2025 Volume 21 Pages 132-142
This study introduces a novel framework, Standardized Anomaly Quantile Regression Forests (SA-QRF), which integrates nonlinear predictor selection via quantile regression forests (QRF) into the standardized anomaly model output statistics (SAMOS) method. Unlike the traditional Boosting-based approach (SA-Boosting), QRF effectively captures nonlinear interactions between predictors and forecast targets while quantifying predictor importance. This strategy avoids overfitting and highlights key variables influencing forecast accuracy. Using ECMWF fine-grid and ensemble forecast data (2019-2020) for Beijing-Tianjin-Hebei Province in China, SA-QRF is evaluated against SA-Boosting for forecasts of 2 m temperature, 2 m relative humidity, and 10 m wind speed. Results demonstrate that SA-QRF achieves skill levels comparable to SA-Boosting in continuous ranked probability skill scores. The spatial continuous ranked probability score comparison with SA-Boosting shows that SA-QRF outperforms by covering 7% more stations in the spatial forecasts of relative humidity and 9% more stations in the spatial forecasts of wind speed. In addition, these two methods effectively mitigate underdispersion in probabilistic forecasts, as evidenced by visual examination of the probability integral transform plots, and enhance deterministic forecast performance by 15%, 31%, and 34%, respectively. These findings validate the QRF can complement and optimize SAMOS, leveraging its nonlinear strengths to achieve better performance.
