Evaluation of Atmosphere and Ocean Initial Condition Uncertainty and Stochastic Exchange Coefficients on Ensemble Tropical Cyclone Intensity Forecasts

Ryan D. Torn Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, Albany, New York

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Abstract

Tropical cyclone (TC) intensity forecasts are impacted by errors in atmosphere and ocean initial conditions and the model formulation, which motivates using an ensemble approach. This study evaluates the impact of uncertainty in atmospheric and oceanic initial conditions, as well as stochastic representations of the drag Cd and enthalphy Ck exchange coefficients on ensemble Advanced Hurricane WRF (AHW) TC intensity forecasts of multiple Atlantic TCs from 2008 to 2011. Each ensemble experiment is characterized by different combinations of either deterministic or ensemble atmospheric and/or oceanic initial conditions, as well as fixed or stochastic representations of Cd or Ck. Among those experiments with a single uncertainty source, atmospheric uncertainty produces the largest standard deviation in TC intensity. While ocean uncertainty leads to continuous growth in ensemble standard deviation, the ensemble standard deviation in the experiments with Cd and Ck uncertainty levels off by 48 h. Combining atmospheric and oceanic uncertainty leads to larger intensity standard deviation than atmosphere or ocean uncertainty alone and preferentially adds variability outside of the TC core. By contrast, combining Cd or Ck uncertainty with any other source leads to negligible increases in standard deviation, which is mainly due to the lack of spatial correlation in the exchange coefficient perturbations. All of the ensemble experiments are deficient in ensemble standard deviation; however, the experiments with combinations of uncertainty sources generally have an ensemble standard deviation closer to the ensemble-mean errors.

Corresponding author address: Ryan Torn, University at Albany, State University of New York, ES 351, 1400 Washington Ave., Albany, NY 12222. E-mail: rtorn@albany.edu

Abstract

Tropical cyclone (TC) intensity forecasts are impacted by errors in atmosphere and ocean initial conditions and the model formulation, which motivates using an ensemble approach. This study evaluates the impact of uncertainty in atmospheric and oceanic initial conditions, as well as stochastic representations of the drag Cd and enthalphy Ck exchange coefficients on ensemble Advanced Hurricane WRF (AHW) TC intensity forecasts of multiple Atlantic TCs from 2008 to 2011. Each ensemble experiment is characterized by different combinations of either deterministic or ensemble atmospheric and/or oceanic initial conditions, as well as fixed or stochastic representations of Cd or Ck. Among those experiments with a single uncertainty source, atmospheric uncertainty produces the largest standard deviation in TC intensity. While ocean uncertainty leads to continuous growth in ensemble standard deviation, the ensemble standard deviation in the experiments with Cd and Ck uncertainty levels off by 48 h. Combining atmospheric and oceanic uncertainty leads to larger intensity standard deviation than atmosphere or ocean uncertainty alone and preferentially adds variability outside of the TC core. By contrast, combining Cd or Ck uncertainty with any other source leads to negligible increases in standard deviation, which is mainly due to the lack of spatial correlation in the exchange coefficient perturbations. All of the ensemble experiments are deficient in ensemble standard deviation; however, the experiments with combinations of uncertainty sources generally have an ensemble standard deviation closer to the ensemble-mean errors.

Corresponding author address: Ryan Torn, University at Albany, State University of New York, ES 351, 1400 Washington Ave., Albany, NY 12222. E-mail: rtorn@albany.edu
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  • Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 28842903, doi:10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Anderson, J. L., 2009: Spatially and temporally varying adaptive covariance inflation for ensemble filters. Tellus, 61A, 7283, doi:10.1111/j.1600-0870.2008.00361.x.

    • Search Google Scholar
    • Export Citation
  • Anderson, J. L., T. Hoar, K. Raeder, H. Liu, N. Collins, R. Torn, and A. Arellano, 2009: The Data Assimilation Research Testbed: A community data assimilation facility. Bull. Amer. Meteor. Soc., 90, 12831296, doi:10.1175/2009BAMS2618.1.

