Asia-Pacific Journal of Atmospheric Sciences

, Volume 50, Issue 1, pp 121–131 | Cite as

A history of mesoscale model development

Review

Abstract

The development of atmospheric mesoscale models from their early origins in the 1970’s until the present day is described. Evolution has occurred in dynamical and physics representations in these models. The dynamics has had to change from hydrostatic to fully nonhydrostatic equations to handle the finer scales that have become possible in the last few decades with advancing computer power, which has enabled real-time forecasting to go to finer grid sizes. Meanwhile the physics has also become more sophisticated than the initial representations of the major processes associated with the surface, boundary layer, radiation, clouds and convection. As resolutions have become finer, mesoscale models have had to change paradigms associated with assumptions related to what is considered sub-grid scale needing parameterization, and what is resolved well enough to be explicitly handled by the dynamics. This first occurred with cumulus parameterization as real-time forecast models became able to represent individual updrafts, and is now starting to occur in the boundary layer as future forecast models may be able resolve individual thermals. Beyond that, scientific research has provided a greater understanding of detailed microphysical and land-surface processes that are important to aspects of weather prediction, and these parameterizations have been developing complexity at a steady rate. This paper can just give a perspective of these developments in the broad field of research associated with mesoscale atmospheric model development.

Keywords

Mesoscale modeling numerical weather prediction physics parameterization 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anthes, R. A., and T. T. Warner, 1978: Development of hydrodynamic models suitable for air pollution and other mesometeorological studies. Mon. Wea. Rev., 106, 1045–1078.CrossRefGoogle Scholar
  2. Arakawa, A., W. H. Schubert, 1974: Interaction of a cumulus cloud ensemble with the large-scale environment, Part I. J. Atmos. Sci., 31, 674–701.CrossRefGoogle Scholar
  3. Beljaars, A. C. M., 1995: The parametrization of surface fluxes in large-scale models under free convection. Quart. J. Roy. Meteor. Soc., 121, 255–270.CrossRefGoogle Scholar
  4. Betts, A. K., and M. J. Miller, 1986: A new convective adjustment scheme. Part II: Single column tests using GATE wave, BOMEX, ATEX and arctic air-mass data sets. Quart. J. Roy. Meteor. Soc., 112, 693–709.Google Scholar
  5. Blackadar, A. K., 1979: High resolution models of the planetary boundary layer. Advances in Science and Engineering. Vol. 1, No. 1, J. Pfafflin and E. Ziegler, Eds., Gordon and Breach, 50–85.Google Scholar
  6. Bougeault, P., and P. Lacarrere, 1989: Parameterization of orography-induced turbulence in a mesobeta-scale model. Mon. Wea. Rev., 117, 1872–1890.CrossRefGoogle Scholar
  7. Bubnova, R., G. Hello, P. Benard, and J.-F. Geleyn, 1995: Integration of the fully-elastic equations cast in the hydrostatic pressure terrain-following coordinate in the framework of the ARPEGE/ALADIN NWP system. Mon. Wea. Rev., 123, 515–535.CrossRefGoogle Scholar
  8. Carlson, T. N., and F. E. Boland, 1978: Analysis of urban-rural canopy using a surface heat flux/temperature model. J. Appl. Meteorol., 17, 998–1013.CrossRefGoogle Scholar
