An Initialization Scheme for Tropical Cyclone Numerical Prediction by Enhancing Humidity in Deep-Convection Region

Jianyong Liu Ningbo Meteorological Observatory, Ningbo, Zhejiang, China

Search for other papers by Jianyong Liu in
Current site
Google Scholar
PubMed
Close
,
Shunan Yang National Meteorological Center, Beijing, China

Search for other papers by Shunan Yang in
Current site
Google Scholar
PubMed
Close
,
Leiming Ma Laboratory of Typhoon Forecast Technique, Shanghai Typhoon Institute, China Meteorological Administration, Shanghai, China

Search for other papers by Leiming Ma in
Current site
Google Scholar
PubMed
Close
,
Xuwei Bao Laboratory of Typhoon Forecast Technique, Shanghai Typhoon Institute, China Meteorological Administration, Shanghai, China

Search for other papers by Xuwei Bao in
Current site
Google Scholar
PubMed
Close
,
Dongliang Wang Laboratory of Typhoon Forecast Technique, Shanghai Typhoon Institute, China Meteorological Administration, Shanghai, China

Search for other papers by Dongliang Wang in
Current site
Google Scholar
PubMed
Close
, and
Difeng Xu Ningbo Meteorological Observatory, Ningbo, Zhejiang, China

Search for other papers by Difeng Xu in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

A nudging scheme for humidity fields is implemented in the Advanced Hurricane Weather Research and Forecasting model (WRF) for tropical cyclone (TC) initialization. The scheme improves TC simulation by enhancing the TC humidity profile in deep-convection regions, where it uses satellite Fengyun 2 cloud-top brightness temperatures as a judging criterion. The impacts of the nudging on predicting TC intensity and structure are evaluated through the simulation of TC Khanun (2005) during its movement toward landfall at the coast of Zhejiang Province, China. During the nudging, the humidity distributions at the TC's inner core and along its outer spiral rainbands, where deep convections occur, are both enhanced. As a result, the intensity of the vortex is enhanced, being more consistent to the best-track data from the China Meteorological Administration. Specifically, the nudging modifies the simulated distribution of humidity according to convective activities captured by the satellite and therefore adjusts the development of deep convection in the model, which then influences the intensity and size of TC vortex through diabatic heating. During WRF simulation, the TC vortex initialized from the humidity nudging is dynamically and thermodynamically balanced with the background field, favoring a steady development of the vortex's intensity and structure. Because of the better simulation of TC inner core and outer spiral rainbands, the WRF simulation skills of TC intensity and track are improved.

Denotes Open Access content.

Corresponding author address: Dr. Jianyong Liu, Ningbo Meteorological Observatory, 118 Qixiang Rd., Haishu District, Ningbo 315012, China. E-mail: jianyong.liu@gmail.com

Abstract

A nudging scheme for humidity fields is implemented in the Advanced Hurricane Weather Research and Forecasting model (WRF) for tropical cyclone (TC) initialization. The scheme improves TC simulation by enhancing the TC humidity profile in deep-convection regions, where it uses satellite Fengyun 2 cloud-top brightness temperatures as a judging criterion. The impacts of the nudging on predicting TC intensity and structure are evaluated through the simulation of TC Khanun (2005) during its movement toward landfall at the coast of Zhejiang Province, China. During the nudging, the humidity distributions at the TC's inner core and along its outer spiral rainbands, where deep convections occur, are both enhanced. As a result, the intensity of the vortex is enhanced, being more consistent to the best-track data from the China Meteorological Administration. Specifically, the nudging modifies the simulated distribution of humidity according to convective activities captured by the satellite and therefore adjusts the development of deep convection in the model, which then influences the intensity and size of TC vortex through diabatic heating. During WRF simulation, the TC vortex initialized from the humidity nudging is dynamically and thermodynamically balanced with the background field, favoring a steady development of the vortex's intensity and structure. Because of the better simulation of TC inner core and outer spiral rainbands, the WRF simulation skills of TC intensity and track are improved.

Denotes Open Access content.

Corresponding author address: Dr. Jianyong Liu, Ningbo Meteorological Observatory, 118 Qixiang Rd., Haishu District, Ningbo 315012, China. E-mail: jianyong.liu@gmail.com
Save
  • Chen, Y., and C. Snyder, 2007: Assimilating vortex position with an ensemble Kalman filter. Mon. Wea. Rev., 135, 18281845.

  • Chu, K., Q. Xiao, Z. Tan, and J. Gu, 2011: A forecast sensitivity study on the intensity change of Typhoon Sinlaku (2008). J. Geophys. Res., 116, D22109, doi:10.1029/2011JD016127.

