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Wave height data assimilation using non-stationary kriging
dc.contributor.author | Tolosana Delgado, Raimon |
dc.contributor.author | Egozcue Rubí, Juan José |
dc.contributor.author | Sánchez-Arcilla Conejo, Agustín |
dc.contributor.author | Gómez Aguar, Jesús Javier |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Matemàtica Aplicada III |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria Hidràulica, Marítima i Ambiental |
dc.date.accessioned | 2011-05-06T08:06:38Z |
dc.date.available | 2011-05-06T08:06:38Z |
dc.date.created | 2011-03 |
dc.date.issued | 2011-03 |
dc.identifier.citation | Tolosana-Delgado, R. [et al.]. Wave height data assimilation using non-stationary kriging. "Computers and geosciences", Març 2011, vol. 37, núm. 3, p. 363-370. |
dc.identifier.issn | 0098-3004 |
dc.identifier.uri | http://hdl.handle.net/2117/12489 |
dc.description.abstract | Data assimilation into numerical models should be both computationally fast and physically meaningful, in order to be applicable in online environmental surveillance. We present a way to improve assimilation for computationally intensive models, based on non-stationary kriging and a separable space–time covariance function. The method is illustrated with significant wave height data. The covariance function is expressed as a collection of fields: each one is obtained as the empirical covariance between the studied property(significant wave height in log-scale)at a pixel where a measurement is located (a wave-buoy is available)and the same parameter at every other pixel of thef ield. These covariances are computed from the available history of forecasts. The method provides a set of weights, that can be mapped for each measuring location, and that do not vary with time. Resulting weights may be used in a weighted average of the differences between the forecast and measured parameter. In the case presented, these weights may show long-range connection patterns, such as between the Catalan coast and the eastern coast of Sardinia, associated to common prevailing meteo-oceanographic conditions. When such patterns are considered as non-informative of the present situation, it is always possible to diminish their influence by relaxing the covariance maps. |
dc.format.extent | 8 p. |
dc.language.iso | eng |
dc.subject | Àrees temàtiques de la UPC::Matemàtiques i estadística::Matemàtica aplicada a les ciències |
dc.subject.lcsh | Kalman filtering |
dc.subject.lcsh | Kriging |
dc.subject.lcsh | Geology--Statistical methods |
dc.subject.lcsh | Waves--Mathematical models |
dc.title | Wave height data assimilation using non-stationary kriging |
dc.type | Article |
dc.subject.lemac | Filtres de Kalman |
dc.subject.lemac | Geologia -- Mètodes estadístics |
dc.subject.lemac | Ones -- Models matemàtics |
dc.contributor.group | Universitat Politècnica de Catalunya. NRG - Riscos Naturals i Geoestadística |
dc.contributor.group | Universitat Politècnica de Catalunya. LIM/UPC - Laboratori d'Enginyeria Marítima |
dc.identifier.doi | 10.1016/j.cageo.2010.05.019 |
dc.relation.publisherversion | http://linkinghub.elsevier.com/retrieve/pii/S0098300410002761 |
dc.rights.access | Restricted access - publisher's policy |
local.identifier.drac | 5471448 |
dc.description.version | Postprint (published version) |
dc.relation.projectid | info:eu-repo/grantAgreement/EC/FP7/242284/EU/Fluxes, Interactions and Environment at the Land-Ocean Boundary. Downscaling, Assimilation and Coupling/FIELD_AC |
local.citation.author | Tolosana-Delgado, R.; Egozcue, J. J.; Sanchez-Arcilla, A.; Gomez, J. |
local.citation.publicationName | Computers and geosciences |
local.citation.volume | 37 |
local.citation.number | 3 |
local.citation.startingPage | 363 |
local.citation.endingPage | 370 |
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