    • Search Google Scholar
    • Export Citation
  • Andreas, E. L, L. Mahrt, and D. Vickers, 2012: A new drag relation for aerodynamically rough flow over the ocean. J. Atmos. Sci., 69, 25202537, doi:10.1175/JAS-D-11-0312.1.

    • Search Google Scholar
    • Export Citation
  • Barker, D. M., W. Huang, Y. R. Guo, A. J. Bourgeois, and Q. N. Xiao, 2004: A three-dimensional variational data assimilation system for MM5: Implementation and initial results. Mon. Wea. Rev., 132, 897914, doi:10.1175/1520-0493(2004)132<0897:ATVDAS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bell, M. M., M. T. Montgomery, and K. E. Emanuel, 2012: Air–sea enthalpy and momentum exchange at major hurricane wind speeds observed during CBLAST. J. Atmos. Sci., 69, 31973222, doi:10.1175/JAS-D-11-0276.1.

    • Search Google Scholar
    • Export Citation
  • Beven, J. L., II, and E. S. Blake, 2015: Atlantic hurricane season of 2010. Mon. Wea. Rev., 143, 33293353, doi:10.1175/MWR-D-11-00264.1.

    • Search Google Scholar
    • Export Citation
  • Bleck, R., 2002: An oceanic general circulation model framed in hybrid isopycnic-Cartesian coordinates. Ocean Modell., 4, 5588, doi:10.1016/S1463-5003(01)00012-9.

    • Search Google Scholar
    • Export Citation
  • Brown, B. R., and G. J. Hakim, 2013: Variability and predictability of a three-dimensional hurricane in statistical equilibrium. J. Atmos. Sci., 70, 18061820, doi:10.1175/JAS-D-12-0112.1.

    • Search Google Scholar
    • Export Citation
  • Buizza, R., M. Milleer, and T. N. Palmer, 1999: Stochastic representation of model uncertainties in the ECMWF Ensemble Prediction System. Quart. J. Roy. Meteor. Soc., 125, 28872908, doi:10.1002/qj.49712556006.

    • Search Google Scholar
    • Export Citation
  • Cavallo, S. M., R. D. Torn, C. Snyder, C. Davis, W. Wang, and J. Done, 2013: Evaluation of the Advanced Hurricane WRF data assimilation system for the 2009 Atlantic hurricane season. Mon. Wea. Rev., 141, 523541, doi:10.1175/MWR-D-12-00139.1.

    • Search Google Scholar
    • Export Citation
  • Davis, C., and Coauthors, 2008: Prediction of landfalling hurricanes with the Advanced Hurricane WRF Model. Mon. Wea. Rev., 136, 19902005, doi:10.1175/2007MWR2085.1.

    • Search Google Scholar
    • Export Citation
  • Davis, C., W. Wang, J. Dudhia, and R. Torn, 2010: Does increased horizontal resolution improve hurricane wind forecasts. Wea. Forecasting, 25, 18261841, doi:10.1175/2010WAF2222423.1.

    • Search Google Scholar
    • Export Citation
  • DeMaria, M., 2009: A simplified dynamical system for tropical cyclone intensity prediction. Mon. Wea. Rev., 137, 6882, doi:10.1175/2008MWR2513.1.

    • Search Google Scholar
    • Export Citation
  • Donelan, M. A., B. K. Haus, N. Reul, W. J. Plant, M. Stianssnie, H. C. Graber, O. B. Brown, and E. S. Saltzman, 2004: On the limiting aerodynamic roughness of the ocean in very strong winds. Geophys. Res. Lett., 31, L18306, doi:10.1029/2004GL019460.

    • Search Google Scholar
    • Export Citation
  • Drennan, W. M., J. A. Zhang, J. R. French, C. McCormick, and P. G. Black, 2007: Turbulent fluxes in the hurricane boundary layer. Part II: Latent heat flux. J. Atmos. Sci., 64, 11031115, doi:10.1175/JAS3889.1.

    • Search Google Scholar
    • Export Citation
  • Dupont, T., M. Plu, P. Caroff, and G. Faure, 2011: Verification of ensemble-based uncertainty circles around tropical cyclone track forecasts. Wea. Forecasting, 26, 664676, doi:10.1175/WAF-D-11-00007.1.

