• Berg, R., 2009: Tropical cyclone report: Hurricane Ike (AL092008) 1-14 September 2008. National Hurricane Center Rep., 55 pp.

  • Bishop, C. H., B. J. Etherton, and S. J. Majumdar, 2001: Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Mon. Wea. Rev., 129, 420436.

    • Search Google Scholar
    • Export Citation
  • Blake, E. S., C. W. Landsea, and E. J. Gibney, 2011: The deadliest, costliest, and most intense United States tropical cyclones from 1851 to 2010 (and other frequently requested hurricane facts). NOAA Tech. Memo. NWS NHC-6, 49 pp.

  • Brown, J. D., T. Spencer, and I. Moeller, 2007: Modeling storm surge flooding of an urban area with particular reference to modeling uncertainties: A case study of Canvey Island, United Kingdom. Water Resour. Res., 43, W06402, doi:10.1029/2005WR004597.

    • Search Google Scholar
    • Export Citation
  • Bunya, S., and Coauthors, 2010: A high-resolution coupled riverine flow, tide, wind, wind wave, and storm surge model for southern Louisiana and Mississippi. Part I: Model development and validation. Mon. Wea. Rev., 138, 345377.

    • Search Google Scholar
    • Export Citation
  • Cane, M., A. Kaplan, R. Miller, B. Tang, E. C. Hackert, and A. Busalacchi, 1996: Mapping tropical Pacific sea level: Data assimilation via a reduced state space Kalman filter. J. Geophys. Res., 101 (C10), 599617.

    • Search Google Scholar
    • Export Citation
  • Dee, D. P., 1991: Simplification of the Kalman filter for meteorological data assimilation. Quart. J. Roy. Meteor. Soc., 117, 365384.

    • Search Google Scholar
    • Export Citation
  • Dietrich, J. C., and Coauthors, 2010: A high-resolution coupled riverine flow, tide, wind, wind wave, and storm surge model for southern Louisiana and Mississippi. Part II: Synoptic description and analysis of Hurricanes Katrina and Rita. Mon. Wea. Rev., 138, 378404.

    • Search Google Scholar
    • Export Citation
  • Dietrich, J. C., and Coauthors, 2011: Hurricane Gustav (2008) waves and storm surge: Hindcast, synoptic analysis, and validation in southern Louisiana. Mon. Wea. Rev., 139, 24882522.

    • Search Google Scholar
    • Export Citation
  • El Serafy, G. Y. H., and A. E. Mynett, 2008: Improving the operational forecasting system of the stratified flow in Osaka Bay using an ensemble Kalman filter–based steady state Kalman filter. Water Resour. Res., 44, W06416, doi:10.1029/2006WR005412.

    • Search Google Scholar
    • Export Citation
  • Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99 (C5), 10 14310 162.

    • Search Google Scholar
    • Export Citation
  • Fleming, J., C. Fulcher, R. Luettich, B. Estrade, G. Allen, and H. Winer, 2008: A real time storm surge forecasting system using ADCIRC. Estuarine and Coastal Modeling: Proceedings of the Tenth International Conference, M. L. Spaulding, Ed., ASCE, 893–912.

  • Fukumori, I., and P. Malanotte-Rizzoli, 1995: An approximate Kalman filter for ocean data assimilation: An example with an idealized Gulf Stream model. J. Geophys. Res., 100 (C4), 67776793.

    • Search Google Scholar
    • Export Citation
  • Gerritsen, H., H. de Vries, and M. Philippart, 1995: The Dutch Continental Shelf Model. Quantitative Skill Assessment for Coastal Ocean Models, Coastal and Estuarine Studies, Vol. 47, D. R. Lynch and A. M. Davies, Eds., Amer. Geophys. Union, 425–467.

  • Ghil, M., 1989: Meteorological data assimilation for oceanographers. Part I: Description and theoretical framework. Dyn. Atmos. Oceans, 13, 331376.

    • Search Google Scholar
    • Export Citation
  • Hamill, T., J. Whitaker, and C. Snyder, 2001: Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Mon. Wea. Rev., 129, 27762790.

    • Search Google Scholar
    • Export Citation
  • Hamill, T., J. Whitaker, J. L. Anderson, and C. Snyder, 2009: Comments on “Sigma-point Kalman filter data assimilation methods for strongly nonlinear systems.” J. Atmos. Sci., 66, 34983500.

    • Search Google Scholar
    • Export Citation
  • Heap, N. S., 1983: Storm surges 1967–1982. Geophys. J. Roy. Astron. Soc., 74, 331376.

  • Heemink, A. W., 1986: Storm surge prediction using Kalman filtering. Ph.D. thesis, Twente University of Technology, 194 pp.

  • Heemink, A. W., and H. Kloosterhuis, 1990: Data assimilation for non-linear tidal models. Int. J. Numer. Methods Fluids, 11, 10971112.

