Improving Short-Range Ensemble Kalman Storm Surge Forecasting Using Robust Adaptive Inflation

Muhammad Altaf, T. Butler, X. Luo, C. Dawson, T. Mayo, Ibrahim Hoteit

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


This paper presents a robust ensemble filtering methodology for storm surge forecasting based on the singular evolutive interpolated Kalman (SEIK) filter, which has been implemented in the framework of the H∞ filter. By design, an H∞ filter is more robust than the common Kalman filter in the sense that the estimation error in the H∞ filter has, in general, a finite growth rate with respect to the uncertainties in assimilation. The computational hydrodynamical model used in this study is the Advanced Circulation (ADCIRC) model. The authors assimilate data obtained from Hurricanes Katrina and Ike as test cases. The results clearly show that the H∞-based SEIK filter provides more accurate short-range forecasts of storm surge compared to recently reported data assimilation results resulting from the standard SEIK filter.
Original languageEnglish (US)
Pages (from-to)2705-2720
Number of pages16
JournalMonthly Weather Review
Issue number8
StatePublished - Aug 2013

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KAUST Repository Item: Exported on 2020-10-01


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