  9. 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.CrossRefGoogle Scholar
  10. Clark, T. L., 1977: A small-scale dynamic model using a terrain-following coordinate transformation. J. Comput. Phys., 24, 186–215.CrossRefGoogle Scholar
  11. Chou, M.-D., and M. J. Suarez, 1994: An efficient thermal infrared radiation parameterization for use in general circulation models. NASA Tech. Memo, 84 pp.Google Scholar
  12. Cotton, W. R., and G. J. Tripoli, 1978: Cumulus convection in shear flow-three-dimensional numerical experiments. J. Atmos. Sci. 35, 1503–1521.CrossRefGoogle Scholar
  13. Cullen, M. J. P., 1993: The Unified Forecast Climate model. Meteorol. Mag., 122, 81–94.Google Scholar
  14. Deardorff, J. W., 1972: Parameterization of the planetary boundary layer for use in general circulation models. Mon. Wea. Rev., 100, 93–106.CrossRefGoogle Scholar
  15. —, 1978: Efficient prediction of ground surface temperature and moisture, with inclusion of a layer of vegetation, J. Geophys. Res., 83(C4), 1889–1903.CrossRefGoogle Scholar
  16. Delsol, F., K. Miyakoda, and R. H. Clarke, 1971: Parameterized processes in the surface boundary layer of an atmospheric circulation model. Quart. J. Roy. Meteor. Soc., 97, 181–208.CrossRefGoogle Scholar
  17. Doms, G., and U. Schaettler, 1997: The nonhydrostatic limited-area model LM (Lokal-Modell) of DWD. Part I: Scientific Documentation. Deutscher Wetterdienst, 155 pp.Google Scholar
  18. 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.CrossRefGoogle Scholar
  19. —, 1993: A nonhydrostatic version of the Penn State / NCAR mesoscale model: Validation tests and simulations of an Atlantic cyclone and cold front. Mon. Wea. Rev., 121, 1493–1513.CrossRefGoogle Scholar
  20. —, and J. F. Bresch, 2002: A global version of the PSU-NCAR mesoscale model. Mon. Wea. Rev., 130, 2989–3007.CrossRefGoogle Scholar
  21. —, S.-Y. Hong, and K.-S. Lim, 2008: A new method for representing mixed-phase particle fall speeds in bulk microphysics parameteriza-tions. J. Meteor. Soc. Japan, 86, 33–44.Google Scholar
  22. Fu, Q., and K. N. Liou, 1992: On the correlated k-distribution method for radiative transfer in nonhomogeneous atmospheres. J. Atmos. Sci., 49, 2139–2156.CrossRefGoogle Scholar
  23. Grell, G. A., 1993: Prognostic evaluation of assumptions used by cumulus parameterizations. Mon. Wea. Rev., 121, 764–787.CrossRefGoogle Scholar
  24. —, and S. Freitas, 2013: Development and applications of a stochastic convective parameterization for a smooth transition to cloud resolving scales that includes aerosol interactions. Geophys. Res. Abs., 15, EGU2013–11198.Google Scholar
  25. Han, J., and H.-L. Pan, 2011: Revision of convection and vertical diffusion schemes in the NCEP global forecast system. Wea. Forecasting, 26, 520–533.CrossRefGoogle Scholar
  26. Hong, S.-Y., and J. Dudhia, 2012: Next-generation numerical weather prediction: Bridging parameterization, explicit clouds, and large eddies. Meeting Summaries. Bull. Amer. Meteor. Soc., 93, January, online.Google Scholar
  27. —, —, and S.-H. Chen, 2004: A revised approach to ice-microphysical processes for the bulk parameterization of cloud and precipitation. Mon. Wea. Rev., 132, 103–120.CrossRefGoogle Scholar
  28. —, H.-M. Juang, and Q. Zhao, 1998: Implementation of prognostic cloud scheme for a regional spectral model. Mon. Wea. Rev., 126, 2621–2639.Google Scholar
