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dc.contributor.advisorHoteit, Ibrahim
dc.contributor.authorRaboudi, Naila
dc.date.accessioned2016-12-07T13:46:07Z
dc.date.available2016-12-07T13:46:07Z
dc.date.issued2016-11
dc.identifier.doi10.25781/KAUST-XA185
dc.identifier.urihttp://hdl.handle.net/10754/621969
dc.description.abstractThe Ensemble Kalman Filter (EnKF) is a popular data assimilation method for state-parameter estimation. Following a sequential assimilation strategy, it breaks the problem into alternating cycles of forecast and analysis steps. In the forecast step, the dynamical model is used to integrate a stochastic sample approximating the state analysis distribution (called analysis ensemble) to obtain a forecast ensemble. In the analysis step, the forecast ensemble is updated with the incoming observation using a Kalman-like correction, which is then used for the next forecast step. In realistic large-scale applications, EnKFs are implemented with limited ensembles, and often poorly known model errors statistics, leading to a crude approximation of the forecast covariance. This strongly limits the filter performance. Recently, a new EnKF was proposed in [1] following a one-step-ahead smoothing strategy (EnKF-OSA), which involves an OSA smoothing of the state between two successive analysis. At each time step, EnKF-OSA exploits the observation twice. The incoming observation is first used to smooth the ensemble at the previous time step. The resulting smoothed ensemble is then integrated forward to compute a "pseudo forecast" ensemble, which is again updated with the same observation. The idea of constraining the state with future observations is to add more information in the estimation process in order to mitigate for the sub-optimal character of EnKF-like methods. The second EnKF-OSA "forecast" is computed from the smoothed ensemble and should therefore provide an improved background. In this work, we propose a deterministic variant of the EnKF-OSA, based on the Singular Evolutive Interpolated Ensemble Kalman (SEIK) filter. The motivation behind this is to avoid the observations perturbations of the EnKF in order to improve the scheme's behavior when assimilating big data sets with small ensembles. The new SEIK-OSA scheme is implemented and its efficiency is demonstrated by performing assimilation experiments with the highly nonlinear Lorenz model and a realistic setting of the Advanced Circulation (ADCIRC) model configured for storm surge forecasting in the Gulf of Mexico during Hurricane Ike.
dc.language.isoen
dc.subjectEnsemble
dc.subjectFiltering
dc.subjectOne Step Ahead
dc.subjectSmoothing
dc.titleA new deterministic Ensemble Kalman Filter with one-step-ahead smoothing for storm surge forecasting
dc.typeThesis
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Division
thesis.degree.grantorKing Abdullah University of Science and Technology
dc.contributor.committeememberKnio, Omar M.
dc.contributor.committeememberSun, Shuyu
dc.contributor.committeememberAit-El-Fquih, Boujemaa
thesis.degree.disciplineEarth Sciences and Engineering
thesis.degree.nameMaster of Science
refterms.dateFOA2017-12-07T00:00:00Z


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