Improved estimates of incident radiation and heat load using non‐ parametric regression against topographic variables

Question: Can non‐parametric multiplicative regression (NPMR) improve estimates of potential direct incident radiation (PDIR) and heat load based on topographic variables, as compared to least‐squares multiple regression against trigonometric transforms of the predictors? Methods: We used a multiplicative kernel smoothing technique to interpolate between tabulated values of PDIR, using a locally linear model and a Gaussian kernel, with slope, aspect, and latitude as predictors. Heat load was calculated as a 45 degree rotation of the PDIR response surface. Results: This method yielded a fit to a complex response surface with R² > 0.99 and eliminated the areas of poor fit given by a previously published method based on least squares multiple regression with trigonometric functions of the predictors. Conclusions: Improved estimates of PDIR and heat load based on topographic variables can be obtained by using non‐parametric multiplicative regression (NPMR). The main drawback to the method is that it requires reference to the data tables, since those data are part of the model.