A Comparison Study of Convective Parameterization Schemes in a Mesoscale Model

Wei Wang Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania

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Nelson L. Seaman Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania

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Abstract

A comparison study of four cumulus parameterization schemes (CPSs), the Anthes–Kuo, Betts–Miller, Grell, and Kain–Fritsch schemes, is conducted using The Pennsylvania State University–National Center for Atmospheric Research mesoscale model. Performance of these CPSs is examined using six precipitation events over the continental United States for both cold and warm seasons. Grid resolutions of 36 and 12 km are chosen to represent current mesoscale research models and future operational models. The key parameters used to evaluate skill include precipitation, sea level pressure, wind, and temperature predictions. Precipitation is evaluated statistically using conventional skill scores (such as threat and bias scores) for different threshold values based on hourly rainfall observations. Rainfall and other mesoscale features are also evaluated by careful examination of analyzed and simulated fields, which are discussed in the context of timing, evolution, intensity, and structure of the precipitation systems.

It is found that the general 6-h precipitation forecast skill for these schemes is fairly good in predicting four out of six cases examined in this study, even for higher thresholds. The forecast skill is generally higher for cold-season events than for warm-season events. There is an increase in the forecast skill in the 12-km model, and the gain is most obvious in predicting heavier rainfall amounts. The model’s precipitation forecast skill is better in rainfall volume than in either the areal coverage or the peak amount. The scheme with the convective available potential energy–based closure assumption (Kain–Fritsch scheme) appears to perform better. Some systematic behaviors associated with various schemes are also noted wherever possible.

The partition of rainfall into subgrid scale and grid scale is sensitive to the particular parameterization scheme chosen, but relatively insensitive to either the model grid sizes or the convective environments.

The prediction of mesoscale surface features in warm-season cases, such as mesoscale pressure centers, wind-shift lines (gust fronts), and temperature fields, strongly suggests that the CPSs with moist downdrafts are able to predict these surface features more accurately.

* Current affiliation: National Center for Atmospheric Research, Boulder, Colorado.

Corresponding author address: Dr. Wei Wang, National Center for Atmospheric Research, Mail Stop: MMM, P.O. Box 3000/Foothills Lab. 3, Boulder, CO 80307-3000.

Email: weiwang@ncar.ucar.edu

Abstract

A comparison study of four cumulus parameterization schemes (CPSs), the Anthes–Kuo, Betts–Miller, Grell, and Kain–Fritsch schemes, is conducted using The Pennsylvania State University–National Center for Atmospheric Research mesoscale model. Performance of these CPSs is examined using six precipitation events over the continental United States for both cold and warm seasons. Grid resolutions of 36 and 12 km are chosen to represent current mesoscale research models and future operational models. The key parameters used to evaluate skill include precipitation, sea level pressure, wind, and temperature predictions. Precipitation is evaluated statistically using conventional skill scores (such as threat and bias scores) for different threshold values based on hourly rainfall observations. Rainfall and other mesoscale features are also evaluated by careful examination of analyzed and simulated fields, which are discussed in the context of timing, evolution, intensity, and structure of the precipitation systems.

It is found that the general 6-h precipitation forecast skill for these schemes is fairly good in predicting four out of six cases examined in this study, even for higher thresholds. The forecast skill is generally higher for cold-season events than for warm-season events. There is an increase in the forecast skill in the 12-km model, and the gain is most obvious in predicting heavier rainfall amounts. The model’s precipitation forecast skill is better in rainfall volume than in either the areal coverage or the peak amount. The scheme with the convective available potential energy–based closure assumption (Kain–Fritsch scheme) appears to perform better. Some systematic behaviors associated with various schemes are also noted wherever possible.

The partition of rainfall into subgrid scale and grid scale is sensitive to the particular parameterization scheme chosen, but relatively insensitive to either the model grid sizes or the convective environments.

The prediction of mesoscale surface features in warm-season cases, such as mesoscale pressure centers, wind-shift lines (gust fronts), and temperature fields, strongly suggests that the CPSs with moist downdrafts are able to predict these surface features more accurately.

* Current affiliation: National Center for Atmospheric Research, Boulder, Colorado.

Corresponding author address: Dr. Wei Wang, National Center for Atmospheric Research, Mail Stop: MMM, P.O. Box 3000/Foothills Lab. 3, Boulder, CO 80307-3000.

Email: weiwang@ncar.ucar.edu

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  • Anthes, R. A., 1977: A cumulus parameterization scheme utilizing a one-dimensional cloud model. Mon. Wea. Rev.,105, 270–286.

  • ——, 1983: Regional models of the atmosphere in middle latitudes. Mon. Wea. Rev.,111, 1306–1335.

  • Arakawa, A., 1993: Closure assumptions in the cumulus parameterization problem. The Representation of Cumulus Convection in Numerical Models, Meteor. Monogr., No. 46, Amer. Meteor. Soc., 1–15.

  • ——, and W. H. Schubert, 1974: Interaction of a cumulus cloud ensemble with the large-scale environment. Part I. J. Atmos. Sci.,31, 674–701.

