• Braun, S. A., and W.-K. Tao, 2000: Sensitivity of high-resolution simulations of Hurricane Bob (1991) to planetary boundary layer parameterizations. Mon. Wea. Rev., 128, 39413961.

    • Search Google Scholar
    • Export Citation
  • Bryan, G. H., and R. Rotunno, 2009: The maximum intensity of tropical cyclones in axisymmetric numerical model simulations. Mon. Wea. Rev., 137, 17701789.

    • Search Google Scholar
    • Export Citation
  • Chang, S. W., 1981: The impact of satellite-sensed winds on intensity forecasts of tropical cyclones. Mon. Wea. Rev., 109, 539553.

  • Charney, J. G., and A. Eliassen, 1964: On the growth of the hurricane depression. J. Atmos. Sci., 21, 6875.

  • Emanuel, K. A., 1986: An air–sea interaction theory for tropical cyclones. Part I: Steady-state maintenance. J. Atmos. Sci., 43, 585605.

    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 1995: Sensitivity of tropical cyclones to surface exchange coefficients and a revised steady-state model incorporating eye dynamics. J. Atmos. Sci., 52, 39693976.

    • Search Google Scholar
    • Export Citation
  • Godinez, H. C., J. M. Reisner, A. O. Fierro, S. R. Guimond, and J. Kao, 2012: Determining key model parameters of rapidly intensifying Hurricane Guillermo (1997) using the ensemble Kalman filter. J. Atmos. Sci., 69, 31473171.

    • Search Google Scholar
    • Export Citation
  • Guimond, S. R., M. A. Bourassa, and P. D. Reasor, 2011: A latent heat retrieval and its effects on the intensity and structure change of Hurricane Guillermo (1997). Part I: The algorithm and observations. J. Atmos. Sci., 68, 15491567.

    • Search Google Scholar
    • Export Citation
  • Hanley, D. E., J. Molinari, and D. Keyser, 2001: A composite study of the interactions between tropical cyclones and upper-tropospheric troughs. Mon. Wea. Rev., 129, 25702584.

    • Search Google Scholar
    • Export Citation
  • Heymsfield, G., J. Carswell, L. Li, D. Schaubert, and J. Creticos, 2007: Development of the High-Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP). Extended Abstracts, NASA Science Technology Conf. 2007 (NSTC2007), College Park, MD, NASA, B5P2. [Available online at http://esto.nasa.gov/conferences/nstc2007/papers/Carswell_James_B5P2_NSTC-07-0085.pdf.]

  • Hoke, J. E., and R. A. Anthes, 1976: The initialization of numerical models by a dynamic- initialization technique. Mon. Wea. Rev., 104, 15511556.

    • Search Google Scholar
    • Export Citation
  • Krishnamurti, T. N., W. Han, B. Jha, and H. S. Bedi, 1998: Numerical prediction of Hurricane Opal. Mon. Wea. Rev., 126, 13471363.

  • Kurihara, Y., M. A. Bender, R. E. Tuleya, and R. J. Ross, 1990: Prediction experiments of Hurricane Gloria (1985) using a multiply nested movable mesh model. Mon. Wea. Rev., 118, 21852198.

    • Search Google Scholar
    • Export Citation
  • Leonard, B. P., and J. E. Drummond, 1995: Why you should not use ‘hybrid’, ‘power-law’ or related exponential schemes for convective modeling—There are better alternatives. Int. J. Numer. Methods Fluids, 20, 421442.

    • Search Google Scholar
    • Export Citation
  • Mayfield, M., cited 2011: Preliminary report: Hurricane Guillermo 30 July - 15 August 1997. National Hurricane Center, 7 pp. [Available online http://www.nhc.noaa.gov/1997guillerm.html.]

  • McFarquhar, G. M., H. Zhang, G. Heymsfield, R. Hood, J. Dudhia, J. B. Halverson, and F. Marks Jr., 2006: Factors affecting the evolution of Hurricane Erin (2001) and the distributions of hydrometeors: Role of microphysical processes. J. Atmos. Sci., 63, 127150.

