A Hybrid Extended Kalman Filter Based on Parametrized ANNs for the Improvement of the Forecasts of Numerical Weather and Wave Prediction Models

Abstract

A hybrid optimization filter for weather and wave numerical models is proposed and tested in this study. Parametrized Artificial Neural Networks are utilized in conjunction with Extended Kalman Filters to provide a novel postprocess strategy for 10 m wind speed, 2 m air temperature, and significant wave height simulations. The innovation of the developed model is the implementation of Feedforward Neural Networks and Radial Basis Function Neural Networks as estimators of an exogenous parameter that adjusts the covariance matrices of the Extended Kalman Filter process. This hybrid system is evaluated through a time window process leading to promising results, thus enabling a decrease in systematic errors alongside the restriction of the error variability and the corresponding forecast uncertainty. The obtained results showed that the average reduction of the systematic error exceeded 75%, while the corresponding nonsystematic part of that error decreased by 35%.

Keywords

• optimization of numerical forecasts
• numerical weather and wave prediction
• Extended Kalman Filters
• feedforward neural networks
• radial basis function neural networks