Validation of a Cloud-Base Temperature-Based Single-Layer Cloud Model for Estimating Surface Longwave Downward Radiation

As one of the four components of surface radiation balance, surface longwave downward radiation (SLDR) greatly affects the accurate characterization of hydrological, ecological, and biochemical processes. Since cloud-base temperature (CBT)-based single-layer cloud models (SLCMs) have advantages in both their strong physical mechanisms and abilities to produce high spatial resolution SLDR products, this study validated a CBT-based SLCM developed at the global scale using in situ observations collected by the baseline surface radiation network (BSRN) in conjunction with Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products and Modern-Era Retrospective Analysis For Research And Applications, Version 2 (MERRA-2) reanalysis data. Overall, the CBT-based SLCM achieved a relatively high SLDR estimation accuracy for the Terra and Aqua satellites, with biases better than -1.2 W/m(2) and root-mean-squared error (RMSE) values better than 29.9 W/m(2). However, its random RMSE was slightly worse than those of two Clouds and the Earth's Radiant Energy System (CERES) Single Scanner Footprint (SSF) SLDR products due to the larger spatial variability that exists at the Earth's surface when it is quantified at a high spatial resolution (1 km). Additionally, the CBT-based SLCM outperformed two existing cloud-top temperature (CTT)-based SLCMs. In the future, we will continue to improve the performance of the CBT-based SLCM and will update the Global LAnd Surface Satellite (GLASS) SLDR product under cloudy sky conditions.

Automated summary

This study validated a CBT-based SLCM at the global scale and achieved relatively high SLDR estimation accuracy for Terra and Aqua satellites, but with slightly worse random RMSE compared to CERES products.