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The impact of microphysical schemes on hurricane intensity and track

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Abstract

During the past decade, both research and operational numerical weather prediction models [e.g. the Weather Research and Forecasting Model (WRF)] have started using more complex microphysical schemes originally developed for high-resolution cloud resolving models (CRMs) with 1–2 km or less horizontal resolutions. WRF is a next-generation meso-scale forecast model and assimilation system. It incorporates a modern software framework, advanced dynamics, numerics and data assimilation techniques, a multiple moveable nesting capability, and improved physical packages. WRF can be used for a wide range of applications, from idealized research to operational forecasting, with an emphasis on horizontal grid sizes in the range of 1–10 km. The current WRF includes several different microphysics options. At NASA Goddard, four different cloud microphysics options have been implemented into WRF. The performance of these schemes is compared to those of the other microphysics schemes available in WRF for an Atlantic hurricane case (Katrina). In addition, a brief review of previous modeling studies on the impact of microphysics schemes and processes on the intensity and track of hurricanes is presented and compared against the current Katrina study. In general, all of the studies show that microphysics schemes do not have a major impact on track forecasts but do have more of an effect on the simulated intensity. Also, nearly all of the previous studies found that simulated hurricanes had the strongest deepening or intensification when using only warm rain physics. This is because all of the simulated precipitating hydrometeors are large raindrops that quickly fall out near the eye-wall region, which would hydrostatically produce the lowest pressure. In addition, these studies suggested that intensities become unrealistically strong when evaporative cooling from cloud droplets and melting from ice particles are removed as this results in much weaker downdrafts in the simulated storms. However, there are many differences between the different modeling studies, which are identified and discussed.

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References

  • Chen, F., and J. Dudhia, 2001: Coupling an advanced land-surface / hydrology model with the Penn State/NCAR MM5 modeling system.. Part I: Model description and implementation. Mon. Wea. Rev., 129, 569–585.

    Article  Google Scholar 

  • Chen S.-H., and W.-Y. Sun, 2002: A one-dimensional time-dependent cloud model. J. Meteor. Soc. Japan, 80, 99–118.

    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 

  • Chou, M.-D., and M. J. Suarez, 1999: A shortwave radiation Parameterization for atmospheric studies. NASA Tech. Memo. 15, NASA/TM-104606, 40 pp.

  • Colle, B. A., and C. F. Mass, 2000: The 5–9 February 1996 flooding event over the Pacific Northwest: Sensitivity studies and evulation of the MM5 precipitation forecasts. Mon. Wea. Rev., 128, 593–617.

    Article  Google Scholar 

  • _____, and Y. Zeng, 2004: Bulk microphysical sensitivities within the MM5 for orographic precipitation. Part I: The Sierra 1986 event. Mon. Wea. Rev., 132, 2780–2801.

    Article  Google Scholar 

  • _____, M. F. Garvert, J. B. Wolfe, C. F. Mass, and C. P. Woods, 2005: The 13–14 December 2001 IMPROVE-2 event. Part III: Simulated microphysical budgets and sensitivity studies. J. Atmos. Sci., 62, 3535–3558.

    Article  Google Scholar 

  • Cotton, W. R., M. A. Stephens, T. Nehrkorn, and G. J. Tripoli, 1982: The Colorado State University three-dimensional cloud-mesoscale model-1982. Part II: An ice-phase parameterization. J. Rech. Atmos., 16, 295–320.

    Google Scholar 

  • _____, G. J. Tripoli, R. M. Rauber, and E. A. Mulvihill, 1986: Numerical simulation of the effect of varying ice crystal nucleation rates and aggregation processes on orographic snowfall. J. Appl. Meteorol., 25, 1658–1679.

    Article  Google Scholar 

  • Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46, 3077–3107.

    Article  Google Scholar 

  • _____, S.-Y. Hong, and K.-S. Lim, 2008: A new method for representing mixed-phase particle fall speeds in bulk microphysics parameterizations. J. Meteor. Soc. Japan, 86A, 33–33.

    Article  Google Scholar 

  • Ferrier, B. S., 1994: A double-moment multiple-phase four-class bulk ice scheme. Part I: Description. J. Atmos. Sci., 51, 249–280.

    Article  Google Scholar 

  • _____, W.-K. Tao, and J. Simpson, 1995: A double-moment multiplephase four-class bulk ice scheme. Part II: Simulations of convective storms in different large-scale environments and comparisons with other bulk parameterizations. J. Atmos Sci., 52, 1001–1033.

