• Arakawa, A., and V. R. Lamb, 1977: Compatible design of the basic dynamical processes of the UCLA general circulation model. Methods in Compatible Physics, Vol. 17, Academic Press, 174–265.

  • Baillie, C. F., A. E. MacDonald, J. L. Lee, and S. Sun, 1995: QNH:A portable, parallel, multiscale quasi-nonhydrostatic meteorological model. Applications of High Performance Computing in Engineering IV, H. Power, Ed., Computational Mechanics Publications, 1995–2202.

  • ——, ——, and ——, 1996: QNH: A numerical weather prediction model developed for Massively Parallel Processors. EUROSIM 96, Elsevier Science B. V., 399–504.

  • Bleck, R., and S. G. Benjamin, 1993: Regional weather prediction with a model combining terrain-following and isentropic coordinates. Part I: Model description. Mon. Wea. Rev.,121, 1770–1785.

  • Browning, G. L., and H.-O. Kreiss, 1986: Scaling and computation of smooth atmospheric motions. Tellus,38A, 295–313.

  • ——, and A. E. MacDonald, 1993: Incorporating topography into the multiscale systems for the atmosphere and oceans. Dyn. Atmos. Oceans,18, 119–149.

  • ——, and H.-O. Kreiss, 1994: Splitting methods for problems of different time scales. Mon. Wea. Rev.,122, 2614–2622.

  • Dudhia, J., 1993: A non-hydrostatic version of the Penn State/NCAR Mesoscale Model: Validation tests and simulation of an Atlantic cyclone cold front. Mon. Wea. Rev.,121, 1493–1513.

  • Durran, D. R., 1991: The third-order Adams–Bashforth method: An attractive alternative to leapfrog time differencing. Mon. Wea. Rev.,119, 702–720.

  • Gal-Chen, T., and R. C. Somerville, 1975: On the use of a coordinate transform for the solution of the Navier–Stokes equations. J. Comput. Phys.,17, 209–228.

  • Grell, G. A., J. Dudhia, and D. R. Stauffer, 1994: A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5). NCAR Tech. Note, NCAR.TN-398+STR, 122 pp. [Available from NCAR Publications, P.O. Box 3000, Boulder, CO 80307.].

  • Gustafsson, B., H.-O. Kreiss, and J. Oliger, 1995: Time Dependent Problems and Difference Methods. John Wiley and Sons, 642 pp.

  • Haltiner, G. J., and R. T. Williams, 1980: Numerical Prediction and Dynamic Meteorology. John Wiley and Sons, 477 pp.

  • Hoskins, B. J., 1976: Baroclinic waves and frontogenesis. Part I: Introduction and eady waves. Quart. J. Roy. Meteor. Soc.,102, 103–122.

  • Jorgenson, D., 1985: Vertical motions in intense hurricanes. J. Atmos. Sci.,42, 840–856.

  • Kreiss, H.-O., 1980: Problems with different time scales for partial differential equations. Comm. Pure Appl. Math.,33, 399–440.

  • Kuo, J., 1974: Further studies of the parameterization of the influence of cumulus convection on large-scale flow. J. Atmos. Sci.,31, 1232–1240.

  • Kurihara, Y., and M. A. Bender, 1982: Structure and analysis of the eye of a numerically simulated tropical cyclone. J. Meteor. Soc. Japan,60, 381–395.

  • Lee, J. L., and A. E. MacDonald, 2000: QNH: Mesoscale bounded derivative initialization and winter storm test over complex terrain. Mon. Wea. Rev.,128, 1037–1051.

  • MacDonald, A. E., J. L. Lee, and Y. Xie, 2000: On the use of quasi-nonhydrostatic models for mesoscale weather prediction. J. Atmos. Sci.,128, 1037–1051.

  • Mellor, G. L., and T. Yamada, 1974: A hierarchy of turbulence closure models for planetary boundary layers. J. Atmos. Sci.,31, 1791–1806.

  • Oliger, J., and A. Sundstrom, 1978: Theoretical and practical aspects of some initial boundary value problems in fluid dynamics. SIAM J. Appl. Math.,35, 419–446.

  • Orlanski, I., 1975: A rational subdivision of scales for atmospheric processes. Bull. Amer. Meteor. Soc.,56, 527–530.

  • Pielke, R. A., 1974: A three-dimensional numerical model of the sea breezes over South Florida. Mon. Wea. Rev.,102, 115–139.

  • ——, 1984: Mesoscale Meteorological Modeling. Academic Press, 612 pp.