    • Search Google Scholar
    • Export Citation
  • Hoang, S., R. Baraille, O. Talagrand, X. Carton, and P. D. Mey, 1998: Adaptive filtering: Application to satellite data assimilation in oceanography. Dyn. Atmos. Oceans, 27 (1–4), 257281, doi:10.1016/S0377-0265(97)00014-6.

    • Search Google Scholar
    • Export Citation
  • Holland, G., 1980: An analytic model of the wind and pressure profiles in hurricanes. Mon. Wea. Rev., 108, 12121218.

  • Hoteit, I., D. Pham, and J. Blum, 2002: A simplified reduced order Kalman filtering and application to altimetric data assimilation in tropical Pacific. J. Mar. Syst., 36, 101127.

    • Search Google Scholar
    • Export Citation
  • Hoteit, I., G. Korres, and G. Triantafyllou, 2005: Comparison of extended and ensemble based Kalman filters with low and high-resolution primitive equations ocean models. Nonlinear Processes Geophys., 12, 755765.

    • Search Google Scholar
    • Export Citation
  • Julier, S., and J. Uhlmann, 1997: A new extension of the Kalman filter to nonlinear systems. Proc. AeroSense: 11th Int. Symp. on Aerospace/Defence Sensing, Simulation and Controls, Orlando, FL, SPIE, 182193.

  • Kalman, R. E., 1960: A new approach to linear filtering and prediction problems. J. Basic Eng., 82, 3545.

  • Kennedy, A. B., and Coauthors, 2011: Origin of the Hurricane Ike forerunner surge. Geophys. Res. Lett., 38, L08608, doi:10.1029/2011GL047090.

    • Search Google Scholar
    • Export Citation
  • Kinnmark, I., 1986: The Shallow Water Wave Equations: Formulation, Analysis, and Applications. Vol. 15, Lecture Notes in Engineering, Springer-Verlag, 187 pp.

  • Knabb, R. D., J. R. Rhome, and D. P. Brown, 2005: Tropical cyclone report: Hurricane Katrina, 23-30 August 2005. National Hurricane Center Rep., 43 pp.

  • Luettich, R., and J. Westerink, 2004: Formulation and numerical implementation of the 2D/3D ADCIRC finite element model version 44.XX. ADCIRC Tech. Rep., 74 pp.

  • Luo, X., and I. Hoteit, 2011: Robust ensemble filtering and its relation to covariance inflation in the ensemble Kalman filter. Mon. Wea. Rev., 139, 39383953.

    • Search Google Scholar
    • Export Citation
  • Lynch, D. R., and W. G. Gray, 1979: A wave equation model for finite element tidal computations. Comput. Fluids, 7, 207228.

  • Malanotte-Rizzoli, P., R. E. Young, and D. B. Haidvogel, 1989: Initialization and data assimilation experiments with a primitive equation model. Dyn. Atmos. Oceans, 13, 349378.

    • Search Google Scholar
    • Export Citation
  • Maybeck, P. S., Ed., 1979: Stochastic Models, Estimation, and Control. Mathematics in Science and Engineering Series, Vol. 141, Academic Press, 411 pp.

  • McRobie, A., T. Spencer, and H. Gerritsen, 2005: The Big Flood: North Sea storm surge. Philos. Trans. Roy. Soc. London, A363, 12631270.

    • Search Google Scholar
    • Export Citation
  • Mitchell, H. L., and P. L. Houtekamer, 2000: An adaptive ensemble Kalman filter. Mon. Wea. Rev., 128, 416433.

  • Murty, T. S., R. A. Flather, and R. F. Henry, 1986: The storm surge problem in the Bay of Bengal. Prog. Oceanogr., 16, 195233.

  • Pham, D., 2001: Stochastic methods for sequential data assimilation in strongly nonlinear systems. Mon. Wea. Rev., 129, 11941207.

  • Pham, D., J. Verron, and M. Roubaud, 1997: Singular evolutive Kalman filter with EOF initialization for data assimilation in oceanography. J. Mar. Syst., 16, 323340.

    • Search Google Scholar
    • Export Citation
  • Sakov, P., and P. R. Oke, 2008: A deterministic formulation of the ensemble Kalman filter: An alternative to ensemble square root filters. Tellus, 60A (2), 361371, doi:10.1111/j.1600-0870.2007.00299.x.

    • Search Google Scholar
    • Export Citation
  • Sorensen, J. V. T., H. Madsen, and H. Madsen, 2004: Efficient Kalman filter techniques for the assimilation of tide gauge data in three-dimensional modeling of the North Sea and Baltic Sea system. J. Geophys. Res., 109, C03017, doi:10.1029/2003JC002144.

    • Search Google Scholar
    • Export Citation
  • Sorensen, J. V. T., H. Madsen, and H. Madsen, 2006: Parameter sensitivity of three Kalman filter schemes for assimilation of water levels in shelf sea models. Ocean Modell., 11, 441463.

