Improving Short-Range Ensemble Kalman Storm Surge Forecasting Using Robust Adaptive Inflation
Type
ArticleKAUST Department
Earth Fluid Modeling and Prediction GroupEarth Science and Engineering Program
Physical Science and Engineering (PSE) Division
Water Desalination and Reuse Research Center (WDRC)
Date
2013-08Permanent link to this record
http://hdl.handle.net/10754/552734
Metadata
Show full item recordAbstract
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.Citation
Improving Short-Range Ensemble Kalman Storm Surge Forecasting Using Robust Adaptive Inflation 2013, 141 (8):2705 Monthly Weather ReviewPublisher
American Meteorological SocietyJournal
Monthly Weather ReviewAdditional Links
http://journals.ametsoc.org/doi/abs/10.1175/MWR-D-12-00310.1ae974a485f413a2113503eed53cd6c53
10.1175/MWR-D-12-00310.1