Comparison of different techniques for estimation of incoming longwave radiation

Abstract

Global warming and climate change have left developing countries fragile in terms of agricultural production, and this vulnerability is expected to increase in the near future. The surface energy budget approach is a different perspective to the investigation of energy change over a landscape. In terms of budget items, the net radiation absorbed by the earth is equal to the difference between the sum of the incoming shortwave and longwave radiation and the sum of the reflected shortwave and emitted longwave radiation. The longwave radiation has important effects on dew deposition and drying on crop leaves in agricultural meteorology. A pyranometer provides routine measurement of the daytime radiation, but the longwave part of this radiation cannot be so readily measured at night time. In this study, multiple linear regression, artificial neural networks, deep learning, adaptive network-based fuzzy inference systems (ANFIS) and empirical models have been applied to model and estimate the mean incoming longwave radiation using atmospheric parameters. The ANFIS model appears to show good agreement between the measured and the estimated values for all days considered than other models.