Print Email Facebook Twitter Improving short-range ensemble Kalman storm surge forecasting using robust adaptive inflation Title Improving short-range ensemble Kalman storm surge forecasting using robust adaptive inflation Author Altaf, M.U. Butler, T. Luo, X. Dawson, C. Mayo, T. Hoteit, I. Faculty Electrical Engineering, Mathematics and Computer Science Department Applied Mathematics Date 2013-08-31 Abstract 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. Subject Kalman filtersensemblesoperational forecastingshort-range prediction To reference this document use: http://resolver.tudelft.nl/uuid:32c996dc-cdca-4655-9a1a-e16322ca86ff DOI https://doi.org/10.1175/MWR-D-12-00310.1 Publisher American Meteorological Society Embargo date 2014-02-28 ISSN 1520-0493 Source Monthly weather review, 141 (8), 2013 Part of collection Institutional Repository Document type journal article Rights (c) 2013 American Meteorological Society Files PDF mwr-d-12-00310.1.pdf 2.19 MB Close viewer /islandora/object/uuid:32c996dc-cdca-4655-9a1a-e16322ca86ff/datastream/OBJ/view