  • Bartels, D. L., and R. A. Maddox, 1991: Midlevel cyclonic vortices generated by mesoscale convective systems. Mon. Wea. Rev.,119, 104–118.

  • Betts A. K., 1986: A new convective adjustment scheme. Part I: Observational and theoretical basis. Quart. J. Roy. Meteor. Soc.,112, 677–692.

  • ——, 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.

  • ——, and ——, 1993: The Betts–Miller scheme. The Representation of Cumulus Convection in Numerical Models, Meteor. Monogr., No. 46, Amer. Meteor. Soc., 107–121.

  • Bougeault, P., and J. F. Geleyn, 1989: Some problems of closure assumption and scale dependency in the parameterization of moist deep convection for numerical weather prediction. Meteor. Atmos. Phys.,40, 123–135.

  • Brandes, E. A., 1990: Evolution and structure of the 6–7 May 1985 mesoscale convective system and associated vortex. Mon. Wea. Rev.,118, 109–127.

  • Bresch, J. F., 1994: Numerical simulation and analysis of a series of mesoscale convective systems. Ph.D. dissertation, Colorado State University, 285 pp.

  • Brown, J. M., 1979: Mesoscale unsaturated downdrafts driven by rainfall evaporation: A numerical study. J. Atmos. Sci.,36, 313–338.

  • Chen, S.-Y., and W. M. Frank, 1993: A numerical study of the genesis of extratropical convective mesovortices. Part I: Evolution and dynamics. J. Atmos. Sci.,50, 2401–2426.

  • Cram, J. M., R. A. Pielke, and W. R. Cotton, 1992: Numerical simulation and analysis of a prefrontal squall line. Part I: Observations and basic simulation results. J. Atmos. Sci.,49, 189–208.

  • 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.

  • ——, 1993: A nonhydrostatic version of the Penn State–NCAR mesoscale model: Validation tests and simulation of an Atlantic cyclone and cold front. Mon. Wea. Rev.,121, 1493–1513.

  • Emanuel, K. A., and D. J. Raymond, Eds., 1993: The Representation of Cumulus Convection in Numerical Models. Meteor. Monogr., No. 46, Amer. Meteor. Soc., 246 pp.

  • Frank, W. M., 1983: The cumulus parameterization problem. Mon. Wea. Rev.,111, 1859–1871.

  • ——, and C. Cohen, 1987: Simulation of tropical convective systems. Part I: A cumulus parameterization. J. Atmos. Sci.,44, 3787–3799.

  • Fritsch, J. M., and C. F. Chappell, 1980: Numerical prediction of convectively driven mesoscale pressure systems. Part I: Convective parameterization. J. Atmos. Sci.,37, 1722–1733.

  • ——, and R. A. Maddox, 1981: Convectively driven mesoscale weather systems aloft. Part II: Numerical simulations. J. Appl. Meteor.,20, 20–26.

  • ——, and K. F. Heideman, 1989: Some characteristics of the Limited-Area Fine-Mesh (LFM) model quantitative precipitation forecasts (QPF) during the 1982 and 1983 warm seasons. Wea. Forecasting,4, 173–185.

  • ——, R. J. Kane, and C. R. Chelius, 1986: The contribution of mesoscale convective weather systems to the warm-season precipitation in the United States. J. Appl. Meteor.,25, 1334–1345.

  • Grell, G. A., 1993: Prognostic evaluation of assumptions used by cumulus parameterizations. Mon. Wea. Rev.,121, 764–787.

  • ——, Y.-H. Kuo, and R. J. Pasch, 1991: Semiprognostic tests of cumulus parameterization schemes in the middle latitudes. Mon. Wea. Rev.,119, 5–31.

  • ——, 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, 138 pp. [Available from NCAR Information Support Services, P.O. Box 3000, Boulder, CO 80307.].

  • Haagenson, P. L., D. O. Gill, and Y.-H. Kuo, 1992: Real-time forecasts for WISP-91 using the Penn State/NCAR mesoscale model. NCAR Tech. Note NCAR/TN-380+STR, 42 pp. [Available from NCAR Information Support Services, P.O. Box 3000, Boulder, CO 80307.].

  • 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.

  • Johnson, R. H., and P. J. Hamilton, 1988: The relationship of surface pressure features to the precipitation and air flow structure of an intense midlatitude squall line. Mon. Wea. Rev.,116, 1444–1472.

  • Kain, J. S., 1994: Interactions between parameterized convection and grid-scale circulations in a mesoscale model. Ph.D. dissertation, The Pennsylvania State University, 143 pp.

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

  • ——, and ——, 1992: The role of convective “trigger function” in numerical forecasts of mesoscale convective systems. Meteor. Atmos. Phys.,49, 93–106.

  • ——, and ——, 1993: Convective parameterization for mesoscale models: The Kain–Fritsch scheme. The Representation of Cumulus Convection in Numerical Models, Meteor. Monogr., No. 46, Amer. Meteor. Soc., 165–170.

