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