Predicting ocean surface currents using numerical weather prediction model and Kohonen neural network: a northern Adriatic study
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The paper documents a concept of ocean forecasting system for ocean surface currents based on self-organizing map (SOM) trained by high-resolution numerical weather prediction (NWP) model and high-frequency (HF) radar data. Wind and surface currents data from the northern Adriatic coastal area were used in a 6-month long training phase to obtain SOM patterns. Very high correlation between current and joined current and wind SOM patterns indicated the strong relationship between winds and currents and allowed for creation of a prediction system. Increasing SOM dimensions did not increase reliability of the forecasting system, being limited by the amount of the data used for training and achieving the lowest errors for 4 × 4 SOM matrix. As the HF radars and high-resolution NWP models are strongly expanding in coastal oceans, providing reliable and long-term datasets, the applicability of the proposed SOM-based forecasting system is expected to be high.
KeywordsSelf-organizing map Neural network Hybrid model Sea surface currents prediction
The work on this paper was supported by the UKF project NEURAL (www.izor.hr/neural) and the IPA CBC project HAZADR (www.hazadr.eu). The SOM Toolbox version 2.0 for MATLAB was developed by E. Alhoniemi, J. Himberg, J. Parhankangas, and J. Vesanto at the Helsinki University of Technology, Finland, and is available at http://www.cis.hut.fi/projects/somtoolbox. Comments raised by anonymous reviewers are appreciated as greatly improving quality of the manuscript.
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