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

  • Skamarock, W. C., and J. B. Klemp, 1994: Efficiency and accuracy of the Klemp–Wilhelmson time-splitting technique. Mon. Wea. Rev.,122, 2623–2630.

  • Smith, R. B., 1980: Linear theory of stratified hydrostatic flow past an isolated mountain. Tellus,32, 348–364.

  • Snook, J. S., 1994: An investigation of Colorado Front Range winter storms using a nonhydrostatic mesoscale numerical model designed for operational use. NOAA Tech. Memo. ERL FSL-10, 373 pp. [Available from Forecast Systems Laboratory, Boulder, CO 80303.].

  • Snyder, C., W. C. Skamarock, and R. Rotunno, 1991: A comparison of primitive-equation and semigeostrophic simulations of baroclinic waves. J. Atmos. Sci.,48, 2179–2194.

  • Stephens, G. L., 1978: Radiation profiles in extended water clouds. Part II: Parameterization schemes. J. Atmos. Sci.,35, 2111–2122.

  • Tao, W.-K., and J. Simpson, 1993: Goddard Cumulus Ensemble Model. Part I: Model description. Terr., Atmos. Oceanic Sci.,4, 35–72.

  • Tapp, M. C., and P. W. White, 1976: A nonhydrostatic mesoscale model. Quart. J. Roy. Meteor. Soc.,102, 277–296.

  • Washington, W. M., and C. L. Parkinson, 1986: An Introduction to Three-Dimensional Climate Modeling. University Science Books, 422 pp.

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QNH: Design and Test of a Quasi-Nonhydrostatic Model for Mesoscale Weather Prediction

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  • 1 NOAA/ERL/Forecast Systems Laboratory, Boulder, Colorado
  • | 2 NOAA/ERL/Forecast Systems Laboratory, Boulder, Colorado, and CIRA, Colorado State University, Foothills Campus, Fort Collins, Colorado
  • | 3 NASA Goddard Institute, New York, New York
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Abstract

A new mesoscale weather prediction model, called QNH, is described. It is characterized by a parameter that multiplies the hydrostatic terms in the vertical equation of motion. Models of this type are referred to generically as “quasi-nonhydrostatic.” The quasi-nonhydrostatic parameter gives the model a character that is essentially nonhydrostatic, but with properties that are theoretically thought to result in smoother, more accurate, and stable predictions. The model is unique in a number of other aspects, such as its treatment of lateral boundary conditions, the use of explicit calculation in the vertical direction, and the use of the bounded derivative theory for initialization. This paper reports on the design and test of the QNH model, which represents the first time the applicability of this type of model has been demonstrated for full-physics, mesoscale weather prediction. The dynamic formulation, discretization, numerical formulation, and physics packages of the model are described. The results of a comprehensive validation of the model are presented. The validation includes barotropic, baroclinic (Eady wave), mountain wave, tropical storm, and sea breeze tests. A simulation of a winter storm (with updated lateral boundary conditions) is presented, which shows that the model has significant skill in forecasting terrain-forced heavy precipitation. It is concluded that the QNH model may be valuable for mesoscale weather prediction.

Corresponding author address: Dr. A. E. MacDonald, NOAA/ERL, R/E/FS, 325 Broadway, Boulder, CO 80523.

Abstract

A new mesoscale weather prediction model, called QNH, is described. It is characterized by a parameter that multiplies the hydrostatic terms in the vertical equation of motion. Models of this type are referred to generically as “quasi-nonhydrostatic.” The quasi-nonhydrostatic parameter gives the model a character that is essentially nonhydrostatic, but with properties that are theoretically thought to result in smoother, more accurate, and stable predictions. The model is unique in a number of other aspects, such as its treatment of lateral boundary conditions, the use of explicit calculation in the vertical direction, and the use of the bounded derivative theory for initialization. This paper reports on the design and test of the QNH model, which represents the first time the applicability of this type of model has been demonstrated for full-physics, mesoscale weather prediction. The dynamic formulation, discretization, numerical formulation, and physics packages of the model are described. The results of a comprehensive validation of the model are presented. The validation includes barotropic, baroclinic (Eady wave), mountain wave, tropical storm, and sea breeze tests. A simulation of a winter storm (with updated lateral boundary conditions) is presented, which shows that the model has significant skill in forecasting terrain-forced heavy precipitation. It is concluded that the QNH model may be valuable for mesoscale weather prediction.

Corresponding author address: Dr. A. E. MacDonald, NOAA/ERL, R/E/FS, 325 Broadway, Boulder, CO 80523.

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