  29. —, and J.-O. J. Lim, 2006: The WRF single-moment 6-class microphysics scheme (WSM6). J. Korean Meteor. Soc., 42, 129–151.Google Scholar
  30. —, Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 2318–2341.Google Scholar
  31. —, and H.-L. Pan, 1996: Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon. Wea. Rev., 124, 2322–2339.Google Scholar
  32. Hsie, E.-Y., R. A. Anthes, and D. Keyser, 1984: Numerical simulation of frontogenesis in a moist atmosphere. J. Atmos. Sci., 41, 2581–2594.CrossRefGoogle Scholar
  33. Janjic, Z. I., 1994: The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon. Wea. Rev., 122, 927–945.CrossRefGoogle Scholar
  34. —, 2003: A nonhydrostatic model based on a new approach. Meteor. Atmos. Phys., 82, 271–285.CrossRefGoogle Scholar
  35. Jimenez, P. A., and J. Dudhia, 2012: Improving the representation of resolved and unresolved topographic effects on surface wind in the WRF model. J. Appl. Meteor. Climatol., 51, 300–316.CrossRefGoogle Scholar
  36. Juang, H.-M., and S.-Y. Hong, 2010: Forward semi-Lagrangian advection with mass conservation and positive definiteness for falling hydro-meteors. Mon. Wea. Rev., 138, 1778–1791.CrossRefGoogle Scholar
  37. —, —, and M. Kanamitsu, 1997: The NCEP regional spectral model: an update. Bull. Amer. Meteor. Soc., 78, 2125–2143.CrossRefGoogle Scholar
  38. Kain, J. S., and J. M. Fritsch, 1990: A one-dimensional entraining/detraining plume model and its application in convective parameteriza-tion. J. Atmos. Sci., 47, 2784–2802.CrossRefGoogle Scholar
  39. Kessler, E., 1969: On the distribution and continuity of water substance in atmospheric circulations. Meteor. Monogr., 32, Amer. Meteor. Soc., 84 pp.Google Scholar
  40. Klemp, J. B., W. C. Skamarock, and J. Dudhia, 2007: Conservative split-explicit time integration methods for the compressible nonhydrostatic equations. Mon. Wea. Rev., 135, 2897–2913.CrossRefGoogle Scholar
  41. —, and R. B. Wilhelmson, 1978: The simulation of three-dimensional convective storm dynamics. J. Atmos. Sci., 35, 1070–1096.CrossRefGoogle Scholar
  42. Kuo, Y.-H, and R. A. Anthes, 1984: Semiprognostic tests of Kuo-type cumulus parameterization schemes in an extratropical convective system. Mon. Wea. Rev., 112, 1498–1509.CrossRefGoogle Scholar
  43. Kuo, H. L., 1974: Further studies of the parameterization of the influence of cumulus convection on large-scale flow. J. Atmos. Sci., 31, 1232–1240.CrossRefGoogle Scholar
  44. Lebo, Z. J., and H. Morrison, 2013: A novel Scheme for parameterizing aerosol processing in warm clouds. J. Atmos. Sci., 70, 3576–3598.CrossRefGoogle Scholar
  45. Liang, X.-Z., and Coauthors, 2012: Regional Climate-Weather Research and Forecasting Model (CWRF). Bull. Amer. Meteor. Soc., 93, 1363–1387.CrossRefGoogle Scholar
  46. Lim, K.-S. S., and S.-Y. Hong, 2010: Development of an effective double-moment cloud microphysics scheme with prognostic cloud conden-sation nuclei (CCN) for weather and climate models. Mon. Wea. Rev., 138, 1587–1612.CrossRefGoogle Scholar
  47. 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.CrossRefGoogle Scholar
  48. Louis, J. F., 1979: A parametric model of vertical eddy fluxes in the atmo-sphere. Bound.-Layer Meteor., 17, 187–202.CrossRefGoogle Scholar
  49. Mahrt, L. T., and J. Sun, 1995: The subgrid velocity scale in the bulk aerodynamic relationship for spatially averaged scalar fluxes. Mon. Wea. Rev., 123, 3032–3041.CrossRefGoogle Scholar
