SVR and Bayesian Neural Networks applied to statistical downscaling of precipitation
Our downscaling models used different classification algorithms like CART and SVM to obtain the rainy days, and then run the SVR on those days to obtain the precipitation amounts. Here we show results from 16 points across North America where we evaluated the mean absolute error skill score (MAE SS) of the different SD models and their corresponding CPU times. The results show non-homogeneous skill scores between the 16 points and higher skills near the Rocky Mountains. The results also show considerable differences in the Peirce skill score from the different classification algorithms, and additionally they show the impact a math library can have on the overall running times.