A Kalman Filter for Ocean Monitoring

Simple item page
dc.creatorBelyaev, Konstantin P.
dc.creatorMueller, Detlev
dc.date2002-07-24
dc.date.accessioned2026-07-07T05:47:54Z
dc.date.available2026-07-07T05:47:54Z
dc.descriptionThe feasibility of global ocean state estimation by sequential data assimilation is demonstrated. The model componenet of the assimilator is the GROB version of the MPIMET ocean circulation model HOPE. Assimilation uses the Fokker-Planck representation of the Kalman Filter. This approach determines the temporal evolution of error statistics by integration of the Fokker-Planck Equation. Phase space advection and diffusion are obtained from histogram techniques considering the model as a black box. For efficiency, the estimate combines nudging and Kalman Filtering. The ocean state is estimated for the El Nino year 1997 by dynamical extrapolation of observed sea-surface temperatures and TAO/Triton subsurface temperatures. The model-data combination yields improved estimates of the ocean's mean state and a realistic record of El Nino related variability. The assimilator proves as an efficient, viable and thus practical approach to operational global ocean state estimation.
dc.description16 pages, 6 figures
dc.identifierhttps://arxiv.org/abs/physics/0207097
dc.identifierhttp://arxiv.org/abs/physics/0207097
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/84828
dc.subjectAtmospheric and Oceanic Physics
dc.subjectData Analysis, Statistics and Probability
dc.titleA Kalman Filter for Ocean Monitoring
dc.typetext

Files

Collections

arXiv