A Deep Learning Model for Improved Wind and Consequent Wave Forecasts

Yuval Yevnin*, Yaron Toledo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The paper presents a combined numerical–deep learning (DL) approach for improving wind and wave forecasting. First, a DL model is trained to improve wind velocity forecasts by using past reanalysis data. The improved wind forecasts are used as forcing in a numerical wave forecasting model. This novel approach, used to combine physics-based and data-driven models, was tested over the Mediterranean. The correction to the wind forecast resulted in ~10% RMSE improvement in both wind velocity and wave height over reanalysis data. This significant improvement is even more substantial at the Aegean Sea when Etesian winds are dominant, improving wave height forecasts by over 35%. The additional computational costs of the DL model are negligible compared to the costs of either the atmospheric or wave numerical model by itself. This work has the potential to greatly improve the wind and wave forecasting models used nowadays by tailoring models to localized seasonal conditions, at negligible additional computational costs.

Original languageEnglish
Pages (from-to)2531-2537
Number of pages7
JournalJournal of Physical Oceanography
Volume52
Issue number10
DOIs
StatePublished - Oct 2022

Keywords

  • Artificial intelligence
  • Deep learning
  • Forecasting
  • Forecasting techniques
  • Hindcasts
  • Machine learning
  • Neural networks
  • Numerical analysis/modeling
  • Numerical weather prediction/forecasting
  • Ocean models
  • Operational forecasting
  • Optimization
  • Postprocessing
  • Reanalysis data
  • Regression
  • Statistical forecasting

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