A Hybrid Extended Kalman Filter Based on a Parametrized FeedForward Neural Network for the Improvement of the Results of Numerical Wave Prediction Models

Abstract

Aiming to develop a novel optimization model for numerical weather and wave predictions, this study proposes a hybrid approach based on the combination of Artificial Neural Networks (ANNs) and Kalman Filters (KFs). The KF technique uses fixed covariance matrices, which is inappropriate for dynamic conditions where the measurement error varies due to outside factors. This work proposes an alternative method for updating the covariance matrices, depending on a selected parameter that was obtained from a parametrized FeedForward Neural Network. The FeedForward Neural Network is trained for various values of the parameter for a set of historical data to provide the best choice based on a predetermined objective. Following this procedure, the parameter is added to the hybrid Extended Kalman Filter, which improves the direct outputs of numerical forecasts by decoding the systematic error between the model’s direct output and the corresponding observations. The suggested approach was used in a number of time periods in various locations with very promising results.

Keywords

• optimization of numerical forecasts
• extended Kalman filter
• feedforward neural networks
• WAM-cycle 4