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Forecasting near-surface ocean winds with Kalman filter techniques

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Author

Summary, in English

In this paper a statistical forecasting model designed for bounded areas of near-surface ocean wind speeds is implemented. Dimension reduction is achieved by decomposing the covariance structure into one large-scale and one small-scale component using empirical orthogonal functions. The large-scale component is modelled with an AR process and forecasts are calculated by applying a Kalman filter. The model is suited for stable weather situations as for unsteady situations it requires more frequent wind information. From the prediction variance fields it is possible to identify where unexpected weather usually enters the area.

Publishing year

2005

Language

English

Pages

273-291

Publication/Series

Ocean Engineering

Volume

32

Issue

3-4

Document type

Journal article

Publisher

Elsevier

Topic

  • Probability Theory and Statistics

Keywords

  • near-surface
  • ocean winds
  • forecasting
  • filtering
  • space-time Kalman
  • dimension reduction
  • principal components

Status

Published

ISBN/ISSN/Other

  • ISSN: 1873-5258