    Article  Google Scholar 

  • Field, P. R., R. J. Hogan, P. R. A. Brown, A. J. Illingworth, T. W. Choularton, and R. J. Cotton, 2005: Parameterization of ice-particle size distributions for mid-latitude stratiform cloud. Quart. J. Roy. Meteor. Soc., 131, 1997–2017.

    Article  Google Scholar 

  • Fovell, R. G., and Y. Ogura, 1988: Numerical simulation of a midlatitude squall line in two-dimensions. J. Atmos. Sci., 45, 3846–3879.

    Article  Google Scholar 

  • _____, and H. Su, 2007: Impact of cloud microphysics on hurricane track forecasts. Geophys. Res. Lett., 34, L24810, doi:10.1029/2007GL031723.

    Article  Google Scholar 

  • Fritsch, J. M., and R. E. Carbone, 2002: Research and development to improve quantitative precipitation forecasts in the warm season: A synopsis of the March 2002 USWRP Workshop and statement of priority recommendations. Technical Report to USWRP Science Committee, 134 pp.

  • Grell, G. A., and D. Devenyi, 2002: A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophy. Res. Lett., 29, 1693, doi:10.1029/2002GL015311.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Hong, S.-Y., and J.-O. J. Lim, 2006: The WRF Single-Moment 6-Class Microphysics Scheme (WSM6). J. Korean Meteor. Soc., 42, 129–151.

    Google Scholar 

  • _____, 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, 103–120.

    Article  Google Scholar 

  • _____, K.-S. Lim, J.-H. Kim, J.-O. Lim, and J. Dudhia, 2009: Sensitivity study of the cloud-resolving convective simulations with WRF using two-bulk microphysical parameterizations: Ice-phase microphysics versus sedimentation effects. J. Appl. Meteor. Climatol., 48, 61–76.

    Article  Google Scholar 

  • Jankov, I., W. A. Gallus Jr., M. Segal, B. Shaw, and S. E. Koch, 2005: The impact of different WRF model physical parameterizations and their interactions on warm season MCS rainfall. Wea. Forecasting, 6, 1048–1060.

    Article  Google Scholar 

  • _____, ______, ______, ______, and ______, 2007: Influence of initial conditions on the WRF-ARW model QPF response to physical parameterization changes. Wea. Forecasting, 22, 501–519.

    Article  Google Scholar 

  • Kain, J. S., and J. M. Frirsch, 1990: A one-dimensional entraining/detraining plume model and its application in convective parameterization. J. Atmos. Sci., 47, 2784–2802.

    Article  Google Scholar 

  • _____, and ______, 1993: Convective parameterization for mesoscale models: The Kain-Fritsch scheme. In The Representation of Cumulus Convection in Numerical Models, Emanuel, K. A., and D. J. Raymond, Eds., Amer. Meteor. Soc., 246 pp.

  • Kessler, E., 1969: On the Distribution and Continuity of Water Substance in Atmospheric Circulation. Meteor. Monogr., 10, Amer. Meteor. Soc., 84 pp.

  • Knabb, R. D., J. R. Rhome, and D. P. Brown, 2005: Tropical Cyclone Report — Hurricane Katrina — 23–30 August 2005. NHC/NOAA, 43 pp. [Available online at http://www.nhc.noaa.gov/2005atlan.shtml.]

  • Lang, S., W.-K. Tao, R. Cifelli, W. Olson, J. Halverson, S. Rutledge, and J. Simpson, 2007: Improving simulations of convective system from TRMM LBA: Easterly and Westerly regimes. J. Atmos. Sci., 64, 1141–1164.

    Article  Google Scholar 

  • Levin, Z., and W.R. Cotton, Eds., 2008: Aerosol Pollution Impact on Precipitation; A Scientific Review. Springer Press, 382 pp.

  • Li, X., and Z. Pu, 2008: Sensitivity of numerical simulation of early rapid intensification of hurricane Emily (2005) to cloud microphysical and planetary boundary layer parameterization. Mon. Wea. Rev., 136, 4819–4838.

    Article  Google Scholar 

  • _____, W.-K. Tao, A. Khain, J. Simpson, and D. Johnson, 2009: Sensitivity of a cloud-resolving model to bulk and explicit-bin microphysics schemes: Part I: Comparisons. J. Atmos. Sci., 66, 3–21.

