Subseasonal Weather Prediction in a Global Convection-Permitting Model

Nicholas J. Weber Department of Atmospheric Sciences, University of Washington, Seattle, Washington

Search for other papers by Nicholas J. Weber in
Current site
Google Scholar
PubMed
Close
and
Clifford F. Mass Department of Atmospheric Sciences, University of Washington, Seattle, Washington

Search for other papers by Clifford F. Mass in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Although accurate weather and climate prediction beyond one to two weeks is of great value to society, the skill of such extended prediction is limited in current operational global numerical models, whose coarse horizontal grid spacing necessitates the parameterization of atmospheric processes. Of particular concern is the parameterization of convection and specifically convection in the tropics, which impacts global weather at all time scales through atmospheric teleconnections. Convection-permitting models, which forego convective parameterization by explicitly resolving cumulus-scale motions using fine (1–4 km) horizontal grid spacing, can improve global prediction at extended time scales by more faithfully simulating tropical convection and associated teleconnections. This study demonstrates that convection-permitting resolution in a global numerical model can improve both the statistical features of tropical precipitation and extended predictive skill in the tropics and midlatitudes. Comparing four monthlong global simulations with 3-km grid spacing to coarser-resolution simulations that parameterize convection reveals that convection-permitting simulations improve tropical precipitation rates and the diurnal cycle of tropical convection. The propagation of the Madden–Julian oscillation was better predicted in three of the four 3-km simulations; these three runs also featured more skillful prediction of weekly extratropical circulation anomalies, particularly during week 3 of each forecast. These results, though based on a small sample of four cases, demonstrate that convection-permitting global modeling can benefit extended atmospheric prediction and offers the potential for improved operational subseasonal forecast skill.

© 2019 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: Nicholas J. Weber, njweber2@atmos.washington.edu

A supplement to this article is available online (10.1175/BAMS-D-18-0210.2)

Abstract

Although accurate weather and climate prediction beyond one to two weeks is of great value to society, the skill of such extended prediction is limited in current operational global numerical models, whose coarse horizontal grid spacing necessitates the parameterization of atmospheric processes. Of particular concern is the parameterization of convection and specifically convection in the tropics, which impacts global weather at all time scales through atmospheric teleconnections. Convection-permitting models, which forego convective parameterization by explicitly resolving cumulus-scale motions using fine (1–4 km) horizontal grid spacing, can improve global prediction at extended time scales by more faithfully simulating tropical convection and associated teleconnections. This study demonstrates that convection-permitting resolution in a global numerical model can improve both the statistical features of tropical precipitation and extended predictive skill in the tropics and midlatitudes. Comparing four monthlong global simulations with 3-km grid spacing to coarser-resolution simulations that parameterize convection reveals that convection-permitting simulations improve tropical precipitation rates and the diurnal cycle of tropical convection. The propagation of the Madden–Julian oscillation was better predicted in three of the four 3-km simulations; these three runs also featured more skillful prediction of weekly extratropical circulation anomalies, particularly during week 3 of each forecast. These results, though based on a small sample of four cases, demonstrate that convection-permitting global modeling can benefit extended atmospheric prediction and offers the potential for improved operational subseasonal forecast skill.

© 2019 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: Nicholas J. Weber, njweber2@atmos.washington.edu

A supplement to this article is available online (10.1175/BAMS-D-18-0210.2)