  50. McGregor, J. L., L. M. Leslie, and D. J. Gauntlett, 1978: The ANMRC limited-area model: Consolidated formulation and operational results. Mon. Wea. Rev., 106, 427–438.CrossRefGoogle Scholar
  51. Mellor, G. L., and T. Yamada, 1974: A hierarchy of turbulence closure models for planetary boundary layers. J. Atmos. Sci., 31, 1791–1806.CrossRefGoogle Scholar
  52. —, and —, 1982: Development of a turbulence closure model for geophysical fluid problems. Rev. Geophys., 20(4), 851–875.CrossRefGoogle Scholar
  53. Miller, M. J., and R. P. Pearce, 1974: A three-dimensional primitive equation model of cumulonimbus convection. Quart. J. Roy. Meteor. Soc., 100, 133–154.CrossRefGoogle Scholar
  54. Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102, 16663–16682.CrossRefGoogle Scholar
  55. Moeng, C.-H., J. Dudhia, J. Klemp, and P. Sullivan, 2007: Examining two-way grid nesting for large eddy simulations of the PBL using the WRF model. Mon. Wea. Rev., 135, 2295–2311.CrossRefGoogle Scholar
  56. Morrison, H., G. Thompson, and V. Tatarskii, 2009: Impact of cloud micro-physics on the development of trailing stratiform precipitation in a simulated squall line: Comparison of one- and two-moment schemes. Mon. Wea. Rev., 137, 991–1007.CrossRefGoogle Scholar
  57. Nakanishi, M., and H. Niino, 2006: An improved Mellor-Yamada level 3 model: its numerical stability and application to a regional prediction of advecting fog. Bound.-Layer Meteor., 119, 397–407.CrossRefGoogle Scholar
  58. Pincus, R., R. Hemler, and S. A. Klein, 2006: Using stochastically gen-erated subcolumns to represent cloud structure in a large-scale model. Mon. Wea. Rev., 134, 3644–3656.CrossRefGoogle Scholar
  59. Pleim, J. E., 2007: A combined local and nonlocal closure model for the atmospheric boundary layer. Part I: Model description and testing. J. Appl. Meteor. Climatol., 46, 1396–1409.CrossRefGoogle Scholar
  60. Reisner, J., R. M. Rasmussen, and R. T. Bruintjes, 1998: Explicit fore-casting of supercooled liquid water in winter storms using the MM5 mesoscale model. Quart. J. Roy. Meteor. Soc., 124, 1071–1107.CrossRefGoogle Scholar
  61. Ruiz-Arias, J. A., J. Dudhia, F. J. Santos-Alamillos, and D. Pozo-Vaìzquez (2013), Surface clear-sky shortwave radiative closure intercomparisons in the Weather Research and Forecasting model. J. Geophys. Res.-Atmos., 118, 9901–9913.CrossRefGoogle Scholar
  62. Rutledge, S. A., and P. V. Hobbs, 1983: The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. VIII: A model for the “seeder-feeder” process in warm-frontal rainbands. J. Atmos. Sci., 40, 1185–1206.Google Scholar
  63. Saito, K., J. I. Ishida, K. Aranami, T. Hara, T. Segawa, M. Narita, and Y. Honda, 2007: Nonhydrostatic atmospheric models and operational development at JMA. J. Meteor. Soc. Japan, 85B, 271–304.CrossRefGoogle Scholar
  64. Sellers, P. J., Y. Mintz, Y. C. Sud, and A. Dalcher, 1986: A simple bio-sphere model (SIB) for use within general circulation models. J. Atmos. Sci., 43, 505–531.CrossRefGoogle Scholar
  65. Shuman, F. G., 1989: History of numerical weather prediction at the National Meteorological Center. Wea. Forecasting, 4, 286–296.CrossRefGoogle Scholar
  66. Siebesma, A. P., P. M. M. Soares, and J. Teixeira, 2007: A combined eddy-diffusivity mass-flux approach for the convective boundary layer. J. Atmos. Sci., 64, 1230–1248.CrossRefGoogle Scholar
