Real-time Data Assimilation for Chaotic Storm Surge Model Using NARX Neural Network
نویسنده
چکیده
Siek, M. and Solomatine, D.P., 2011. Real-time data assimilation for chaotic storm surge model using NARX neural network. Journal of Coastal Research, SI 64 (Proceedings of the 11th International Coastal Symposium), 1189 – 1194. Szczecin, Poland, ISSN 0749-0208 This paper introduces a real-time data assimilation technique where Nonlinear AutoRegressive with eXogenous inputs (NARX) neural network is used to re-analyze and improve chaotic model forecasts. The chaotic model is built using adaptive local models based on the dynamical neighbors in the reconstructed phase space of the observables. The NARX data assimilation can perform in nearly real-time process since it does not require a lot of computation in comparison to variational or sequential data assimilation methods. This proposed method was implemented and tested for assimilating chaotic storm surge models for the North Sea. The results show that the chaotic storm surge model with data assimilation using NARX neural network has typically more accurate forecasts than the chaotic storm surge model without data assimilation, and than other European operational numerical storm surge models. ADDITIONAL INDEX WORDS: Storm surge dynamics, Phase space reconstruction, Adaptive local model, Real-time data assimilation, NARX neural network, European operational storm surge models
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