Hybrid numerical models for wind speed forecasting. (1st September 2021) - Brabec, Marek
Abstract: Wind speed is involved in multiple scales physical phenomena and depends on specific features, that are not always easy to simulate numerically. Alternative solution that combines the physical advantages provided by numerical weather prediction (NWP) simulations and statistical models is investigated for wind speed forecast. Several aspects that influence the wind speed forecast error at synoptic stations in Romania were identified, such as discrepancy between model and true topography, urbanicity or distance to the Black Sea. Calibration models in the framework of Generalized Additive Models (GAM) are developed for the proposed endeavour. A set of models applied to limited area model ALARO were introduced and evaluated. Results showed improved statistical scores compared to raw ALARO output and simple regression model: a decrease of up to 23% for the RMSE score, or 94% for the bias was observed for the model which performed best in terms of annual bias and RMSE. Different impact of terms involved in the calibration model is found. Most important effects in the model are associated with wind speed observations from the 24 past hours and simulated wind speed effect in relation to altitude. Highlights: Numeric wind speed prediction can be improved through statistical calibration models. Complex semi-parametric regression models can account for local topographic features influencing the wind speed forecast. Generalized Additive Models provide an effective tool for gaining insight into the sources of wind speed forecast error.