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Advanced Numerical Modeling and Data Assimilation Techniques for Tropical Cyclone Prediction pp 331–360Cite as

Data Assimilation and Predictability of Tropical Cyclones

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Abstract

Hurricanes are one of deadliest and costliest natural hazards, with total losses topping $100 billion for the first time in 2005 (Pielke et al., 2008). Accurate predictions of hurricanes, therefore, have enormous economic value, and demand is increasing for more accurate forecasts with longer lead times and more precise warnings to minimize losses due to hurricane preparation and evacuation as well as to destruction. Over the past decade, significant progress has been made in short-range (up to five days) track forecasts of tropical cyclones. The current day average 48-h forecast position is as accurate as a 24-h track forecast 10 yr ago (Franklin, 2004).

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Notes

  1. 1.

    Online information is available at http://www.nhc.noaa.gov/modelsummary.shtml.

References

  • Alpert, J.C. and V.K. Kumar, 2007: Radial wind super-obs from the WSR-88D radars in the NCEP operational assimilation system. Mon. Wea. Rev., 135, 1090-1108.

    Article  Google Scholar 

  • Bei, N. and F. Zhang, 2007: Mesoscale predictability of the torrential rainfall along the mei-yu front of China. Quart. J. Roy. Meteor. Soc., 133, 83-99.

    Article  Google Scholar 

  • Bennett, A.F., 1992: Inverse methods in physical oceanography. Cambridge University Press, Cambridge.

    Book  Google Scholar 

  • Bennett, A.F. and B.S. Chua, 1994: Open-ocean modelling as an inverse problem: The primitive equations. Mon Weather Rev, 122, 1326-1336.

    Article  Google Scholar 

  • Bennett, A.F., B.S. Chua and L.M. Leslie, 1996: Generalized inversion of a global numerical weather prediction model. Meteorol Atmos Phys, 60, 165-178.

    Article  Google Scholar 

  • Bennett, A.F., L.M. Leslie, C.R. Hagelberg and P.E. Powers, 1993: Tropical cyclone prediction using a barotropic model initialized by a generalized inverse method. Mon Weather Rev, 121, 1714-1729.

    Article  Google Scholar 

  • Chen, S.S., J.F. Price, W. Zhao, M.A. Donelan and E.J. Walsh, 2007: The CBLAST-Hurricane Program and the next generation fully coupled atmosphere-wave-ocean models for hurricane research and prediction. Bull. Amer. Meteor. Soc., 88, 311-317.

    Article  Google Scholar 

  • Courtier, P. and O. Talagrand, 1987: Variational assimilation of meteorological observations with the adjoint vorticity equation II: Numerical results. Q J R Meteorol Soc, 113, 1329-1347.

    Article  Google Scholar 

  • Courtier, P., Thepaut and A. Hollingsworth, 1994: A strategy for operational implementation of 4DVAR, using an incremental approach. Q J R Meteorol Soc, 120, 1367-1387

    Article  Google Scholar 

  • Davis, C.A. et al., 2008: Prediction of landfalling hurricanes with the advanced hurricane WRF model. Mon. Wea. Rev., 136, 1990-2005.

    Article  Google Scholar 

  • Elsberry, R.L., T.D.B. Lambert and M.A. Boothe, 2007: Accuracy of Atlantic and eastern North Pacific tropical cyclone intensity forecast guidance. Wea. Forecasting, 22, 747-762.

    Article  Google Scholar 

  • Emanuel, K.A., 1999: Thermodynamic control of hurricane intensity. Nature, 401, 665-669.

    Article  Google Scholar 

  • Evensen, G., 2003: The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dyn., 53, 343-367.

    Article  Google Scholar 

  • Franklin, J.L., 2004: National Hurricane Centre verification report. Proc. 57th Interdepartmental Hurricane Conf., Miami, FL, NOAA. Available online at http://www.nhc.noaa.gov/verification/pdfs/Verification_2008.pdf.

    Google Scholar 

  • Franklin, J.L., 2005: 2004 National Hurricane Centre forecast verification report. NHC. Available online at http://www.nhc.boulder.noaa.gov/verification/pdfs/Verification_2004.pdf.

  • Gao, J., M. Xue, A. Shapiro and K.K. Droegemeier, 1999: A variational method for the analysis of three-dimensional wind fields from two Doppler radars. Mon. Wea. Rev., 127, 2128-2142.

