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dc.contributor.authorHildeman, Anders
dc.contributor.authorBolin, David
dc.contributor.authorRychlik, Igor
dc.date.accessioned2020-06-03T13:53:01Z
dc.date.available2020-06-03T13:53:01Z
dc.date.issued2020-05-07
dc.date.submitted2019-10-22
dc.identifier.citationHildeman, A., Bolin, D., & Rychlik, I. (2020). Deformed SPDE models with an application to spatial modeling of significant wave height. Spatial Statistics, 100449. doi:10.1016/j.spasta.2020.100449
dc.identifier.issn2211-6753
dc.identifier.doi10.1016/j.spasta.2020.100449
dc.identifier.urihttp://hdl.handle.net/10754/662999
dc.description.abstractA non-stationary Gaussian random field model is developed based on a combination of the stochastic partial differential equation (SPDE) approach and the classical deformation method. With the deformation method, a stationary field is defined on a domain which is deformed so that the field becomes non-stationary. We show that if the stationary field is a Matérn field defined as a solution to a fractional SPDE, the resulting non-stationary model can be represented as the solution to another fractional SPDE on the deformed domain. By defining the model in this way, the computational advantages of the SPDE approach can be combined with the deformation method's more intuitive parameterization of non-stationarity. In particular it allows for independent control over the non-stationary practical correlation range and the variance, which has not been possible with previously proposed non-stationary SPDE models. The model is tested on spatial data of significant wave height, a characteristic of ocean surface conditions which is important when estimating the wear and risks associated with a planned journey of a ship. The model parameters are estimated to data from the north Atlantic using a maximum likelihood approach. The fitted model is used to compute wave height exceedance probabilities and the distribution of accumulated fatigue damage for ships traveling a popular shipping route. The model results agree well with the data, indicating that the model could be used for route optimization in naval logistics.
dc.description.sponsorshipThis work has been supported in part by the Swedish Research Council under grant No. 2016-04187. We would like to thank the European Center for Medium-range Weather Forecast for the development of the ERA-Interim data set and for making it publicly available. The data used was the ERA-Interim reanalysis dataset, Copernicus Climate Change Service (C3S) (accessed September 2018), available from https://www.ecmwf.int/en/forecasts/datasets/archive-datasets/reanalysis-datasets/era-interim.
dc.publisherElsevier BV
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/S2211675320300439
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Spatial Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial Statistics, [, , (2020-05-07)] DOI: 10.1016/j.spasta.2020.100449 . © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleDeformed SPDE models with an application to spatial modeling of significant wave height
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology, Saudi Arabia
dc.identifier.journalSpatial Statistics
dc.rights.embargodate2022-05-07
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Space-, Earth-, and Environmental Sciences, Chalmers University of Technology, Sweden
dc.contributor.institutionDepartment of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, Sweden
dc.identifier.pages100449
kaust.personHildeman, Anders
kaust.personBolin, David
kaust.personRychlik, Igor
dc.date.accepted2020-04-23
dc.identifier.eid2-s2.0-85085057688
refterms.dateFOA2020-06-04T06:46:26Z


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