    • Search Google Scholar
    • Export Citation
  • Ek, M. B., K. E. Mitchell, Y. Lin, E. Rodgers, P. Grunmann, V. Koren, G. Gayno, and J. D. Tarpley, 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res., 108, 8851, doi:10.1029/2002JD003296.

    • Search Google Scholar
    • Export Citation
  • Elsberry, R. L., M. S. Jordan, and F. Vitart, 2010: Predictability of tropical cyclone events on intraseasonal timescales with the ECMWF monthly forecast model. Asia-Pac. J. Atmos. Sci., 46, 135153, doi:10.1007/s13143-010-0013-4.

    • Search Google Scholar
    • Export Citation
  • Emanuel, K., 1986: An air–sea interaction theory for tropical cyclones. Part I: Steady-state maintenance. J. Atmos. Sci., 43, 585604, doi:10.1175/1520-0469(1986)043<0585:AASITF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Emanuel, K., C. DesAutels, C. Holloway, and R. Korty, 2004: Environmental control of tropical cyclone intensity. J. Atmos. Sci., 61, 843858, doi:10.1175/1520-0469(2004)061<0843:ECOTCI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • French, J. R., W. M. Drennan, J. A. Zhang, and P. G. Black, 2007: Turbulent fluxes in the hurricane boundary layer. Part I: Momentum flux. J. Atmos. Sci., 64, 10891102, doi:10.1175/JAS3887.1.

    • Search Google Scholar
    • Export Citation
  • Gall, R., J. Franklin, F. Marks, E. N. Rappaport, and F. Toepfer, 2013: The Hurricane Forecast Improvement Project. Bull. Amer. Meteor. Soc., 94, 329343, doi:10.1175/BAMS-D-12-00071.1.

    • Search Google Scholar
    • Export Citation
  • Gaspari, G., and S. E. Cohn, 1999: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc., 125, 723757, doi:10.1002/qj.49712555417.

    • Search Google Scholar
    • Export Citation
  • Gemmill, W., B. Katz, and X. Li, 2007: Daily real-time, global sea surface temperature—High-resolution analysis: RTG_SST_HR. NCEP/EMC Tech. Note 260, NOAA/NWS/NCEP/EMC/MMAB, Science Application International Corporation, and Joint Center for Satellite Data Assimilation, 39 pp. [Available online at http://polar.ncep.noaa.gov/mmab/papers/tn260/MMAB260.pdf.]

  • Godinez, H. C., J. M. Reisner, A. O. Fierro, S. R. Giumond, and J. Kao, 2012: Determining key model parameters of rapidly intensifying Hurricane Guillermo (1997) using the ensemble Kalman filter. J. Atmos. Sci., 69, 31473171, doi:10.1175/JAS-D-12-022.1.

    • Search Google Scholar
    • Export Citation
  • Green, B. W., and F. Zhang, 2013: Impacts of air–sea flux parameterizations on the intensity and structure of tropical cyclones. Mon. Wea. Rev., 141, 23082324, doi:10.1175/MWR-D-12-00274.1.

    • Search Google Scholar
    • Export Citation
  • Green, B. W., and F. Zhang, 2014: Sensitivity of tropical cyclone simulations to parametric uncertainties in air–sea fluxes and implications for parameter estimation. Mon. Wea. Rev., 142, 22902308, doi:10.1175/MWR-D-13-00208.1.

    • Search Google Scholar
    • Export Citation
  • Hakim, G. J., 2013: The variability and predictability of axisymmetric hurricanes in statistical equilibrium. J. Atmos. Sci., 70, 9931005, doi:10.1175/JAS-D-12-0188.1.

    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., J. S. Whitaker, M. Fiorino, and S. J. Benjamin, 2011: Global ensemble predictions of 2009’s tropical cyclones initialized with an ensemble Kalman filter. Mon. Wea. Rev., 139, 668688, doi:10.1175/2010MWR3456.1.

    • Search Google Scholar
    • Export Citation
  • Hong, S.-Y., J. Dudhia, and S. H. Chen, 2004: A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Wea. Rev., 132, 103120, doi:10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 23182341, doi:10.1175/MWR3199.1.