Save
  • Bechtold, P., N. Semane, and P. Lopez, 2014: Representing equilibrium and nonequilibrium convection in large-scale models. J. Atmos. Sci., 71, 734753, https://doi.org/10.1175/JAS-D-13-0163.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brunet, G., and Coauthors, 2010: Collaboration of the weather and climate communities to advance subseasonal-to-seasonal prediction. Bull. Amer. Meteor. Soc., 91, 13971406, https://doi.org/10.1175/2010BAMS3013.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chu, P. C., 1999: Two kinds of predictability in the Lorenz system. J. Atmos. Sci., 56, 14271432, https://doi.org/10.1175/1520-0469(1999)056<1427:TKOPIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davis, C. A., K. W. Manning, R. E. Carbone, S. B. Trier, and J. D. Tuttle, 2003: Coherence of warm-season continental rainfall in numerical weather prediction models. Mon. Wea. Rev., 131, 26672679, https://doi.org/10.1175/1520-0493(2003)131<2667:COWCRI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gottschalck, J., P. E. Roundy, C. J. Schreck III, A. Vintzileos, and C. Zhang, 2013: Large-scale atmospheric and oceanic conditions during the 2011–12 DYNAMO field campaign. Mon. Wea. Rev., 141, 41734196, https://doi.org/10.1175/MWR-D-13-00022.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hirons, L. C., N. P. Klingaman, and S. J. Woolnough, 2018: The impact of air-sea interactions on the representation of tropical precipitation extremes. J. Adv. Model. Earth Syst., 10, 550559, https://doi.org/10.1002/2017MS001252.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holloway, C. E., S. J. Woolnough, and G. M. S. Lister, 2012: Precipitation distributions for explicit versus parametrized convection in a large-domain high-resolution tropical case study. Quart. J. Roy. Meteor. Soc., 138, 16921708, https://doi.org/10.1002/qj.1903.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Inoue, R., M. Satoh, H. Miura, and B. Mapes, 2008: Characteristics of cloud size of deep convection simulated by a global cloud resolving model over the western tropical Pacific. J. Meteor. Soc. Japan, 86A, 115, https://doi.org/10.2151/jmsj.86A.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kerns, B. W., and S. S. Chen, 2014: ECMWF and GFS model forecast verification during DYNAMO: Multiscale variability in MJO initiation over the equatorial Indian Ocean. J. Geophys. Res. Lett., 119, 37363755, https://doi.org/10.1002/2013JD020833.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, H.-M., P. J. Webster, V. E. Toma, and D. Kim, 2014: Predictability and prediction skill of the MJO in two operational forecasting systems. J. Climate, 27, 53645378, https://doi.org/10.1175/JCLI-D-13-00480.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, S., and A. W. Robertson, 2015: Evaluation of submonthly precipitation forecast skill from global ensemble prediction systems. Mon. Wea. Rev., 143, 28712889, https://doi.org/10.1175/MWR-D-14-00277.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, J.-L., and Coauthors, 2006: Tropical intraseasonal variability in 14 IPCC AR4 climate models. Part I: convective signals. J. Climate, 19, 26652690, https://doi.org/10.1175/JCLI3735.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lorenz, E. N., 1963: Deterministic nonperiodic flow. J. Atmos. Sci., 20, 130141, https://doi.org/10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Madden, R. A., and P. R. Julian, 1972: Description of global-scale circulation cells in the tropics with a 40–50 day period. J. Atmos. Sci., 29, 11091123, https://doi.org/10.1175/1520-0469(1972)029<1109:DOGSCC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miura, H., M. Satoh, T. Nasuno, A. T. Noda, and K. Oouchi, 2007: A Madden-Julian oscillation event realistically simulated by a global cloud-resolving model. Science, 318, 17631765, https://doi.org/10.1126/science.1148443.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mori, M., and M. Watanabe, 2008: The growth and triggering mechanisms of the PNA: A MJO-PNA coherence. J. Meteor. Soc. Japan, 86, 213236, https://doi.org/10.2151/jmsj.86.213.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prein, A. F., and Coauthors, 2015: A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges. Rev. Geophys., 53, 323361, https://doi.org/10.1002/2014RG000475.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saha, S., and Coauthors, 2014: The Climate Forecast System version 2. J. Climate, 27, 21852208, https://doi.org/10.1175/JCLI-D-12-00823.1.

  • Sardeshmukh, P. D., and B. J. Hoskins, 1988: The generation of global rotational flow by steady idealized tropical divergence. J. Atmos. Sci., 45, 12281251, https://doi.org/10.1175/1520-0469(1988)045<1228:TGOGRF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sato, T., H. Miura, M. Satoh, Y. N. Takayabu, and Y. Wang, 2009: Diurnal cycle of precipitation in the tropics simulated in a global cloud-resolving model. J. Climate, 22, 48094826, https://doi.org/10.1175/2009JCLI2890.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seo, K.-H., W. Wang, J. Gottschalck, Q. Zhang, J. E. Schemm, W. R. Higgins, and A. Kumar, 2009: Evaluation of MJO forecast skill from several statistical and dynamical forecast models. J. Climate, 22, 23722388, https://doi.org/10.1175/2008JCLI2421.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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 tessellations and C-grid staggering. Mon. Wea. Rev., 140, 30903105, https://doi.org/10.1175/MWR-D-11-00215.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, S. A., J. D. Doyle, A. R. Brown, and S. Webster, 2006: Sensitivity of resolved mountain drag to model resolution for MAP case-studies. Quart. J. Roy. Meteor. Soc., 132, 14671487, https://doi.org/10.1256/qj.05.67.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stephens, G., and Coauthors, 2010: Dreary state of precipitation in global models. J. Geophys. Res., 115, D24211, https://doi.org/10.1029/2010JD014532.

    • Search Google Scholar
    • Export Citation
  • Vitart, F., 2014: Evolution of ECMWF sub-seasonal forecast skill scores. Quart. J. Roy. Meteor. Soc., 140, 18891899, https://doi.org/10.1002/qj.2256.

  • Vitart, F., 2017: Madden-Julian oscillation prediction and teleconnections in the S2S database. Quart. J. Roy. Meteor. Soc., 143, 22102220, https://doi.org/10.1002/qj.3079.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vitart, F., and A. Robertson, 2018: The sub-seasonal to Seasonal Prediction Project (S2S) and the prediction of extreme events. npj Climate Atmos. Sci., 1, 3, https://doi.org/10.1038/s41612-018-0013-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wallace, J. M., and D. S. Gutzler, 1981: Teleconnections in the geopotential height field during the Northern Hemisphere winter. Mon. Wea. Rev., 109, 784812, https://doi.org/10.1175/1520-0493(1981)109<0784:TITGHF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weaver, S.-J., W. Wang, M. Chen, and A. Kumar, 2011: Representation of MJO variability in the NCEP Climate Forecast System. J. Climate, 24, 46764694, https://doi.org/10.1175/2011JCLI4188.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weber, N., and C. Mass, 2017: Evaluating CFSv2 subseasonal forecast skill with an emphasis on tropical convection. Mon. Wea. Rev., 145, 37953815, https://doi.org/10.1175/MWR-D-17-0109.1.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, C., 2013: Madden-Julian oscillation: Bridging weather and climate. Bull. Amer. Meteor. Soc., 94, 18491870, https://doi.org/10.1175/BAMS-D-12-00026.1.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1073 200 20
PDF Downloads 755 140 19