    • Search Google Scholar
    • Export Citation
  • Davidson, N. E., and K. Puri, 1992: Tropical prediction using dynamical nudging, satellite-defined convective heat sources, and a cyclone bogus. Mon. Wea. Rev., 120, 25012522.

    • Search Google Scholar
    • Export Citation
  • Davis, C., and Coauthors, 2008: Prediction of landfalling hurricanes with the advanced hurricane WRF. Mon. Wea. Rev., 136, 19902005.

  • Davolio, S., and A. Buzzi, 2004: A nudging scheme for the assimilation of precipitation data into a mesoscale model. Wea. Forecasting, 19, 855871.

    • Search Google Scholar
    • Export Citation
  • Donelan, M. A., B. K. Haus, N. Reul, W. J. Plant, M. Stiassnie, 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
  • Ebert, E. E., J. E. Janowiak, and C. Kidd, 2007: Comparison of near-real-time precipitation estimates from satellite observations and numerical models. Bull. Amer. Meteor. Soc., 88, 4764.

    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 1986: An air–sea interaction theory for tropical cyclones. Part I: Steady-state maintenance. J. Atmos. Sci., 43, 585604.

    • Search Google Scholar
    • Export Citation
  • Hack, J. J., and W. H. Schubert, 1986: Nonlinear response of atmospheric vortices to heating by organized cumulus convection. J. Atmos. Sci., 43, 15591573.

    • Search Google Scholar
    • Export Citation
  • Hendon, H. H., and K. Woodberry, 1993: The diurnal cycle of tropical convection. J. Geophys. Res., 98 (D9), 623637.

  • Hendricks, E. A., M. T. Montgomery, and C. A. Davis, 2004: On the role of “vortical” hot towers in formation of tropical cyclone Diana (1984). J. Atmos. Sci., 61, 12091232.

    • Search Google Scholar
    • Export Citation
  • Hill, K. A., and G. M. Lackmann, 2009: Influence of environmental humidity on tropical cyclone size. Mon. Wea. Rev., 137, 32943315.

  • 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.

    • Search Google Scholar
    • Export Citation
  • Kain, J. S., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170181.

  • Kain, J. S., and J. M. Fritsch, 1990: A one-dimensional entraining/detraining plume model and its application in convective parameterization. J. Atmos. Sci., 47, 27842802.

    • Search Google Scholar
    • Export Citation
  • Kain, J. S., and J. M. Fritsch, 1993: Convective parameterization for mesoscale models: The Kain–Fritcsh scheme. The Representation of Cumulus Convection in Numerical Models, Meteor. Monogr., No. 46, Amer. Meteor. Soc., 165–170.

  • Krishnamurti, T. N., H. S. Bedi, and K. Ingles, 1993: Physical initialization using SSM/I rain rates. Tellus, 45A, 247269.

  • Krishnamurti, T. N., S. K. Roy Bhowmik, D. Oosterhof, and G. Rohaly, 1995: Mesoscale signatures within the tropics generated by physical initialization. Mon. Wea. Rev., 123, 27712790.

    • Search Google Scholar
    • Export Citation
  • Krishnamurti, T. N., R. Correa-Torres, G. Rohaly, and D. Oosterhof, 1997: Physical initialization and hurricane ensemble forecasts. Wea. Forecasting, 12, 503514.

    • 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., and R. J. Ross, 1993: An initialization scheme of hurricane models by vortex specification. Mon. Wea. Rev., 121, 20302045.

    • Search Google Scholar
    • Export Citation
  • Kurihara, Y., M. A. Bender, R. E. Tuleya, and R. J. Ross, 1995: Improvements in the GFDL hurricane prediction system. Mon. Wea. Rev., 123, 27912801.

    • Search Google Scholar
    • Export Citation
  • Liang, X., B. Wang, J. C. L. Chan, Y. Duan, D. Wang, Z. Zeng, and L. Mab, 2007: Tropical cyclone forecasting with model-constrained 3D-Var. II: Improved cyclone track forecasting using AMSU-A, QuikSCAT and cloud-drift wind data. Quart. J. Roy. Meteor. Soc., 133, 155165.

    • Search Google Scholar
    • Export Citation
  • Liu, Y., D. L. Zhang, and M. K. Yau, 1997: A multiscale numerical study of Hurricane Andrew (1992). Part I: Explicit simulation and verification. Mon. Wea. Rev., 125, 30733093.

    • Search Google Scholar
    • Export Citation
  • Lord, S. J., 1991: A bogusing system for vortex circulations in the National Meteorological Center global forecast model. Preprints, 19th Conf. on Hurricane and Tropical Meteorology, Miami, FL, Amer. Meteor. Soc., 328–330.

  • Ma, L. M., and Z. Tan, 2010: Tropical cyclone initialization with dynamical retrieval from a modified UWPBL model. J. Meteor. Soc. Japan, 88, 827846.