    • Search Google Scholar
    • Export Citation
  • Houtekamer, P. L., H. L. Mitchell, G. Pellerin, M. Buehner, M. Charron, L. Spacek, and B. Hansen, 2005: Atmospheric data assimilation with an ensemble Kalman filter: Results with real observations. Mon. Wea. Rev., 133, 604620, doi:10.1175/MWR-2864.1.

    • Search Google Scholar
    • Export Citation
  • Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103, doi:10.1029/2008JD009944.

    • Search Google Scholar
    • Export Citation
  • Judt, F., S. S. Chen, and J. Berner, 2016: Predictability of tropical cyclone intensity: Scale-dependent forecast error growth in high-resolution stochastic kinetic-energy backscatter ensembles. Quart. J. Roy. Meteor. Soc., 142, 4357, doi:10.1002/qj.2626.

    • Search Google Scholar
    • Export Citation
  • Kaplan, J., and M. DeMaria, 2003: Large-scale characteristics of rapidly intensifying tropical cyclones in the North Atlantic basin. Wea. Forecasting, 18, 10931108, doi:10.1175/1520-0434(2003)018<1093:LCORIT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Knaff, J. A., M. DeMaria, B. Sampson, and J. M. Gross, 2003: Statistical, 5-day tropical cyclone intensity forecasts derived from climatology and persistence. Wea. Forecasting, 18, 8092, doi:10.1175/1520-0434(2003)018<0080:SDTCIF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Komaromi, W. A., and S. J. Majumdar, 2014: Ensemble-based error and predictability metrics associated with tropical cyclogenesis. Part I: Basinwide perspective. Mon. Wea. Rev., 142, 28792898, doi:10.1175/MWR-D-13-00370.1.

    • Search Google Scholar
    • Export Citation
  • Komaromi, W. A., and S. J. Majumdar, 2015: Ensemble-based error and predictability metrics associated with tropical cyclogenesis. Part II: Wave-relative framework. Mon. Wea. Rev., 143, 16651686, doi:10.1175/MWR-D-14-00286.1.

    • Search Google Scholar
    • Export Citation
  • Kunii, M., and T. Miyoshi, 2012: Including uncertainties of sea surface temperature in an ensemble Kalman filter: A case study of Typhoon Sinlaku (2008). Wea. Forecasting, 27, 15861597, doi:10.1175/WAF-D-11-00136.1.

    • Search Google Scholar
    • Export Citation
  • Landsea, C. W., and J. L. Franklin, 2013: Atlantic hurricane database uncertainty and presentation of a new database format. Mon. Wea. Rev., 141, 35763592, doi:10.1175/MWR-D-12-00254.1.

    • Search Google Scholar
    • Export Citation
  • Lang, S. T. K., M. Leutbecher, and S. C. Jones, 2012: Impact of perturbation methods in the ECMWF ensemble prediction system on tropical cyclone forecasts. Quart. J. Roy. Meteor. Soc., 138, 20302046, doi:10.1002/qj.1942.

    • Search Google Scholar
    • Export Citation
  • Majumdar, S. J., and P. M. Finocchio, 2010: On the ability of global ensemble prediction systems to predict tropical cyclone track probabilities. Wea. Forecasting, 25, 659680, doi:10.1175/2009WAF2222327.1.

    • Search Google Scholar
    • Export Citation
  • Majumdar, S. J., and R. D. Torn, 2014: Probabilistic verification of global and mesoscale ensemble forecasts of tropical cyclogenesis. Wea. Forecasting, 29, 11811198, doi:10.1175/WAF-D-14-00028.1.

    • Search Google Scholar
    • Export Citation
  • McLay, J. G., C. H. Bishop, and C. A. Reynolds, 2008: Evaluation of the ensemble transform analysis perturbation scheme at NRL. Mon. Wea. Rev., 136, 10931108, doi:10.1175/2007MWR2010.1.

    • Search Google Scholar
    • Export Citation
  • Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the long-wave. J. Geophys. Res., 102, 16 66316 682, doi:10.1029/97JD00237.

