Forecasting near-surface ocean winds with Kalman filter techniques
(2005) In Ocean Engineering 32(3-4). p.273-291- Abstract
- 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.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/254147
- author
- Malmberg, Anders LU ; Holst, Ulla LU and Holst, Jan LU
- organization
- publishing date
- 2005
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- near-surface, ocean winds, forecasting, filtering, space-time Kalman, dimension reduction, principal components
- in
- Ocean Engineering
- volume
- 32
- issue
- 3-4
- pages
- 273 - 291
- publisher
- Elsevier
- external identifiers
-
- wos:000226896100002
- scopus:10944245372
- ISSN
- 1873-5258
- DOI
- 10.1016/j.oceaneng.2004.08.005
- language
- English
- LU publication?
- yes
- id
- c69b303d-caef-4b2b-9b5b-936eb885483f (old id 254147)
- date added to LUP
- 2016-04-01 11:36:00
- date last changed
- 2022-04-20 19:02:56
@article{c69b303d-caef-4b2b-9b5b-936eb885483f, abstract = {{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.}}, author = {{Malmberg, Anders and Holst, Ulla and Holst, Jan}}, issn = {{1873-5258}}, keywords = {{near-surface; ocean winds; forecasting; filtering; space-time Kalman; dimension reduction; principal components}}, language = {{eng}}, number = {{3-4}}, pages = {{273--291}}, publisher = {{Elsevier}}, series = {{Ocean Engineering}}, title = {{Forecasting near-surface ocean winds with Kalman filter techniques}}, url = {{http://dx.doi.org/10.1016/j.oceaneng.2004.08.005}}, doi = {{10.1016/j.oceaneng.2004.08.005}}, volume = {{32}}, year = {{2005}}, }