    Article  Google Scholar 

  • Lin, Y.-L., R. D. Farley and H. D. Orville, 1983: Bulk parameterization of the snow field in a cloud model. J. Climate Appl. Meteor., 22, 1065–1092.

    Article  Google Scholar 

  • _____, D.-L. Zhang, and M. K. Yau, 1997: A multiscale numerical study of Hurricane Andrew (1992). Part I: An explicit simulation. Mon. Wea. Rev., 125, 3073–3093.

    Google Scholar 

  • Lord, S. J., H. E. Willoughby, and J. M. Piotrowicz, 1984: Role of a parameterized ice-phase microphysics in an axisymmetric, nonhydrostatic tropical cyclone model. J. Atmos. Sci., 41, 2836–2848.

    Article  Google Scholar 

  • McCumber, M., W.-K. Tao, J. Simpson, R. Penc, and S.-T. Soong, 1991: Comparison of ice-phase microphysical parameterization schemes using numerical simulations of tropical convection. J. Appl. Meteorol., 30, 985–1004.

    Article  Google Scholar 

  • Mellor, G. L., and T. Yamada, 1992: Development of a turbulence closure model for geophysical fluid problems. Rev. Geophys. Space Phys., 20, 851–875.

    Article  Google Scholar 

  • Meyers, M. P., R. L. Walko, J. Y. Harrington, and W. R. Cotton, 1997: New RAMS cloud microphysics. Part II: The two-moment scheme. Atmos. Res., 45, 3–39.

    Article  Google Scholar 

  • Michalakes, J., S. Chen, J. Dudhia, L. Hart, J. Klemp, J. Middlecoff, and W. Skamarock, 2001: Development of a next generation regional weather research and forecast model?. Argonne National Laboratory Preprint, ANL/MCS-p868-0101, 8 pp.

  • Milbrandt, J. A., and M. K. Yau, 2005: A multimoment bulk microphysics parameterization. Part II: A proposed three-moment closure and scheme description. J. Atmos. Sci., 62, 3065–3081.

    Article  Google Scholar 

  • Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Jacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102(D14), 16663–16682.

    Article  Google Scholar 

  • Monin, A. S., and A. M. Obukhov, 1954: Basic laws of turbulent mixing in the surface layer of the atmosphere. Contrib. Geophys. Inst. Acad. Sci. USSR, 151, 163–187. (in Russian)

    Google Scholar 

  • Morrison, H., and W. Grabowski, 2008: A novel approach for representing ice microphysics in models: Description and tests using a kinematic framework. J. Atmos. Sci., 65, 1528–1548.

    Article  Google Scholar 

  • _____, J. A. Curry, and V. I. Khvorostyanov, 2005: A new doublemoment microphysics parameterization for application in cloud and climate models. Part I: Description. J. Atmos. Sci., 62, 1665–1677.

    Article  Google Scholar 

  • Nicholls, M. E., 1987: A comparison of the results of a two-dimensional numerical simulation of a tropical squall line with observations. Mon. Wea. Rev., 115, 3055–3077.

    Article  Google Scholar 

  • Olson, W.-S., and Coauthors, 2006: Precipitation and latent heating distributions from satellite passive microwave radiometry Part I: Method and uncertainties. J. Appl. Meteorol., 45, 702–720.

    Article  Google Scholar 

  • Prasad, N., H.-Y. M. Yeh, R. F. Adler, and W.-K. Tao, 1995: Infrared and microwave simulations of an intense convective system and comparison with aircraft observations. J. Appl. Meteorol., 34, 153–174.

    Article  Google Scholar 

  • Pu, Z., X. Li, C. Velden, S. Aberson, and W. T. Liu, 2008: Impact of aircraft dropsonde and satellite wind data on numerical simulations of two landfalling tropical storms during the Tropical Cloud Systems and Processes Experiment. Wea. Forecasting, 23, 62–79

    Article  Google Scholar 

  • Reisner, J. R., R. M. Rasmussen, and R. T. Bruintjes, 1998: Explicit forecasting of supercooled liquid water in winter storms using the MM5 mesoscale model. Quart. J. Roy. Meteor. Soc., 124, 1071–1107.

    Article  Google Scholar 

  • Rogers, E., T. Black, B. Ferrier, Y. Lin, D. Parrish, and G. DiMego, 2001: Changes to the NCEP Meso Eta analysis and forecast system: Increase in resolution, new cloud microphysics, modified precipitation assimilation, and modified 3DVAR analysis. NWS Tech. Procedures Bull. 488, NOAA/NWS. [Available online at http://www.emc.ncep.noaa.gov/mmb/mmbpll/eta12tpb.]