    • Search Google Scholar
    • Export Citation
  • Ma, L. M., Z. Qin, Y. Duan, X. Liang, and D. Wang, 2006: Impacts of TRMM SRR assimilation on the numerical prediction of tropical cyclone. Acta Oceanol. Sin., 25 (5), 1426.

    • Search Google Scholar
    • Export Citation
  • Ma, L. M., J. C. L. Chan, N. E. Davidson, and J. Turk, 2007: Initialization with diabatic heating from satellite-derived rainfall. Atmos. Res., 85, 148158.

    • Search Google Scholar
    • Export Citation
  • Montroty, R., F. Rabier, S. Westrelin, G. Faure, and N. Viltard, 2008: Impact of wind bogus and cloud- and rain-affected SSM/I data on tropical cyclone analyses and forecasts. Quart. J. Roy. Meteor. Soc., 134, 16731699.

    • Search Google Scholar
    • Export Citation
  • Nguyen, H. V., and Y. L. Chen, 2011: High-resolution initialization and simulations of Typhoon Morakot (2009). Mon. Wea. Rev., 139, 14631491.

    • Search Google Scholar
    • Export Citation
  • Orlandi, E., F. Fierli, S. Davolio, A. Buzzi, and O. Drofa, 2010: A nudging scheme to assimilate satellite brightness temperature in a meteorological model: Impact on representation of African mesoscale convective systems. Quart. J. Roy. Meteor. Soc., 136, 462474.

    • Search Google Scholar
    • Export Citation
  • Pattnaik, S., C. Inglish, and T. N. Krishnamurti, 2011: Influence of rain-rate initialization, cloud microphysics, and cloud torques on hurricane intensity. Mon. Wea. Rev., 139, 627649.

    • Search Google Scholar
    • Export Citation
  • Rotunno, R., and K. A. Emanuel, 1987: An air–sea interaction theory for tropical cyclones. Part II: Evolutionary study using a nonhydrostatic axisymmetric numerical model. J. Atmos. Sci., 44, 542561.

    • Search Google Scholar
    • Export Citation
  • Thompson, G., P. R. Field, R. M. Rasmussen, and W. D. Hall, 2008: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon. Wea. Rev., 136, 50955115.

    • Search Google Scholar
    • Export Citation
  • Wang, Y., 2009: How do outer spiral rainbands affect tropical cyclone structure and intensity? J. Atmos. Sci., 66, 12501273.

  • Wang, Y., and G. J. Holland, 1996a: The beta drift of baroclinic vortices. Part I: Adiabatic vortices. J. Atmos. Sci., 53, 411427.

  • Wang, Y., and G. J. Holland, 1996b: The beta drift of baroclinic vortices. Part II: Diabatic vortices. J. Atmos. Sci., 53, 37373756.

  • Wu, C.-C., G.-Y. Lien, J.-H. Chen, and F. Zhang, 2010: Assimilation of tropical cyclone track and structure based on the ensemble Kalman filter (EnKF). J. Atmos. Sci., 67, 38063822.

    • Search Google Scholar
    • Export Citation
  • Xiao, Q., X. Zou, and B. Wang, 2000: Initialization and simulation of a landfalling hurricane using a variational bogus data assimilation scheme. Mon. Wea. Rev., 128, 22522269.

    • Search Google Scholar
    • Export Citation
  • Xiao, Q., Y. Y. Kuo, Y. Zhang, D. M. Barker, and D. J. Won, 2006: A tropical cyclone bogus data assimilation scheme in the MM5 3D-Var system and numerical experiments with Typhoon Rusa (2002) near landfall. J. Meteor. Soc. Japan, 84, 671689.

    • Search Google Scholar
    • Export Citation
  • Xiao, Q., L. Chen, and X. Zhang, 2009: Evaluations of BDA scheme using the Advanced Research WRF (ARW) model. J. Appl. Meteor. Climatol., 48, 680689.

    • Search Google Scholar
    • Export Citation
  • Zhang, X., Q. Xiao, and P. J. Fitzpatrick, 2007: The impact of multisatellite data on the initialization and simulation of Hurricane Lili's (2002) rapid weakening phase. Mon. Wea. Rev., 135, 526548.

    • Search Google Scholar
    • Export Citation
  • Zhao, Y., 2005: Improved track forecasting of a typhoon reaching landfall from four-dimensional variational data assimilation of AMSU-A retrieved data. J. Geophys. Res., 110, D14101, doi:10.1029/2004JD005267.

    • Search Google Scholar
    • Export Citation
  • Zou, X., and Q. Xiao, 2000: Studies on the initialization and simulation of a mature hurricane using a variational bogus data assimilation scheme. J. Atmos. Sci., 57, 836860.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 175 41 2
PDF Downloads 109 30 4