS and MTSAT data