  • Rogers, R., M. Black, S. S. Chen, and R. Black, 2007: An evaluating microphysical fields from mesoscale model simulations of tropical cyclones. Part I: Comparisons with observations. J. Atmos. Sci., 64, 1811–1834.

    Article  Google Scholar 

  • Rosenfeld, D., A. Khain, B. Lynn, and W. L. Woodley, 2007: Simulation of hurricane response to suppression of warm rain by sub-micron aerosols. Atmos. Chem. Phys., 7, 3411–3424.

    Google Scholar 

  • Rutledge, S. A., and P. V. Hobbs, 1984: The mesoscale and microscale structure and organization of clouds and precipitation in mid-latitude clouds. Part XII: A diagnostic modeling study of precipitation development in narrow cold frontal rainbands. J. Atmos. Sci., 41, 2949–2972.

    Article  Google Scholar 

  • Schultz, P., 1995: An explicit cloud physics parameterization for operational numerical weather prediction. Mon. Wea. Rev., 123, 3331–3343.

    Article  Google Scholar 

  • Soong, S.-T., and Y. Ogura, 1973: A comparison between axisymmetric and slab-symmetric cumulus cloud models. J. Atmos. Sci., 30, 879–893.

    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 mesoscaleresolving model: Preliminary results with Hurricane Katrina (2005). Geophys. Res. Lett., 33, L13813, doi:10.1029/2006GL026143.

    Article  Google Scholar 

  • Straka, J. M., and E. R. Mansell, 2005: A bulk microphysics parameterization with multiple ice precipitation categories. J. Appl. Meteorol., 44, 445–466.

    Article  Google Scholar 

  • Tao, W.-K., and J. Simpson, 1989: Modeling study of a tropical squall-type convective line. J. Atmos. Sci., 46, 177–202.

    Article  Google Scholar 

  • _____, and ______, 1993: The Goddard Cumulus Ensemble Model. Part I: Model description. Terr. Atmos. Oceanic Sci., 4, 19–54.

    Google Scholar 

  • _____, and M. Moncrieff, 2009: Multi-scale cloud-system modeling. Rev. Geophys., 47, RG4002, doi:10.1029/2008RG000276.

    Article  Google Scholar 

  • _____, J. Simpson, and M. McCumber, 1989: An ice-water saturation adjustment. Mon. Wea. Rev., 117, 231–235.

    Article  Google Scholar 

  • _____, J. Scala, B. Ferrier, and J. Simpson, 1995: The effects of melting processes on the development of a tropical and a midlatitude squall line. J. Atmos. Sci., 52, 1934–1948.

    Article  Google Scholar 

  • _____, and Coauthors, 2003a: Microphysics, radiation and surface processes in the Goddard Cumulus Ensemble (GCE) model. Meteor. Atmos. Phys., 82, 97–137.

    Article  Google Scholar 

  • _____, C.-L. Shie, D. Johnson, R. Johnson, S. Braun, J. Simpson, and P. E. Ciesielski, 2003b: Convective systems over South China Sea: Cloudresolving model simulations. J. Atmos. Sci., 60, 2929–2956.

    Article  Google Scholar 

  • Thompson, G., R. M. Rasmussen, and K. Manning, 2004: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part I: Description and sensitivity analysis. Mon. Wea., Rev., 132, 519–542.

    Article  Google Scholar 

  • _____, 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, 5095–5155.

    Article  Google Scholar 

  • Walko, R. L., W. R. Cotton, M. P. Meyers, and J. Y. Harrington, 1995: New RAMS cloud microphysics parameterization Part I: the singlemoment scheme. Atmos. Res., 38, 29–62.

    Article  Google Scholar 

  • Wang, Y., 2002: An explicit simulation of tropical cyclones with a triply nested movable mesh primitive equations model-TCM3. Part II: Model refinements and sensitivity to cloud microphysics parameterization. Mon. Wea. Rev., 130, 3022–3036.

    Article  Google Scholar 

  • Weckwerth, T. M., H. V. Murphey, C. Flamant, J. Goldstein, and C. R. Pettet, 2008: An observation study of convective initiation on 12 June 2002 during IHOP_2002. Mon. Wea. Rev., 136, 2283–2304.

