Modelling significant wave height in the North Atlantic
(2003) Proceedings of the Thirteenth (2003) International Offshore and Polar Engineering Conference In Proceedings of the International Offshore and Polar Engineering Conference p.3037 Abstract
 The surface of the ocean, and so such quantities as the significant wave height, can be thought of as a random surface in space which develops over time. In this paper, we explore certain types of random fields (in space and time) as models for the significant wave height and fit these models to data obtained from the TOPEXPoseidon satellite. The data consist of observations along different onedimensional tracks over time. It is assumed that, for the region of ocean considered and for a fixed time, the data can be considered stationary. Furthermore, the shape of the data suggests that it is reasonable to use a lognormal distribution. As the covariance function may change over time, the model chosen is fitted to the data for each time... (More)
 The surface of the ocean, and so such quantities as the significant wave height, can be thought of as a random surface in space which develops over time. In this paper, we explore certain types of random fields (in space and time) as models for the significant wave height and fit these models to data obtained from the TOPEXPoseidon satellite. The data consist of observations along different onedimensional tracks over time. It is assumed that, for the region of ocean considered and for a fixed time, the data can be considered stationary. Furthermore, the shape of the data suggests that it is reasonable to use a lognormal distribution. As the covariance function may change over time, the model chosen is fitted to the data for each time separately. The data over space exhibit variation at different scales and hence the covariance function needs to reflect this property. Consequently, a mixture of Gaussian functions is assumed for the covariance function. To fit the model to the data, the theoretical variogram is fitted to the empirical variogram using weighted least squares. Stochastic models for the variation of the parameter values were investigated. The results of fitting these models are discussed and interpreted. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/record/613056
 author
 Baxevani, Anastassia ^{LU} ; Rychlik, Igor ^{LU} and Wilson, Richard J
 organization
 publishing date
 2003
 type
 Chapter in Book/Report/Conference proceeding
 publication status
 published
 subject
 keywords
 Variograms, Gaussian random fields
 in
 Proceedings of the International Offshore and Polar Engineering Conference
 pages
 30  37
 publisher
 International Society of Offshore and Polar Engineers
 conference name
 Proceedings of the Thirteenth (2003) International Offshore and Polar Engineering Conference
 external identifiers

 wos:000223140300005
 scopus:0942288317
 ISSN
 10986189
 language
 English
 LU publication?
 yes
 id
 6efa4a7d8f8541cfadfb5da40948758e (old id 613056)
 date added to LUP
 20071128 09:51:41
 date last changed
 20180529 11:00:01
@inproceedings{6efa4a7d8f8541cfadfb5da40948758e, abstract = {The surface of the ocean, and so such quantities as the significant wave height, can be thought of as a random surface in space which develops over time. In this paper, we explore certain types of random fields (in space and time) as models for the significant wave height and fit these models to data obtained from the TOPEXPoseidon satellite. The data consist of observations along different onedimensional tracks over time. It is assumed that, for the region of ocean considered and for a fixed time, the data can be considered stationary. Furthermore, the shape of the data suggests that it is reasonable to use a lognormal distribution. As the covariance function may change over time, the model chosen is fitted to the data for each time separately. The data over space exhibit variation at different scales and hence the covariance function needs to reflect this property. Consequently, a mixture of Gaussian functions is assumed for the covariance function. To fit the model to the data, the theoretical variogram is fitted to the empirical variogram using weighted least squares. Stochastic models for the variation of the parameter values were investigated. The results of fitting these models are discussed and interpreted.}, author = {Baxevani, Anastassia and Rychlik, Igor and Wilson, Richard J}, booktitle = {Proceedings of the International Offshore and Polar Engineering Conference}, issn = {10986189}, keyword = {Variograms,Gaussian random fields}, language = {eng}, pages = {3037}, publisher = {International Society of Offshore and Polar Engineers}, title = {Modelling significant wave height in the North Atlantic}, year = {2003}, }