Sequential Data Assimilation: Information Fusion of a Numerical Simulation and Large Scale Observation Data

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dc.creatorNakamura,Kazuyuki
dc.creatorHiguchi,Tomoyuki
dc.creatorHirose,Naoki
dc.date2006
dc.date.accessioned2024-02-06T12:54:25Z
dc.date.available2024-02-06T12:54:25Z
dc.descriptionData assimilation is a method of combining an imperfect simulation model and a number of incomplete observation data. Sequential data assimilation is a data assimilation in which simulation variables are corrected at every time step of observation. The ensemble Kalman filter is developed for a sequential data assimilation and frequently used in geophysics. On the other hand, the particle filter developed and used in statistics is similar in view of ensemble-based method, but it has different properties. In this paper, these two ensemble based filters are compared and characterized through matrix representation. An application of sequential data assimilation to tsunami simulation model with a numerical experiment is also shown. The particle filter is employed for this application. An erroneous bottom topography is corrected in the numerical experiment, which demonstrates that the particle filter is useful tool as the sequential data assimilation method.
dc.formattext/html
dc.identifierhttps://doi.org/10.3217/jucs-012-06-0608
dc.identifierhttps://lib.jucs.org/article/28619/
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/9035
dc.languageen
dc.publisherJournal of Universal Computer Science
dc.relationinfo:eu-repo/semantics/altIdentifier/eissn/0948-6968
dc.relationinfo:eu-repo/semantics/altIdentifier/pissn/0948-695X
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsJ.UCS License
dc.sourceJUCS - Journal of Universal Computer Science 12(6): 608-626
dc.subjectparticle filter
dc.subjectsimulation science
dc.subjectdata fusion
dc.titleSequential Data Assimilation: Information Fusion of a Numerical Simulation and Large Scale Observation Data
dc.typeResearch Article
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