Integrating Machine Learning with Adaptive Kalman Filtering to Downscale GFS Air Temperature Forecasts in Mountainous Areas

Accurate, high-resolution gridded air temperature forecasts are essential, particularly in mountainous areas with complex terrain. This study proposes a two-stage processing framework DOWN + BC to downscale Global Forecast System (GFS) temperature forecasts and correct their bias. The approach first employs a random forest (RF) model to geographically downscale 3-hourly 0.25° GFS forecasts to a 30 m resolution (DOWN), followed by bias correction (BC) using a first-order adaptive Kalman filter (AKF). The accuracy of the DOWN + BC-processed forecasts was evaluated against both automatic weather station (AWS) observations and high-resolution air temperature fields derived from an extreme gradient boosting model (XGB-derived). The results indicate that (1) the DOWN step effectively refines the spatial detail of temperature distribution, though it yields limited improvement in accuracy compared to the raw GFS forecasts; (2) the combined DOWN + BC method substantially enhances forecast accuracy. At AWS locations, the root mean square error (RMSE) of GFS forecasts decreased by 37.84% in January 2020 and 41.16% in July 2023. Relative to the XGB-derived temperature distribution, RMSE was reduced by 47.27% and 33.79% for the respective periods.