A hybrid Bayesian Kalman filter and applications to numerical wind speed modeling

A new hybrid optimization technique for numerical environmental simulation models is proposed and tested in this work. Bayesian modeling is utilized in conjunction with a nonlinear Kalman filter towards a novel post process algorithm applied to numerical wind speed simulations. The new model is tested on idealized data as well as on numerical model forecasts leading to promising results and supporting both the reduction of systematic biases but also the significant limitation of the error variability and the associated forecast uncertainty, a point where classical Kalman filters usually fail to contribute.