    • Search Google Scholar
    • Export Citation
  • Tanaka, S., S. Bunya, J. J. Westerink, C. Dawson, and R. A. Luettich Jr., 2011: Scalability of an unstructured grid continuous Galerkin based hurricane storm surge model. J. Sci. Comput., 46, 329358.

    • Search Google Scholar
    • Export Citation
  • Tippett, M., J. Anderson, C. Bishop, T. Hamill, and J. Whitaker, 2003: Ensemble square root filters. Mon. Wea. Rev., 131, 14851490.

  • Verlaan, M., and A. W. Heemink, 1997: Tidal flow forecasting using reduced rank square root filters. Stochastic Hydrol. Hydraul., 11, 349368.

    • Search Google Scholar
    • Export Citation
  • Vreugdenhil, C. B., 1994: Numerical Methods for Shallow-Water Flow. Kluwer Academic, 261 pp.

  • Wan, E. A., and R. van der Merwe, 2000: The unscented Kalman filter for nonlinear estimation. Proc. Adaptive Systems for Signal Processing, Communications, and Control Symp., Lake Louise, AB, Canada, IEEE, 153–158.

  • Westerink, J. J., and Coauthors, 2008: A basin- to channel-scale unstructured grid hurricane storm surge model applied to southern Louisiana. Mon. Wea. Rev., 136, 833864.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 12 12 12
PDF Downloads 6 6 6

Data Assimilation within the Advanced Circulation (ADCIRC) Modeling Framework for Hurricane Storm Surge Forecasting

View More View Less
  • 1 Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas
  • | 2 King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
  • | 3 Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas
  • | 4 King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
  • | 5 Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas
Restricted access

Abstract

Accurate, real-time forecasting of coastal inundation due to hurricanes and tropical storms is a challenging computational problem requiring high-fidelity forward models of currents and water levels driven by hurricane-force winds. Despite best efforts in computational modeling there will always be uncertainty in storm surge forecasts. In recent years, there has been significant instrumentation located along the coastal United States for the purpose of collecting data—specifically wind, water levels, and wave heights—during these extreme events. This type of data, if available in real time, could be used in a data assimilation framework to improve hurricane storm surge forecasts. In this paper a data assimilation methodology for storm surge forecasting based on the use of ensemble Kalman filters and the advanced circulation (ADCIRC) storm surge model is described. The singular evolutive interpolated Kalman (SEIK) filter has been shown to be effective at producing accurate results for ocean models using small ensemble sizes initialized by an empirical orthogonal function analysis. The SEIK filter is applied to the ADCIRC model to improve storm surge forecasting, particularly in capturing maximum water levels (high water marks) and the timing of the surge. Two test cases of data obtained from hindcast studies of Hurricanes Ike and Katrina are presented. It is shown that a modified SEIK filter with an inflation factor improves the accuracy of coarse-resolution forecasts of storm surge resulting from hurricanes. Furthermore, the SEIK filter requires only modest computational resources to obtain more accurate forecasts of storm surge in a constrained time window where forecasters must interact with emergency responders.

Corresponding author address: T. Butler, Institute for Computational Engineering and Sciences, The University of Texas at Austin, 1 University Station C0200, Austin, TX 78712. E-mail: tbutler@ices.utexas.edu

Abstract

Accurate, real-time forecasting of coastal inundation due to hurricanes and tropical storms is a challenging computational problem requiring high-fidelity forward models of currents and water levels driven by hurricane-force winds. Despite best efforts in computational modeling there will always be uncertainty in storm surge forecasts. In recent years, there has been significant instrumentation located along the coastal United States for the purpose of collecting data—specifically wind, water levels, and wave heights—during these extreme events. This type of data, if available in real time, could be used in a data assimilation framework to improve hurricane storm surge forecasts. In this paper a data assimilation methodology for storm surge forecasting based on the use of ensemble Kalman filters and the advanced circulation (ADCIRC) storm surge model is described. The singular evolutive interpolated Kalman (SEIK) filter has been shown to be effective at producing accurate results for ocean models using small ensemble sizes initialized by an empirical orthogonal function analysis. The SEIK filter is applied to the ADCIRC model to improve storm surge forecasting, particularly in capturing maximum water levels (high water marks) and the timing of the surge. Two test cases of data obtained from hindcast studies of Hurricanes Ike and Katrina are presented. It is shown that a modified SEIK filter with an inflation factor improves the accuracy of coarse-resolution forecasts of storm surge resulting from hurricanes. Furthermore, the SEIK filter requires only modest computational resources to obtain more accurate forecasts of storm surge in a constrained time window where forecasters must interact with emergency responders.

Corresponding author address: T. Butler, Institute for Computational Engineering and Sciences, The University of Texas at Austin, 1 University Station C0200, Austin, TX 78712. E-mail: tbutler@ices.utexas.edu
Save