    • Search Google Scholar
    • Export Citation
  • Navon, I. M., X. Zou, J. Derber, and J. Sela, 1992: Variational data assimilation with an adiabatic version of the NMC spectral model. Mon. Wea. Rev., 120, 14331446.

    • Search Google Scholar
    • Export Citation
  • Pattnaik, S., and T. N. Krishnamurti, 2007: Impact of cloud microphysical processes on hurricane intensity, part 2: Sensitivity experiments. Meteor. Atmos. Phys., 97, 127147.

    • Search Google Scholar
    • Export Citation
  • Reasor, P. D., M. D. Eastin, and J. F. Gamache, 2009: Rapidly intensifying Hurricane Guillermo (1997). Part I: Low-wavenumber structure and evolution. Mon. Wea. Rev., 137, 603631.

    • Search Google Scholar
    • Export Citation
  • Reisner, J., and C. A. Jeffery, 2009: A smooth cloud model. Mon. Wea. Rev., 137, 18251843.

  • Reisner, J., R. Bruintjes, and R. Rasmussen, 1998: An examination on the utility of forecasting supercooled liquid water in a mesoscale model. Quart. J. Roy. Meteor. Soc., 124, 10711107.

    • Search Google Scholar
    • Export Citation
  • Reisner, J., V. A. Mousseau, A. A. Wyszogrodzki, and D. A. Knoll, 2005: An implicitly balanced hurricane model with physics-based preconditioning. Mon. Wea. Rev., 133, 10031022.

    • Search Google Scholar
    • Export Citation
  • Reynolds, R. W., T. M. Smith, C. Liu, D. B. Chelton, K. S. Casey, and M. G. Schlax, 2007: Daily high-resolution-blended analyses for sea surface temperature. J. Climate, 20, 54735496.

    • Search Google Scholar
    • Export Citation
  • Rogers, R. F., M. L. Black, S. S. Chen, and R. A. Black, 2007: An evaluation of microphysics fields from mesoscale model simulations of tropical cyclones. Part I: Comparisons with observations. J. Atmos. Sci., 64, 18111834.

    • Search Google Scholar
    • Export Citation
  • Sitkowski, M., and G. M. Barnes, 2009: Low-level thermodynamic, kinematic, and reflectivity fields of Hurricane Guillermo (1997) during rapid intensification. Mon. Wea. Rev., 137, 645663.

    • Search Google Scholar
    • Export Citation
  • Tong, H., V. Chandrasekar, K. R. Knupp, and J. Stalker, 1998: Multiparameter radar observations of time evolution of convective storms: Evaluation of water budgets and latent heating rates. J. Atmos. Oceanic Technol., 15, 10971109.

    • Search Google Scholar
    • Export Citation
  • Tory, K. J., M. T. Montgomery, and N. E. Davidson, 2006: Prediction and diagnosis of tropical cyclone formation in an NWP system. Part I: The critical role of vortex enhancement in deep convection. J. Atmos. Sci., 63, 30773090.

    • Search Google Scholar
    • Export Citation
  • Zalesak, S., 1979: Fully multidimensional flux-corrected transport algorithm for fluids. J. Comput. Phys., 31, 335362.

  • Zhang, D.-L., Y. Liu, and M. K. Yau, 2002: A multiscale numerical study of Hurricane Andrew (1992). Part V: Inner-core thermodynamics. Mon. Wea. Rev., 130, 27452763.

    • Search Google Scholar
    • Export Citation
  • Zou, X., I. M. Navon, and F. X. Ledimet, 1992: An optimal nudging data assimilation scheme using parameter estimation. Quart. J. Roy. Meteor. Soc., 118, 11631186.

