Deriving High-Resolution Estimation of TOA Net Shortwave Radiation Over Global Land Using Data From Multiple-Geostationary Satellites

Estimation of net shortwave radiation at the top of the atmosphere (TOA) ( $R_{\text {ns,TOA}} $ ) at high spatial and temporal resolutions is essential for studying the Earth’s energy budget and its associated radiative forcing of natural or anthropogenic events on global or regional scales. Existing products typically use broadband sensors with coarse spatial resolution for the estimation. While narrowband sensors offer higher spatial resolution, they have a limited number of daily observations. Traditional estimation methods often necessitate atmospheric products as inputs, while inaccurate cloud and aerosol information can result in substantial estimation errors. Furthermore, geostationary satellite-based products are often developed for specific regions, with limited spatial coverage and varying accuracy due to the diverse range of satellites and algorithms used. To overcome these challenges and obtain global $R_{\text {ns,TOA}}$ with improved spatiotemporal resolution and accuracy, a universal approach was proposed in this study to derive hourly 3-km global $R_{\text {ns,TOA}}$ , which takes the advantages from the radiative transfer model, machine learning algorithm, and dense observations from five geostationary satellites. Our $R_{\text {ns,TOA}}$ estimation shows reasonably good agreement with the clouds and the earth’s radiant energy system (CERES) product, with root-mean-square errors (RMSEs) ranging from 53.43 to 75.67 W/m2 and bias ranging from -12.78 to -2.01 W/m2 on instantaneous scales, and the RMSE on daily scale improved by up to 6.7 W/m2 compared to those of the sinusoidal-integrated values. Our generated 3-km daily $R_{\text {ns,TOA}}$ exhibits a high consistency of spatial pattern with 1° CERES product at multiple temporal conditions while providing much more spatial details. Furthermore, we find that the diurnal patterns at 3-km resolution differ significantly from the sinusoidal patterns at 1°, exhibiting greater variability. The difference in daytime $R_{\text {ns,TOA}}$ estimation between the two reaches up to 86 W/m2. This significant difference reflects the unreliability of relying solely on sinusoidal pattern and the necessity of high-frequency observations to estimate daily values at high spatial resolution. However, increasing the observation frequency beyond a certain point (120 min) yields only limited improvements in the accuracy of daily radiation estimates. Overall, the algorithms proposed in this study are reliable and can be easily applied to any satellite equipped with Meteosat Second Generation (MSG)-like or more bands. This study demonstrates the feasibility of jointly using multiple geostationary satellites for $R_{\text {ns,TOA}}$ estimation with high spatial and temporal resolutions.