  67. Skamarock, W. C., J. B. Klemp, M. G. Duda, L. D. Fowler, S.-H. Park, and T. D. Ringler, 2012: A multiscale nonhydrostatic atmospheric model using centroidal Voronoi tesselations and C-Grid staggering. Mon. Wea. Rev., 140, 3090–3105.CrossRefGoogle Scholar
  68. Stull, R. B., 1984: Transilient Turbulence Theory. Part I: The concept of eddy-mixing across finite distances. J. Atmos. Sci., 41, 3351–3367.Google Scholar
  69. Sukoriansky, S., B. Galperin, and V. Perov, 2005: Application of a new spectral model of stratified turbulence to the atmospheric boundary layer over sea ice. Bound.-Layer Meteor., 117, 231–257.CrossRefGoogle Scholar
  70. Tanguay, M. A., A. Robert, and R. Laprise, 1990: A semi-implicit semi-Lagrangian fully compressible regional forecast model. Mon. Wea. Rev., 118, 1970–1980.CrossRefGoogle Scholar
  71. Tao, W.-K., J. Simpson, and M. McCumber, 1989: An ice-water saturation adjustment. Mon. Wea. Rev., 117, 231–235.CrossRefGoogle Scholar
  72. Tapp, M. C., and P. W. White, 1976: A non-hydrostatic mesoscale model. Quart. J. Roy. Meteor. Soc., 102, 277–296.CrossRefGoogle Scholar
  73. Thompson, G., and T. Eidhammer, 2014: A study of aerosol impacts on clouds and precipitation development in a large winter cyclone. Accepted by J. Atmos. Sci.,January 2014.Google Scholar
  74. —, 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, 5095–5115.Google Scholar
  75. Tiedtke, M., 1989: A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon. Wea. Rev., 117, 1779–1800.CrossRefGoogle Scholar
  76. —, W. A. Heckley, and J. Slingo, 1988: Tropical forecasting at ECMWF: The influence of physical parametrization on the mean structure of forecasts and analyses. Quart. J. Roy. Meteor. Soc., 114, 639–664.CrossRefGoogle Scholar
  77. Tripoli, G. J., and W. R. Cotton, 1982: The Colorado State University three-dimensional cloud / mesoscale model-1982. Part I: General theoretical framework and sensitivity experiments. J. Rech. Atmos., 16, 185–220.Google Scholar
  78. Troen, I., and L. Mahrt, 1986: A simple model of the atmospheric boundary layer; sensitivity to surface evaporation. Bound.-Layer Meteor., 37, 129–148.CrossRefGoogle Scholar
  79. Wilson, M. F., A. Henderson-Sellers, R. E. Dickinson, and P. J. Kennedy, 1987: Sensitivity of the Biosphere-Atmosphere Transfer Scheme (BATS) to the inclusion of variable soil characteristics. J. Climate Appl. Meteor., 26, 341–362.CrossRefGoogle Scholar
  80. Xue, M., K. K. Droegemeier, V. Wong, A. Shapiro, and K. Brewster, 1995: Advanced Regional Prediction System, Version 4.0. Center for Analysis and Prediction of Storms, University of Oklahoma, 380 pp.Google Scholar
  81. Zhang, D.-L., and R. A. Anthes, 1982: A high-resolution model of the planetary boundary layer-sensitivity tests and comparisons with SESAME-79 data. J. Appl. Meteorol., 21, 1594–1609.CrossRefGoogle Scholar
  82. —, H.-R. Chang, N. L. Seaman, T. T. Warner, and J. M. Fritsch, 1986: A two-way interactive nesting procedure with variable terrain resolution. Mon. Wea. Rev., 114, 1330–1339.CrossRefGoogle Scholar

Copyright information

© Korean Meteorological Society and Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.National Center for Atmospheric ResearchBoulderUSA
  2. 2.Mesoscale and Microscale Meteorology DivisionNational Center for Atmospheric ResearchBoulderUSA

Personalised recommendations