    Article  Google Scholar 

  • Wicker, L. J., and W. C. Skamarock, 2002: Time splitting methods for elastic models using forward time schemes. Mon. Wea. Rev., 130, 2088–2097.

    Article  Google Scholar 

  • Willoughby, H. E., H.-L. Jin, S. J. Lord, and J. M. Piotrowicz, 1984: Hurricane structure and evolution as simulated by an axisymmetric nonhydrostatic numerical model. J. Atmos. Sci., 41, 1169–1186.

    Article  Google Scholar 

  • Wilson, J. W., and R. D. Roberts, 2006: Summary of convective storm initiation and evolution during IHOP: Observational and modeling perspective. Mon. Wea. Rev., 134, 23–47.

    Article  Google Scholar 

  • Wu, X., W. D. Hall, W. W. Grabowski, M. W. Moncrieff, W. D. Collins, and J. T. Kiehl, 1999: Long-term behavior of cloud systems in TOGA COARE and their interactions with radiative and surface processes. Part II: Effects of ice microphysics on cloud-radiation interaction. J. Atmos. Sci., 56, 3177–3195.

    Article  Google Scholar 

  • Yang, M.-J., and R. A. Houze Jr., 1995: Multicell squall-line structure as a manifestation of vertically trapped gravity waves. Mon. Wea. Rev., 123, 641–661.

    Article  Google Scholar 

  • _____, and L. Ching, 2005: A modeling study of Typhoon Toraji (2001): Physical parameterization sensitivity and topographic effect. Terr. Atmos. Oceanic Sci., 16, 177–213.

    Google Scholar 

  • Yeh, H.-Y. M., N. Prasad, R. Meneghini, W.-K. Tao, and R. F. Adler, 1995: Model-based simulation of TRMM spaceborne radar observations. J. Appl. Meteorol., 34, 175–197.

    Article  Google Scholar 

  • Yoshizaki, M., 1986: Numerical simulations of tropical squall-line clusters: Two-dimensional model. J. Meteor. Soc. Japan, 64, 469–491.

    Google Scholar 

  • Yuter, S. E., and R. A. Houze Jr., 1995: Three-dimensional kinematic and microphysical evolution of Florida cumulonimbus. Part II: Frequency distributions of vertical velocity, reflectivity, and differential reflectivity. Mon. Wea. Rev., 123, 1941–1963.

    Article  Google Scholar 

  • Zeng, X., W.-K. Tao, S. Lang, A. Y. Hou, M. Zhang, and J. Simpson, 2008: On the sensitivity of atmospheric ensembles to cloud microphysics in long-term cloud-resolving model simulations. J. Meteor. Soc. Japan, 86A, 45–65.

    Article  Google Scholar 

  • Zhu, T., and D.-L. Zhang, 2006a: Numerical simulation of Hurricane Bonnie (1998). Part II: Sensitivity to varying cloud microphysical processes. J. Atmos. Sci., 63, 109–126.

    Article  Google Scholar 

  • _____, and ______, 2006b: The impact of the storm-induced SST cooling on hurricane intensity. Adv. Atmos. Sci., 23, 14–22.

    Article  Google Scholar 

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Authors and Affiliations

  1. Laboratory for Atmospheres, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA

    Wei-Kuo Tao, Jainn Jong Shi & Stephen Lang

  2. Goddard Earth Sciences and Technology Center, University of Maryland at Baltimore County, Baltimore County, Maryland, USA

    Jainn Jong Shi

  3. Rosentiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida, USA

    Shuyi S. Chen

  4. Science Systems and Applications, Inc., Lanham, Maryland, USA

    Stephen Lang

  5. Department of Atmospheric Science, National Central University, Jhong-Li, Taiwan, R.O.C.

    Pay-Liam Lin

  6. Department of Atmospheric Sciences and Global Environment Laboratory, Yonsei University, Seoul, Korea

    Song-You Hong

  7. Hydrological Sciences Branch, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA

    Christa Peters-Lidard

  8. Goddard Modeling Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA

    Arthur Hou

  9. Code 613.1, NASA/GSFC, Greenbelt, MD, 20771, USA

    Wei-Kuo Tao

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Tao, WK., Shi, J.J., Chen, S.S. et al. The impact of microphysical schemes on hurricane intensity and track. Asia-Pacific J Atmos Sci 47, 1–16 (2011). https://doi.org/10.1007/s13143-011-1001-z

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