Estimation of downwelling surface longwave radiation for cloudy skies by considering the radiation effect from the entire cloud layers

Cloud base height (CBH) is a key parameter to characterize the cloud radiation effect. However, the CBH used in downwelling surface longwave radiation (DSLR) estimation is generally obtained indirectly through cloud top parameters retrieved by passive optical remote sensing instruments, which is of high uncertainty. At the same time, it is unreasonable to replace the effective radiation of the entire cloud layers by only using the cloud base radiation with single layer cloud model. This study proposes a new method to estimate cloudy-sky DSLR, which considers the radiation effect of the entire cloud layers from the cloud base to top. First, the CBH estimation model is established by the genetic algorithm-artificial neural network (GA-ANN) algorithm. The cloud top height and cloud attribute parameters (cloud optical depth, cloud water path, and cloud phase) from the passive remote sensing are used as the input features, and meanwhile CBH data from the active remote sensing are output features in the training and testing process of the model. Then, the cloud base temperature (CBT) is estimated based on the CBH combined with the temperature profile data in the EAR5 reanalysis data. Finally, the effective temperature of the entire cloud layers is calculated by using CBT and cloud top temperature. The verification results of CBH estimation showed that R2 is 0.83, the bias and root mean square error (RMSE) are 0.02 km and 1.56 km, respectively, which indicates a comparable accuracy and higher stability compared with the previous studies. The ground-based measurements in the SURFRAD network are used to validate the newly proposed DSLR estimation method, and the results showed that the bias and RMSE are 5.27 W/m2 and 28.48 W/ m2, respectively. Additionally, this study found that although the effective temperature of the entire cloud layers has a weaker linear correlation with DSLR, the radiation contribution generated by cloud still occupies a certain weight, and the maximum ratio of cloud radiation in DSLR estimation can account for 30%. Therefore, the cloud radiation effect must be taken into account in the estimation of cloudy-sky DSLR.