Forecasting Convection with a “Scale-Aware” Tiedtke Cumulus Parameterization Scheme at Kilometer Scales

Wei Wang aNational Center for Atmospheric Research, Boulder, Colorado

Search for other papers by Wei Wang in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

A scale-aware convective parameterization based on the Tiedtke scheme is developed and tested in the Weather Research and Forecasting (WRF) Model and the Model for Prediction Across Scales (MPAS) for a few convective cases at grid sizes in the ranges of 1.5–4.5 km. These tests demonstrate that the scale-aware scheme effectively reduces the outcome of deep convection by decreasing the convective portion of the total surface precipitation. When compared to the model runs that use microphysics without the cumulus parameterization at these grid sizes, the modified Tiedtke scheme is shown to improve some aspects of the precipitation forecasts. When the scheme is applied on a variable mesh in MPAS, it handles the convection across the mesh transition zones smoothly.

Significance Statement

Representing convection accounting for variations in the size of grid mesh is crucial in numerical models with variable resolutions, and in precipitation events where convection is not well depicted even by a model mesh of a few kilometers. Many convective parameterizations have already considered this grid-size dependency. This paper fills a gap by applying the same concept to a different convective parameterization, and evaluating it in a few precipitation forecast scenarios.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Wei Wang, weiwang@ucar.edu

Abstract

A scale-aware convective parameterization based on the Tiedtke scheme is developed and tested in the Weather Research and Forecasting (WRF) Model and the Model for Prediction Across Scales (MPAS) for a few convective cases at grid sizes in the ranges of 1.5–4.5 km. These tests demonstrate that the scale-aware scheme effectively reduces the outcome of deep convection by decreasing the convective portion of the total surface precipitation. When compared to the model runs that use microphysics without the cumulus parameterization at these grid sizes, the modified Tiedtke scheme is shown to improve some aspects of the precipitation forecasts. When the scheme is applied on a variable mesh in MPAS, it handles the convection across the mesh transition zones smoothly.

Significance Statement

Representing convection accounting for variations in the size of grid mesh is crucial in numerical models with variable resolutions, and in precipitation events where convection is not well depicted even by a model mesh of a few kilometers. Many convective parameterizations have already considered this grid-size dependency. This paper fills a gap by applying the same concept to a different convective parameterization, and evaluating it in a few precipitation forecast scenarios.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Wei Wang, weiwang@ucar.edu
Save
  • Arakawa, A., and C.-M. Wu, 2013: A unified representation of deep moist convection in numerical modeling of the atmosphere. Part I. J. Atmos. Sci., 70, 19771992, https://doi.org/10.1175/JAS-D-12-0330.1.

    • Search Google Scholar
    • Export Citation
  • Bassill, N. P., 2015: An analysis of the operational GFS simplified Arakawa–Schubert parameterization within a WRF framework: A Hurricane Sandy (2012) long-term track forecast perspective. J. Geophys. Res. Atmos., 120, 379398, https://doi.org/10.1002/2014JD022211.

    • Search Google Scholar
    • Export Citation
  • Bechtold, P., M. Köhler, T. Jung, F. Doblas-Reyes, M. Leutbecher, M. J. Rodwell, F. Vitart, and G. Balsamo, 2008: Advances in simulating atmospheric variability with the ECMWF model: From synoptic to decadal time-scales. Quart. J. Roy. Meteor. Soc., 134, 13371351, https://doi.org/10.1002/qj.289.

    • Search Google Scholar
    • Export Citation
  • Bechtold, P., N. Semane, P. Lopez, J.-P. Chaboureau, A. Beljaars, and N. Bormann, 2014: Representing equilibrium and non-equilibrium convection in large-scale models. J. Atmos. Sci., 71, 734753, https://doi.org/10.1175/JAS-D-13-0163.1.

    • Search Google Scholar
    • Export Citation
  • Bryan, G. H., and H. Morrison, 2012: Sensitivity of a simulated squall line to horizontal resolution and parameterization of microphysics. Mon. Wea. Rev., 140, 202225, https://doi.org/10.1175/MWR-D-11-00046.1.

    • Search Google Scholar
    • Export Citation
  • Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129, 569585, https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Dipankar, A., and Coauthors, 2020: SINGV: A convective-scale weather forecast model for Singapore. Quart. J. Roy. Meteor. Soc., 146, 41314146, https://doi.org/10.1002/qj.3895.

    • Search Google Scholar
    • Export Citation
  • Done, J., C. A. Davis, and M. L. Weisman, 2004: The next generation of NWP: Explicit forecasts of convection using the Weather Research and Forecast (WRF) model. Atmos. Sci. Lett., 5, 110117, https://doi.org/10.1002/asl.72.

