Surface Daytime Net Radiation Estimation Using Artificial Neural Networks

Net all-wave surface radiation (R-n) is one of the most important fundamental parameters in various applications. However, conventional R-n measurements are difficult to collect because of the high cost and ongoing maintenance of recording instruments. Therefore, various empirical R-n estimation models have been developed. This study presents the results of two artificial neural network (ANN) models (general regression neural networks (GRNN) and Neuroet) to estimate R-n globally from multi-source data, including remotely sensed products, surface measurements, and meteorological reanalysis products. R-n estimates provided by the two ANNs were tested against in-situ radiation measurements obtained from 251 global sites between 1991-2010 both in global mode (all data were used to fit the models) and in conditional mode (the data were divided into four subsets and the models were fitted separately). Based on the results obtained from extensive experiments, it has been proved that the two ANNs were superior to linear-based empirical models in both global and conditional modes and that the GRNN performed better and was more stable than Neuroet. The GRNN estimates had a determination coefficient (R-2) of 0.92, a root mean square error (RMSE) of 34.27 W.m(-2), and a bias of -0.61 W.m(-2) in global mode based on the validation dataset. This study concluded that ANN methods are a potentially powerful tool for global R-n estimation.