  • Kane, R. J., Jr., C. R. Chelius, and J. M. Fritsch, 1987: Precipitation characteristics of mesoscale convective weather systems. J. Climate Appl. Meteor.,26, 1345–1357.

  • Kreitzberg, C. W., and D. Perkey, 1976: Release of potential instability. Part I: A sequential plume model within a hydrostatic primitive equation model. J. Atmos. Sci.,33, 456–475.

  • 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.

  • Kuo, Y.-H., and S. Low-Nam, 1990: Prediction of nine explosive cyclones over the western Atlantic Ocean with a regional model. Mon. Wea. Rev.,118, 3–25.

  • ——, R. J. Reed, and Y.-B. Liu, 1996: The ERICA IOP 5 storm. Part III: Mesoscale cyclogenesis and precipitation parameterization. Mon. Wea. Rev.,124, 1409–1434.

  • Locatelli, J. D., J. E. Martin, J. A. Castle, and P. V. Hobbs, 1995: Structure and evolution of winter cyclones in the central United States and their effects on distribution of precipitation. Part III: The development of a squall line associated with weak cold frontogenesis aloft. Mon. Wea. Rev.,123, 2641–2662.

  • Maddox, R. A., 1980: Mesoscale convective complexes. Bull. Amer. Meteor. Soc.,61, 1374–1387.

  • Manning, K. W., and P. L. Haagenson, 1992: Date ingest and objective analysis for the PSU/NCAR modeling system: Programs DATAGRID and RAWINS. NCAR Tech. Note NCAR/TN-376+IA, 209 pp. [Available from NCAR Information Support Services, P.O. Box 3000, Boulder, CO 80307.].

  • McAnelly, R. L., and W. R. Cotton, 1986: Meso-β-scale characteristics of an episode of meso-α-scale convective complexes. Mon. Wea. Rev.,114, 1740–1770.

  • Molinari, J., and T. Corsetti, 1985: Incorporation of cloud-scale and mesoscale downdraft into a cumulus parameterization: Results of one- and three-dimensional integrations. Mon. Wea. Rev.,113, 485–501.

  • Olson, D. A., N. W. Junker, and B. Korty, 1995: Evaluation of 33 years of quantitative precipitation forecasting at the NMC. Wea. Forecasting,10, 498–511.

  • Rutledge, S. A., R. A. Houze Jr., M. I. Biggerstaff, and T. Matejka, 1988: The Oklahoma–Kansas mesoscale convective system of 10–11 June 1985: Precipitation structure and single Doppler radar analysis. Mon. Wea. Rev.,116, 1409–1430.

  • Smull, B. F., and R. A. Houze Jr., 1985: A midlatitude squall line with a trailing region of stratiform rain: Radar and satellite observations. Mon. Wea. Rev.,113, 117–133.

  • ——, and J. A. Augustine, 1993: Multiscale analysis of a mature mesoscale convective complex. Mon. Wea. Rev.,121, 103–132.

  • Stensrud, D. J., and J. M. Fritsch, 1994: Mesoscale convective systems in weakly forced large-scale environments. Part III: Numerical simulations and implications for operational forecasting. Mon. Wea. Rev.,122, 2084–2104.

  • Tremback, C. J., 1990: Numerical simulation of a mesoscale convective complex: Model development and numerical results. Ph.D. dissertation, Colorado State University, 247 pp.

  • Trier, S. B., and D. B. Parsons, 1993: Evolution of environmental conditions preceding the development of a nocturnal mesoscale convective complex. Mon. Wea. Rev.,121, 1078–1098.

  • Wang, P.-Y., J. E. Martin, J. D. Locatelli, and P. V. Hobbs, 1995: Structure and evolution of winter cyclones in the central United States and their effects on distribution of precipitation. Part II: Arctic fronts. Mon. Wea. Rev.,123, 1328–1344.

  • Weisman, L. M., J. B. Klemp, and W. C. Skamarock, 1991: The resolution dependence of explicitly-modeled convection. Preprints, Ninth Conf. on Numerical Weather Prediction, Denver, CO, Amer. Meteor. Soc., 38–41.

  • 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. Meteor.,21, 1594–1609.

  • ——, and J. M. Fritsch, 1986: A case study of the sensitivity of numerical simulation of mesoscale convective systems to varying initial conditions. Mon. Wea. Rev.,114, 2418–2431.

  • ——, and K. Gao, 1989: Numerical simulation of an intense squall line during 10–11 June 1985 PRE-STORM. Part II: Rear inflow, surface pressure perturbations and stratiform precipitation. Mon. Wea. Rev.,117, 2067–2094.

  • ——, ——, and D. B. Parsons, 1989: Numerical simulation of an intense squall line during 10–11 June 1985 PRE-STORM. Part I: Model verification. Mon. Wea. Rev.,117, 960–994.

  • Zheng, Y., 1993: The observational study and numerical simulations of the 7 May 1985 mesoscale convective system. M.S. thesis, Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, 106 pp.

  • ——, Q. Xu, and D. J. Stensrud, 1995: A numerical simulation of the 7 May 1985 mesoscale convective system. Mon. Wea. Rev.,123, 1781–1799.

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