    • Search Google Scholar
    • Export Citation
  • Done, J., G. C. Craig, S. L. Gray, P. A. Clark, and M. E. B. Gray, 2006: Mesoscale simulations of organized convection: Importance of convective equilibrium. Quart. J. Roy. Meteor. Soc., 132, 737756, https://doi.org/10.1256/qj.04.84.

    • Search Google Scholar
    • Export Citation
  • Dudhia, J., 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, 3344, https://doi.org/10.2151/jmsj.86A.33.

    • Search Google Scholar
    • Export Citation
  • ECMWF, 2014: IFS documentation CY40R1—Part IV: Physical processes. ECMWF, 190 pp., https://doi.org/10.21957/f56vvey1x.

  • ECMWF, 2016: IFS documentation CY43R1—Part IV: Physical processes. ECMWF, 223 pp., https://doi.org/10.21957/sqvo5yxja.

  • Fowler, L. D., W. C. Skamarock, G. A. Grell, S. R. Freitas, and M. G. Duda, 2016: Analyzing the Grell–Freitas convection scheme from hydrostatic to nonhydrostatic scales within a global model. Mon. Wea. Rev., 144, 22852306, https://doi.org/10.1175/MWR-D-15-0311.1.

    • Search Google Scholar
    • Export Citation
  • Fowler, L. D., M. C. Barth, and K. Alapaty, 2020: Impact of scale-aware deep convection on the cloud liquid and ice water paths and precipitation using the Model for Prediction Across Scales (MPAS-v5.2). Geosci. Model Dev., 13, 28512877, https://doi.org/10.5194/gmd-13-2851-2020.

    • Search Google Scholar
    • Export Citation
  • Fox-Rabinovitz, M. S., E. H. Berbery, L. L. Takacs, and R. C. Govindaraju, 2005: A multiyear ensemble simulation of the U.S. climate with a stretched-grid GCM. Mon. Wea. Rev., 133, 25052525, https://doi.org/10.1175/MWR2956.1.

    • Search Google Scholar
    • Export Citation
  • Gregory, D., and P. R. Rowntree, 1990: A mass flux convection scheme with representation of cloud ensemble characteristics and stability-dependent closure. Mon. Wea. Rev., 118, 14831506, https://doi.org/10.1175/1520-0493(1990)118<1483:AMFCSW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Grell, G. A., and S. R. Freitas, 2014: A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling. Atmos. Chem. Phys., 14, 52335250, https://doi.org/10.5194/acp-14-5233-2014.

    • Search Google Scholar
    • Export Citation
  • 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, https://doi.org/10.1175/MWR3199.1.

    • Search Google Scholar
    • Export Citation
  • Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103, https://doi.org/10.1029/2008JD009944.

    • Search Google Scholar
    • Export Citation
  • Janjić, Z. I., 1994: The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer and turbulence closure schemes. Mon. Wea. Rev., 122, 927945. https://doi.org/10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Judt, F., and Coauthors, 2021: Tropical cyclones in global storm-resolving models. J. Meteor. Soc. Japan, 99, 579602, https://doi.org/10.2151/jmsj.2021-029.

    • Search Google Scholar
    • Export Citation
  • Kain, J. S., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kuo, Y.-H., R. J. Reed, and Y.-B. Liu, 1996: The ERICA IOP 5 storm. Part III: Mesoscale cyclogenesis and precipitation parameterization. Mon. Wea. Rev., 124, 14091434, https://doi.org/10.1175/1520-0493(1996)124<1409:TEISPI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kwon, Y. C., and S. Hong, 2017: A mass-flux cumulus parameterization scheme across gray-zone resolutions. Mon. Wea. Rev., 145, 583598, https://doi.org/10.1175/MWR-D-16-0034.1.

    • Search Google Scholar
    • Export Citation
  • Malardel, S., and P. Bechtold, 2019: The coupling of deep convection with the resolved flow via the divergence of mass flux in the IFS. Quart. J. Roy. Meteor. Soc., 145, 18321845, https://doi.org/10.1002/qj.3528.

    • Search Google Scholar
    • Export Citation
  • Nakanishi, M., and H. Niino, 2009: Development of an improved turbulence closure model for the atmospheric boundary layer. J. Meteor. Soc. Japan, 87, 895912, https://doi.org/10.2151/jmsj.87.895.

    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., G. S. Romine, R. A. Sobash, K. R. Fossell, and M. L. Weisman, 2015: NCAR’s experimental real-time convection-allowing ensemble prediction system. Wea. Forecasting, 30, 16451654, https://doi.org/10.1175/WAF-D-15-0103.1.

