Neural Networks as Routine for Error Updating of Numerical Models

Abstract

This paper describes a somewhat alternative approach to combining observations and numerical model results in order to produce a more accurate forecast routine. The approach utilizes artificial neural networks to analyze and forecast the errors created by numerical models. The resulting hybrid model provides very good forecast skills that can be extended over a forecasting horizon of considerable length. The method has been developed for the purpose of operational forecasting of current speeds in the Danish ∅resund Strait. The forecast system was used as a planning tool during the construction of the 16 km-long fixed link across the ∅resund Strait, linking the countries of Denmark and Sweden.