Operational Machine Learning Post-Processing of Short-Range Temperature, Humidity, Wind Speed and Gust Forecasts

Statistical methods can be used to create bias correction models that learn from past forecast errors and reduce systematic errors in real-time forecasts. This study presents a machine learning (ML) approach using extreme gradient-boosted (XGBoost) trees to address biases in a numerical weather prediction (NWP) nowcast model for key meteorological parameters: 2-m temperature, 2-m relative humidity, 10-m wind speed, and 10-m wind gust. These ML models have been integrated into the Finnish Meteorological Institute's (FMI) operational nowcasting framework, Smartmet nowcast. Results show that, even with a relatively modest set of meteorological predictors, the ML bias correction method significantly improves forecast accuracy, reducing the root mean square error (RMSE) by 24%–29% compared to the direct NWP model output. The implementation of this new bias correction method not only improves the quality of FMI's short-range forecasts, but also extends the availability of bias-corrected data for longer forecast lead times, offering substantial improvements over the previously implemented bias correction method. The codebase for this machine learning bias correction is available at (https://github.com/fmidev/snwc_bc).