Estimating Surface Downward Longwave Radiation Using Machine Learning Methods

Abstract

The downward longwave radiation (Ld, 4–100 μm) is a major component of research for the surface radiation energy budget and balance. In this study, we applied five machine learning methods, namely artificial neural network (ANN), support vector regression (SVR), gradient boosting regression tree (GBRT), random forest (RF), and multivariate adaptive regression spline (MARS), to estimate Ld using ground measurements collected from 27 Baseline Surface Radiation Network (BSRN) stations. Ld measurements in situ were used to validate the accuracy of Ld estimation models on daily and monthly time scales. A comparison of the results demonstrated that the estimates on the basis of the GBRT method had the highest accuracy, with an overall root-mean-square error (RMSE) of 17.50 W m−2 and an R value of 0.96 for the test dataset on a daily time scale. These values were 11.19 W m−2 and 0.98, respectively, on a monthly time scale. The effects of land cover and elevation were further studied to comprehensively evaluate the performance of each machine learning method. All machine learning methods achieved better results over the grass land cover type but relatively worse results over the tundra. GBRT, RF, and MARS methods were found to show good performance at both the high- and low-altitude sites.

Keywords

• downward longwave radiation
• machine learning
• GBRT
• energy budget
• random forest