    Article  Google Scholar 

  • Hawblitzel, D., F. Zhang, Z. Meng and C.A. Davis, 2007: Probabilistic evaluation of the dynamics and predictability of mesoscale convective vortex event of 10-13 June 2003. Mon. Wea. Rev., 135, 1544-1563.

    Article  Google Scholar 

  • Hendricks, E.A., M.T. Montgomery and C.A. Davis, 2004: The role of “vortical” hot towers in the formation of Tropical Cyclone Diana (1984). J. Atmos. Sci., 61, 1209-1232.

    Article  Google Scholar 

  • Holland, G.J., 1997: The maximum potential intensity of tropical cyclones. J. Atmos. Sci., 54, 2519-2541.

    Article  Google Scholar 

  • Houze, R.A., S.S. Chen, B.F. Smull, W.-C. Lee and M.M. Bell, 2007: Hurricane intensity and eyewall replacement. Science, 315, 1235-1238.

    Article  Google Scholar 

  • Knabb, R.D., J.R. Rhome and D.P. Brown, 2006: Tropical cyclone report: Hurricane Katrina: 23-30 August 2005. National Hurricane Centre, Miami, FL. Available online at http://www.nhc.noaa.gov/pdf/TCR-AL122005_Katrina.pdf.

    Google Scholar 

  • Krishnamurti, T.N., S. Pattnaik, L. Stefanova, T.S.V. Kumar, B.P. Mackey, A.J. O’Shay and R.J. Pasch, 2005: The hurricane intensity issue. Mon. Wea. Rev., 133, 1886-1912.

    Article  Google Scholar 

  • Laroche, S. and I. Zawadzki, 1994: A variational analysis method for the retrieval of three-dimensional wind field from single-Doppler radar data. J. Atmos. Sci., 51, 2664-2682.

    Article  Google Scholar 

  • Lindskog, M., H. Järvinen and D.B. Michelson, 2000: Assimilation of radar radial winds in the HIRLAM 3D-Var. Phys. Chem. Earth, 25B, 1243-1249.

    Article  Google Scholar 

  • Lindskog, M., K. Salonen, H. Jarvinen and D.B. Michelson, 2004: Doppler radar wind data assimilation with HIRLAM 3DVAR. Mon. Wea. Rev., 132, 1081-1092.

    Article  Google Scholar 

  • Montgomery, M.T., M.E. Nicholls, T.A. Cram and A.B. Saunders, 2006: A vortical hot tower route to tropical cyclogenesis. J. Atmos. Sci., 63, 355-386.

    Article  Google Scholar 

  • Pielke, R.A., Jr., J. Gratz, C.W. Landsea, D. Collins, M.A. Saunders and R. Musulin, 2008: Normalized hurricane damages in the United States. Nat. Hazards Rev., 9, 29-42.

    Article  Google Scholar 

  • Qiu, C. and Q. Xu, 1992: A simple adjoint method of wind analysis for single-Doppler data. J. Atmos. Oceanic Technol., 9, 588-598.

    Article  Google Scholar 

  • Rappin, E.D. and D.S. Nolan, 2012: The effect of vertical shear orientation on tropical cyclogenesis. Q. J. Roy. Meteorol. Soc., DOI: 10.1002/qj.977.

    Google Scholar 

  • Shapiro, A., S. Ellis and J. Shaw, 1995: Single-Doppler velocity retrievals with Phoenix II data: Clear air and microburst wind retrievals in the planetary boundary layer. J. Atmos. Sci., 52, 1265-1287.

    Article  Google Scholar 

  • Shen, B.W., R. Atlas, O. Reale, S.-J. Lin, J.-D. Chern, J. Chang, C. Henze and J.-L. Li, 2006: Hurricane forecasts with a global mesoscale-resolving model: Preliminary results with Hurricane Katrina (2005). Geophys. Res. Lett., 33, L13813, doi:10.1029/2006GL026143.

    Article  Google Scholar 

  • Snyder, C. and F. Zhang, 2003: Assimilation of simulated Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 131, 1663-1677.

    Article  Google Scholar 

  • Sun, J. and N.A. Crook, 1997: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part I: Model development and simulated data experiments. J. Atmos. Sci., 54, 1642-1661.

    Article  Google Scholar 

  • Talagrand, O. and P. Courtier, 1987: Variational assimilation of meteorological observations with the adjoint vorticity equation. I: Theory. Q J R Meteorol Soc, 113, 1311-1328.