    • Search Google Scholar
    • Export Citation
  • Munsell, E. B., F. Zhang, and D. P. Stern, 2013: Predictability and dynamics of a nonintensifying tropical storm: Erika (2009). J. Atmos. Sci., 70, 25052524, doi:10.1175/JAS-D-12-0243.1.

    • Search Google Scholar
    • Export Citation
  • Murphy, J. M., 1988: The impact of ensemble forecasts on predictability. Quart. J. Roy. Meteor. Soc., 114, 463493, doi:10.1002/qj.49711448010.

    • Search Google Scholar
    • Export Citation
  • Pollard, R. T., P. B. Rhines, and R. O. R. Y. Thompson, 1972: The deepening of the wind-mixed layer. Geophys. Fluid Dyn., 4, 381404, doi:10.1080/03091927208236105.

    • Search Google Scholar
    • Export Citation
  • Poterjoy, J., and F. Zhang, 2014: Predictability and genesis of Hurricane Karl (2010) examined through the EnKF assimilation of field observations collected during PREDICT. J. Atmos. Sci., 71, 12601275, doi:10.1175/JAS-D-13-0291.1.

    • Search Google Scholar
    • Export Citation
  • Powell, M. D., P. J. Vickery, and T. A. Reinhold, 2003: Reduced drag coefficient for high wind speeds in tropical cyclones. Nature, 422, 279283, doi:10.1038/nature01481.

    • Search Google Scholar
    • Export Citation
  • Qian, C., F. Zhang, B. W. Green, J. Zhang, and X. Zhou, 2013: Probabilistic evaluation of the dynamics and prediction of Supertyphoon Megi (2010). Wea. Forecasting, 28, 15621577, doi:10.1175/WAF-D-12-00121.1.

    • Search Google Scholar
    • Export Citation
  • Reynolds, C. A., J. G. McLay, E. A. Goerss, J. S. Serra, D. Hodyss, and C. R. Sampson, 2011a: Impact of resolution and design on the U.S. Navy Global Ensemble performance in the tropics. Mon. Wea. Rev., 139, 21452155, doi:10.1175/2011MWR3546.1.

    • Search Google Scholar
    • Export Citation
  • Reynolds, C. A., J. A. Ridout, and J. G. McLay, 2011b: Examination of parameter variations in the U. S. Navy Global Ensemble. Tellus, 63A, 841857, doi:10.1111/j.1600-0870.2011.00532.x.

    • Search Google Scholar
    • Export Citation
  • Reynolds, R. W., and D. B. Chelton, 2010: Comparisons of daily sea surface temperature analyses for 2007–08. J. Climate, 23, 35453562, doi:10.1175/2010JCLI3294.1.

    • Search Google Scholar
    • Export Citation
  • Rios-Berrios, R., R. D. Torn, and C. A. Davis, 2016a: An ensemble approach to investigate tropical cyclone intensification in sheared environments. Part I: Katia (2011). J. Atmos. Sci., 73, 7193, doi:10.1175/JAS-D-15-0052.1.

    • Search Google Scholar
    • Export Citation
  • Rios-Berrios, R., R. D. Torn, and C. A. Davis, 2016b: An ensemble approach to investigate tropical cyclone intensification in sheared environments. Part II: Ophelia (2011). J. Atmos. Sci., 73, 15551575, doi:10.1175/JAS-D-15-0245.1.

    • Search Google Scholar
    • Export Citation
  • Sippel, J. A., and F. Zhang, 2008: A probabilistic analysis of the dynamics and predictability of tropical cyclogenesis. J. Atmos. Sci., 65, 34403459, doi:10.1175/2008JAS2597.1.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and M. L. Weisman, 2009: The impact of positive-definite moisture transport on NWP precipitation forecasts. Mon. Wea. Rev., 137, 488494, doi:10.1175/2008MWR2583.1.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, and J. G. Powers, 2005: A description of the Advanced Research WRF version 2. NCAR Tech. Note NCAR/TN-468+STR, 88 pp., doi:10.5065/D6DZ069T.

