Exploring the potential of regional cloud vertical structure climatology statistical model in estimating surface downwelling longwave radiation

Cloud base height (CBH) is one of the most uncertain parameters in surface downward longwave radiation (SDLR) estimation. Climatology statistical models of cloud vertical structure (CVS), which provide 1-degree grid averages or latitude zone averages of CBH and cloud thickness (CT), have been frequently applied to improve coarse-resolution SDLR estimation. This study aims to develop a regional CVS climatology statistical model containing CT and CBH statistics at a kilometer scale, using CloudSat, CALIPSO, and MODIS data, and to explore its potential in kilometer-scale CBH and SDLR estimations. The RMSE of CBH estimated from the new CVS model ranges from 0.4 to 2.6 km for different cloud types when validated using CloudSat/CALIPSO data. CBH RMSEs are 2.20 km for Terra data and 1.99 km for Aqua data when validated against ground measurements. The simple Minnis CT model greatly overestimated CBH, while the new CVS model produced much better results. Using CBH from the new CVS model, the RMSEs of estimated cloudy SDLR are 26.8 W/m² and 29.2 W/m² for the Gupta-SDLR and Diak-SDLR models, respectively. These results are significantly better than those from the Minnis CT model and are comparable to those from the more advanced Yang-Cheng CT model. Moreover, the RMSEs of all-sky SDLR range from 22.6 to 21.5 W/m² with resolution from 1 km to 20 km. These findings indicate that the regional CVS model is feasible for high-resolution CBH and SDLR estimation and can be effectively combined with other CBH estimation methods. This study provides a novel approach for estimating SDLR by integrating active and passive satellite data.