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A Comparison of Ensemble Kalman Filters for Storm Surge Assimilation

M. U. Altaf * King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, and Delft University of Technology, Delft, Netherlands

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T. Butler Department of Mathematical and Statistical Sciences, University of Colorado Denver, Denver, Colorado

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T. Mayo Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas

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X. Luo International Research Institute of Stavanger, Bergen, Norway

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C. Dawson Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas

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A. W. Heemink Delft University of Technology, Delft, Netherlands

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I. Hoteit ** King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

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Print Publication:
01 Aug 2014
DOI:
https://doi.org/10.1175/MWR-D-13-00266.1
Page(s):
2899–2914
Restricted access

Abstract

This study evaluates and compares the performances of several variants of the popular ensemble Kalman filter for the assimilation of storm surge data with the advanced circulation (ADCIRC) model. Using meteorological data from Hurricane Ike to force the ADCIRC model on a domain including the Gulf of Mexico coastline, the authors implement and compare the standard stochastic ensemble Kalman filter (EnKF) and three deterministic square root EnKFs: the singular evolutive interpolated Kalman (SEIK) filter, the ensemble transform Kalman filter (ETKF), and the ensemble adjustment Kalman filter (EAKF). Covariance inflation and localization are implemented in all of these filters. The results from twin experiments suggest that the square root ensemble filters could lead to very comparable performances with appropriate tuning of inflation and localization, suggesting that practical implementation details are at least as important as the choice of the square root ensemble filter itself. These filters also perform reasonably well with a relatively small ensemble size, whereas the stochastic EnKF requires larger ensemble sizes to provide similar accuracy for forecasts of storm surge.

Corresponding author address: I. Hoteit, KAUST, 4700 King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia. E-mail: ibrahim.hoteit@kaust.edu.sa

Abstract

This study evaluates and compares the performances of several variants of the popular ensemble Kalman filter for the assimilation of storm surge data with the advanced circulation (ADCIRC) model. Using meteorological data from Hurricane Ike to force the ADCIRC model on a domain including the Gulf of Mexico coastline, the authors implement and compare the standard stochastic ensemble Kalman filter (EnKF) and three deterministic square root EnKFs: the singular evolutive interpolated Kalman (SEIK) filter, the ensemble transform Kalman filter (ETKF), and the ensemble adjustment Kalman filter (EAKF). Covariance inflation and localization are implemented in all of these filters. The results from twin experiments suggest that the square root ensemble filters could lead to very comparable performances with appropriate tuning of inflation and localization, suggesting that practical implementation details are at least as important as the choice of the square root ensemble filter itself. These filters also perform reasonably well with a relatively small ensemble size, whereas the stochastic EnKF requires larger ensemble sizes to provide similar accuracy for forecasts of storm surge.

Corresponding author address: I. Hoteit, KAUST, 4700 King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia. E-mail: ibrahim.hoteit@kaust.edu.sa
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