Improving Short-Range Ensemble Kalman Storm Surge Forecasting Using Robust Adaptive Inflation
KAUST DepartmentWater Desalination and Reuse Research Center (WDRC)
Physical Sciences and Engineering (PSE) Division
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AbstractThis 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.
CitationImproving Short-Range Ensemble Kalman Storm Surge Forecasting Using Robust Adaptive Inflation 2013, 141 (8):2705 Monthly Weather Review
PublisherAmerican Meteorological Society
JournalMonthly Weather Review