  • Snyder, A. D., Z. Pu, and Y. Zhu, 2010: Tracking and verification of east Atlantic tropical cyclone genesis in the NCEP global ensemble: Case studies during the NASA African Monsoon Multidisciplinary Analyses. Wea. Forecasting, 25, 13971411, doi:10.1175/2010WAF2222332.1.

    • Search Google Scholar
    • Export Citation
  • 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. J. Adv. Model. Earth Syst., 6, 384404, doi:10.1002/2014MS000314.

    • Search Google Scholar
    • Export Citation
  • Tiedtke, M., 1989: A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon. Wea. Rev., 117, 17791800, doi:10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Torn, R. D., 2010: Performance of a mesoscale ensemble Kalman filter (EnKF) during the NOAA High-Resolution Hurricane Test. Mon. Wea. Rev., 138, 43754392, doi:10.1175/2010MWR3361.1.

    • Search Google Scholar
    • Export Citation
  • Torn, R. D., and C. A. Davis, 2012: The influence of shallow convection on tropical cyclone track forecasts. Mon. Wea. Rev., 140, 21882197, doi:10.1175/MWR-D-11-00246.1.

    • Search Google Scholar
    • Export Citation
  • Torn, R. D., and C. Snyder, 2012: Uncertainty of tropical cyclone best track information. Wea. Forecasting, 27, 715729, doi:10.1175/WAF-D-11-00085.1.

    • Search Google Scholar
    • Export Citation
  • Torn, R. D., G. J. Hakim, and C. Snyder, 2006: Boundary conditions for limited-area ensemble Kalman filters. Mon. Wea. Rev., 134, 24902502, doi:10.1175/MWR3187.1.

    • Search Google Scholar
    • Export Citation
  • Vukicevic, T., E. Uhlhorn, P. Reasor, and B. Klotz, 2014: A novel multiscale intensity metric for evaluation of tropical cyclone intensity forecasts. J. Atmos. Sci., 71, 12921304, doi:10.1175/JAS-D-13-0153.1.

    • Search Google Scholar
    • Export Citation
  • Wang, Y., and C.-C. Wu, 2004: Current understanding of tropical cyclone structure and intensity changes—A review. Meteor. Atmos. Phys., 87, 257278, doi:10.1007/s00703-003-0055-6.

    • Search Google Scholar
    • Export Citation
  • Wu, T.-C., H. Liu, S. J. Majumdar, C. S. Velden, and J. L. Anderson, 2014: Influence of assimilating satellite-derived atmospheric motion vector observations on numerical analyses and forecasts of tropical cyclone track and intensity. Mon. Wea. Rev., 142, 4971, doi:10.1175/MWR-D-13-00023.1.

    • Search Google Scholar
    • Export Citation
  • Yamaguchi, M., and S. J. Majumdar, 2010: Using TIGGE data to diagnose initial perturbations and their growth for tropical cyclone ensemble forecasts. Mon. Wea. Rev., 138, 36343655, doi:10.1175/2010MWR3176.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, C., Y. Wang, and K. Hamilton, 2011: Improved representation of boundary layer clouds over the Southeast Pacific in WRF-ARW using a modified Tiedtke cumulus parameterization scheme. Mon. Wea. Rev., 139, 34893513, doi:10.1175/MWR-D-10-05091.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, F., and J. A. Sippel, 2009: Effects of moist convection on hurricane predictability. J. Atmos. Sci., 66, 19441961, doi:10.1175/2009JAS2824.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, F., and D. Tao, 2013: Effects of vertical wind shear on the predictability of tropical cyclones. J. Atmos. Sci., 70, 975983, doi:10.1175/JAS-D-12-0133.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, J. A., W. M. Drennan, P. G. Black, and R. French, 2009: Turbulence structure of the hurricane boundary layer between the outer rainbands. J. Atmos. Sci., 66, 24552467, doi:10.1175/2009JAS2954.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, Z., V. Tallapragada, C. Kieu, S. Trahan, and W. Wang, 2014: HWRF based ensemble prediction system using perturbations from GEFS and stochastic convective trigger function. Trop. Cyclone Res. Rev., 3, 145161.

    • Search Google Scholar
    • Export Citation
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