    • Search Google Scholar
    • Export Citation
  • Zou, X., Y. Wu, and P. S. Ray, 2010: Verification of a high-resolution model forecast using airborne Doppler radar analysis during the rapid intensification of Hurricane Guillermo. J. Appl. Meteor. Climatol., 49, 807820.

    • Search Google Scholar
    • Export Citation
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A Latent Heat Retrieval and Its Effects on the Intensity and Structure Change of Hurricane Guillermo (1997). Part II: Numerical Simulations

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  • 1 Center for Ocean-Atmospheric Prediction Studies, and Department of Earth, Ocean and Atmospheric Science, The Florida State University, Tallahassee, Florida
  • | 2 Los Alamos National Laboratory, Los Alamos, New Mexico
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Abstract

In Part I of this study, a new algorithm for retrieving the latent heat field in tropical cyclones from airborne Doppler radar was presented and fields from rapidly intensifying Hurricane Guillermo (1997) were shown. In Part II, the usefulness and relative accuracy of the retrievals is assessed by inserting the heating into realistic numerical simulations at 2-km resolution and comparing the generated wind structure to the radar analyses of Guillermo. Results show that using the latent heat retrievals as forcing produces very low intensity and structure errors (in terms of tangential wind speed errors and explained wind variance) and significantly improves simulations relative to a predictive run that is highly calibrated to the latent heat retrievals by using an ensemble Kalman filter procedure to estimate values of key model parameters.

Releasing all the heating/cooling in the latent heat retrieval results in a simulation with a large positive bias in Guillermo’s intensity that motivates the need to determine the saturation state in the hurricane inner-core retrieval through a procedure similar to that described in Part I of this study. The heating retrievals accomplish high-quality structure statistics by forcing asymmetries in the wind field with the generally correct amplitude, placement, and timing. In contrast, the latent heating fields generated in the predictive simulation contain a significant bias toward large values and are concentrated in bands (rather than discrete cells) stretched around the vortex. The Doppler radar–based latent heat retrievals presented in this series of papers should prove useful for convection initialization and data assimilation to reduce errors in numerical simulations of tropical cyclones.

Current affiliation: NASA Goddard Space Flight Center, Greenbelt, Maryland.

Corresponding author address: Stephen R. Guimond, NASA Goddard Space Flight Center, Code 612, Greenbelt, MD 20771. E-mail: stephen.guimond@nasa.gov

Abstract

In Part I of this study, a new algorithm for retrieving the latent heat field in tropical cyclones from airborne Doppler radar was presented and fields from rapidly intensifying Hurricane Guillermo (1997) were shown. In Part II, the usefulness and relative accuracy of the retrievals is assessed by inserting the heating into realistic numerical simulations at 2-km resolution and comparing the generated wind structure to the radar analyses of Guillermo. Results show that using the latent heat retrievals as forcing produces very low intensity and structure errors (in terms of tangential wind speed errors and explained wind variance) and significantly improves simulations relative to a predictive run that is highly calibrated to the latent heat retrievals by using an ensemble Kalman filter procedure to estimate values of key model parameters.

Releasing all the heating/cooling in the latent heat retrieval results in a simulation with a large positive bias in Guillermo’s intensity that motivates the need to determine the saturation state in the hurricane inner-core retrieval through a procedure similar to that described in Part I of this study. The heating retrievals accomplish high-quality structure statistics by forcing asymmetries in the wind field with the generally correct amplitude, placement, and timing. In contrast, the latent heating fields generated in the predictive simulation contain a significant bias toward large values and are concentrated in bands (rather than discrete cells) stretched around the vortex. The Doppler radar–based latent heat retrievals presented in this series of papers should prove useful for convection initialization and data assimilation to reduce errors in numerical simulations of tropical cyclones.

Current affiliation: NASA Goddard Space Flight Center, Greenbelt, Maryland.

Corresponding author address: Stephen R. Guimond, NASA Goddard Space Flight Center, Code 612, Greenbelt, MD 20771. E-mail: stephen.guimond@nasa.gov
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