Dynamic Emulations of Surface Radiation Components During Day and Night Under all Sky and Surface Conditions Using Temporal Neural Networks

Modeling of land surface radiation budget and its components is essential to a better understanding of soil-vegetation-atmosphere interactions. Time Delay Neural Network (TDNN) and Time Lag Recurrent Network (TLRN) models were used to emulate all the hourly surface radiation components for a temperate peatland during day and night under all-sky and -surface conditions. Sensitivity analyses of full versus reduced models, daytime versus nighttime periods, and TDNN versus TLRN models were carried out using training-, cross-validation, and testing-derived metrics of root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R-2) for each of the components. The full daytime temporal neural network models performed best based on RMSE of 2.3 W m(-2) in downwelling longwave radiation to 112.2 W m(-2) in upwelling shortwave radiation; R-2 of 0.52 in downwelling longwave radiation to 0.88 in net shortwave radiation and net radiation; and MAE of 1.73 W m(-2) in downwelling longwave radiation to 90.57 W m(-2) in upwelling shortwave radiation. The best nighttime TDNN models led to RMSE values that ranged from 4.4 W m(-2) in downwelling longwave radiation to 9.3 W m(-2) in upwelling longwave radiation; R-2 values that ranged from 0.38 in net longwave radiation to 0.60 in downwelling longwave radiation; and MAE values that ranged from 4.1 W m(-2) in downwelling longwave radiation to 8.1 W m(-2) in upwelling longwave radiation. Temporal neural networks used in this study appear to be a promising approach to predict nonlinear behaviors of the daytime and nighttime surface radiation components.