A new deterministic Ensemble Kalman Filter with one-step-ahead smoothing for storm surge forecasting
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Raboudi, Naila Thesis
Type
ThesisAuthors
Raboudi, Naila Mohammed Fathi
Advisors
Hoteit, Ibrahim
Committee members
Knio, Omar
Sun, Shuyu

Ait-El-Fquih, Boujemaa
Program
Earth Science and EngineeringKAUST Department
Physical Science and Engineering (PSE) DivisionDate
2016-11Embargo End Date
2017-12-07Permanent link to this record
http://hdl.handle.net/10754/621969
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At the time of archiving, the student author of this thesis opted to temporarily restrict access to it. The full text of this thesis became available to the public after the expiration of the embargo on 2017-12-07.Abstract
The 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.Citation
Raboudi, N. M. F. (2016). A new deterministic Ensemble Kalman Filter with one-step-ahead smoothing for storm surge forecasting. KAUST Research Repository. https://doi.org/10.25781/KAUST-XA185ae974a485f413a2113503eed53cd6c53
10.25781/KAUST-XA185