    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., G. S. Romine, R. A. Sobash, K. R. Fossell, and M. L. Weisman, 2019: NCAR’s real-time convection-allowing ensemble project. Bull. Amer. Meteor. Soc., 100, 321343, https://doi.org/10.1175/BAMS-D-17-0297.1.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., J. B. Klemp, L. D. Fowler, M. G. Duda, 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, 30903105, https://doi.org/10.1175/MWR-D-11-00215.1.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and Coauthors, 2019: A description of the Advanced Research WRF Model version 4. NCAR Tech. Note NCAR/TN-556+STR, 145 pp., https://doi.org/10.5065/1dfh-6p97.

    • Search Google Scholar
    • Export Citation
  • Stevens, B., and Coauthors, 2019: DYAMOND: The DYnamics of the atmospheric general circulation modeled on non-hydrostatic domains. Prog. Earth Planet. Sci., 6, 61, https://doi.org/10.1186/s40645-019-0304-z.

    • 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, https://doi.org/10.1175/2008MWR2387.1.

    • Search Google Scholar
    • Export Citation
  • Tiedtke, M., 1989: A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon. Wea. Rev., 117, 17791800, https://doi.org/10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wang, W., D. Ahijevych, C. Davis, and W. Skamarock, 2018: Performance of MPAS for tropical cyclone prediction in 2016 and 2017 seasons. 33rd Conf. on Hurricanes and Tropical Meteorology, 90, Ponte Vedra Beach, FL, Amer. Meteor. Soc., https://ams.confex.com/ams/33HURRICANE/webprogram/Paper340177.html.

    • Search Google Scholar
    • Export Citation
  • Weisman, M. L., W. C. Skamarock, and J. B. Klemp, 1997: The resolution dependence of explicitly modeled convective systems. Mon. Wea. Rev., 125, 527548, https://doi.org/10.1175/1520-0493(1997)125<0527:TRDOEM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Weisman, M. L., C. A. Davis, W. Wang, K. W. Manning, and J. B. Klemp, 2008: Experiences with 0–36-h explicit convective forecasts with the WRF-ARW model. Wea. Forecasting, 23, 407437, https://doi.org/10.1175/2007WAF2007005.1.

    • Search Google Scholar
    • Export Citation
  • Wilt, B., and W. Wang, 2020: IBM GRAF—Scale-aware convective forecast evaluation and improvements. 30th Conf. on Weather Analysis and Forecasting (WAF)/26th Conf. on Numerical Weather Prediction (NWP), 8A.5, Boston, MA, Amer. Meteor. Soc., https://ams.confex.com/ams/2020Annual/webprogram/Paper369291.html.

    • Search Google Scholar
    • Export Citation
  • Xue, M., and W. J. Martin, 2006: A high-resolution modeling study of the 24 May 2002 dryline case during IHOP. Part I: Numerical simulation and general evolution of the dryline and convection. Mon. Wea. Rev., 134, 149171, https://doi.org/10.1175/MWR3071.1.

    • Search Google Scholar
    • Export Citation
  • Yeh, K.-S., J. Côté, S. Gravel, A. Méthot, A. Patoine, M. Roch, and A. Staniforth, 2002: The CMC–MRB Global Environmental Multiscale (GEM) model. Part III: Nonhydrostatic formulation. Mon. Wea. Rev., 130, 339356, https://doi.org/10.1175/1520-0493(2002)130<0339:TCMGEM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Zarzycki, C. M., C. Jablonowski, and M. A. Taylor, 2014: Using variable-resolution meshes to model tropical cyclones in the Community Atmosphere Model. Mon. Wea. Rev., 142, 12211239, https://doi.org/10.1175/MWR-D-13-00179.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, C., and Y. Wang, 2017: Projected future changes of tropical cyclone activity over the western North and South Pacific in a 20-km-mesh regional climate model. J. Climate, 30, 59235941, https://doi.org/10.1175/JCLI-D-16-0597.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, D., K. Gao, 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, 960994, https://doi.org/10.1175/1520-0493(1989)117<0960:NSOAIS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Zhang, G. J., J. Fan, and K. M. Xu, 2015: Comments on “A unified representation of deep moist convection in numerical modeling of the atmosphere. Part I.” J. Atmos. Sci., 72, 25622565, https://doi.org/10.1175/JAS-D-14-0246.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, J., and Coauthors, 2016: Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation: Initial operating capabilities. Bull. Amer. Meteor. Soc., 97, 621638, https://doi.org/10.1175/BAMS-D-14-00174.1.

    • Search Google Scholar
    • Export Citation
  • Zheng, Y., K. Alapaty, J. A. Herwehe, A. D. Del Genio, and D. Niyogi, 2016: Improving high-resolution weather forecasts using the Weather Research and Forecasting (WRF) Model with an updated Kain–Fritsch scheme. Mon. Wea. Rev., 144, 833860, https://doi.org/10.1175/MWR-D-15-0005.1.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 849 0 0
Full Text Views 2005 1300 304
PDF Downloads 1604 899 75