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A new ocean surface currents forecasting system using Self-Organizing Maps (SOM) and HF radar shows improved skill over physics-based models, particularly in strong winds. Further improvements are expected with higher quality training data.

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Area of Science:

  • Oceanography
  • Artificial Intelligence
  • Coastal Monitoring

Background:

  • Accurate forecasting of ocean surface currents is crucial for various marine applications.
  • Existing methods often rely on complex physics-based numerical models.
  • The integration of machine learning offers a novel approach to ocean current prediction.

Purpose of the Study:

  • To develop and evaluate an ocean surface currents forecasting system utilizing Self-Organizing Maps (SOM).
  • To compare the performance of the SOM-based system against a traditional ROMS (Regional Ocean Modeling System) model.
  • To assess the system's forecasting skill using high-frequency (HF) ocean radar and numerical weather prediction (NWP) data.

Main Methods:

  • Development of a SOM neural network algorithm for forecasting surface currents.
  • Integration of high-frequency (HF) ocean radar measurements and numerical weather prediction (NWP) products.
  • Comparative analysis of the SOM-based system against ROMS-derived currents using independent datasets.

Main Results:

  • The SOM-based forecasting system demonstrated slightly superior forecasting skill compared to the ROMS model.
  • Enhanced performance was observed, particularly under strong wind conditions.
  • The study highlights the potential for improved accuracy with higher quality and longer duration training datasets.

Conclusions:

  • Unsupervised learning techniques, such as SOM, offer a viable and effective alternative for ocean surface current forecasting.
  • The SOM-based system shows promise for operational applications in coastal areas.
  • Future research should focus on optimizing training data to further enhance predictive capabilities.