    Article  Google Scholar 

  • Tao, D. and F. Zhang, 2014: Effect of environmental shear, sea-surface temperature and ambient moisture on the formation and predictability of tropical cyclones: An ensemble-mean perspective. Journal of Advances in Modeling Earth Systems (JAMES), DOI: 10.1002/2014MS000314.

    Google Scholar 

  • Tao, D. and F. Zhang, 2015: Effects of vertical wind shear, on the predictability of tropical cyclones: Pratical versus intrinsic limit J. Adv. Model. Earth Syst., DOI: 10.1002/2015MS00474.

  • Van Sang, N., R.K. Smith and M.T. Montgomery, 2008: Tropical cyclone intensification and predictability in three dimensions. Quart. J. Roy. Meteor. Soc., 134, 563-582.

    Article  Google Scholar 

  • Weygandt, S.S., A. Shapiro and K.K. Droegemeier, 2002: Retrieval of model initial fields from single-Doppler observations of a supercell thunderstorm. Part I: Single-Doppler velocity retrieval. Mon. Wea. Rev., 130, 433-453.

    Article  Google Scholar 

  • Willoughby, H.E., 1999: Hurricane heat engines. Nature, 401, 649-650.

    Article  Google Scholar 

  • Xu, Q., H.D. Gu and S. Yang, 2001: Simple adjoint method for three-dimensional wind retrievals from single-Doppler data. Quart. J. Roy. Meteor. Soc., 127, 1053-1067.

    Article  Google Scholar 

  • Zhang, F., A. Odins and J.W. Nielsen-Gammon, 2006: Mesoscale predictability of an extreme warm-season rainfall event. Wea. Forecasting, 21, 149-166.

    Article  Google Scholar 

  • Zhang, F. and C. Snyder, 2007: Ensemble-based data assimilation. Bull. Amer. Meteor. Soc., 88, 565-568.

    Article  Google Scholar 

  • Zhang, F. and J.A. Sippel, 2009: Effects of moist convection on hurricane predictability. J. Atmos. Sci., 66, 1944-1961.

    Article  Google Scholar 

  • Zhang, F., C. Snyder and R. Rotunno, 2002: Mesoscale predictability of the “surprise” snowstorm of 24-25 January 2000. Mon. Wea. Rev., 130, 1617-1632.

    Article  Google Scholar 

  • Zhang, F., C. Snyder and R. Rotunno, 2003: Effects of moist convection on mesoscale predictability. J. Atmos. Sci., 60, 1173-1185.

    Article  Google Scholar 

  • Zhang, F., N. Bei, R. Rotunno, C. Snyder and C.C. Epifanio, 2007: Mesoscale predictability of moist baroclinic waves: Convection-permitting experiments and multistage error growth dynamics. J. Atmos. Sci., 64, 3579-3594.

    Article  Google Scholar 

  • Zhang, F. and Y. Weng, 2015: Predicting Hurricane, Intensity and Associated Hazards: A Five year Real-Time Forecast Experiment with Assimilation of Airborne Doppler Radar Observations. Bulletin of the American Meteorological Society, 96, 25-32.

    Article  Google Scholar 

  • Zhang, F., Z. Meng and A. Aksoy, 2006: Test of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part I: Perfect model experiments. Mon. Wea. Rev., 134, 722-736.

    Article  Google Scholar 

  • Zhang, F., Z. Meng, J.F. Gamache and F.D. Marks, 2011: Performance of convection-permitting hurricane initialization and prediction during 2008-2010 with ensemble data assimilation of innercore airborne Doppler radar observations. Geophys. Res. Lett., 38, L15810, doi:10.1029/2011GL048469.

    Google Scholar 

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Authors and Affiliations

  1. Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania, USA

    Fuqing Zhang

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  1. Fuqing Zhang
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Editors and Affiliations

  1. School of Earth, Ocean and Climate Sciences, Indian Institute of Technology Bhubaneswar, 110016, Hauz Khas, Delhi, India

    U. C. Mohanty

  2. Hurricane Research Division, Atlantic Oceanographic Meteorological Laboratory, NOAA, Rickenbacker Causeway, 4301, 33149, Miami, FL, USA

    Sundararaman G. Gopalakrishnan

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Zhang, F. (2016). Data Assimilation and Predictability of Tropical Cyclones. In: Mohanty, U.C., Gopalakrishnan, S.G. (eds) Advanced Numerical Modeling and Data Assimilation Techniques for Tropical Cyclone Prediction. Springer, Dordrecht. https://doi.org/10.5822/978-94-024-0896-6_12

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