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Diagnosing added value of convection-permitting regional models using precipitation event identification and tracking

  • Won Chang
  • Jiali Wang
  • Julian Marohnic
  • V. Rao Kotamarthi
  • Elisabeth J. Moyer
Article

Abstract

Dynamical downscaling with high-resolution regional climate models may offer the possibility of realistically reproducing precipitation and weather events in climate simulations. As resolutions fall to order kilometers, the use of explicit rather than parametrized convection may offer even greater fidelity. However, these increased resolutions both allow and require increasingly complex diagnostics for evaluating model fidelity. In this study we focus on precipitation evaluation and analyze five 2-month-long dynamically downscaled model runs over the continental United States that employ different convective and microphysics parameterizations, including one high-resolution convection-permitting simulation. All model runs use the Weather Research and Forecasting Model driven by National Center for Environmental Prediction reanalysis data. We show that employing a novel rainstorm identification and tracking algorithm that allocates essentially all rainfall to individual precipitation events (Chang et al. in J Clim 29(23):8355–8376, 2016) allows new insights into model biases. Results include that, at least in these runs, model wet bias is driven by excessive areal extent of individual precipitating events, and that the effect is time-dependent, producing excessive diurnal cycle amplitude. This amplified cycle is driven not by new production of events but by excessive daytime enlargement of long-lived precipitation events. We further show that in the domain average, precipitation biases appear best represented as additive offsets. Of all model configurations evaluated, convection-permitting simulations most consistently reduced biases in precipitation event characteristics.

Keywords

Precipitation Convection permitting simulation Parameterization Rainstorm tracking 

Notes

Acknowledgements

The authors thank Bill Collins, Peter Caldwell, Matthew Huber, Robert Jacob, Andreas Prein, and the participants in the 2016 GEWEX Convection-Permitting Climate Modeling Workshop for many helpful comments and suggestions. Christopher Callahan assisted with preparation of figures and diurnal cycle analysis. This work was conducted as part of the Research Network for Statistical Methods for Atmospheric and Oceanic Sciences (STATMOS), supported by NSF awards 1106862, 1106974, and 1107046, and the Center for Robust Decision-making on Climate and Energy Policy (RDCEP), supported by the NSF Decision Making under Uncertainty program award 0951576. Additional support was provided by the University of Cincinnati TAFT Research Center and computing resources were provided by the University of Chicago Research Computing Center.

Supplementary material

382_2018_4294_MOESM1_ESM.pdf (3.2 mb)
Supplementary material 1 (PDF 366 KB)

References

  1. Adler RF, Huffman GJ, Chang A, Ferraro R, Xie PP, Janowiak J, Rudolf B, Schneider U, Curtis S, Bolvin D et al (2003) The Version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). J Hydrometeorol 4(6):1147–1167CrossRefGoogle Scholar
  2. Andrys J, Lyons TJ, Kala J (2015) Multidecadal evaluation of WRF downscaling capabilities over Western Australia in simulating rainfall and temperature extremes. J Appl Meteorol 54(2):370–394CrossRefGoogle Scholar
  3. Awan NK, Truhetz H, Gobiet A (2011) Parameterization-induced error characteristics of MM5 and WRF operated in climate mode over the alpine region: an ensemble-based analysis. J Clim 24(12):3107–3123CrossRefGoogle Scholar
  4. Baldwin ME, Kain JS, Lakshmivarahan S (2005) Development of an automated classification procedure for rainfall systems. Mon Weather Rev 133(4):844–862CrossRefGoogle Scholar
  5. Berenguer M, Surcel M, Zawadzki I, Xue M, Kong F (2012) The diurnal cycle of precipitation from continental radar mosaics and numerical weather prediction models. Part II: Intercomparison among numerical models and with nowcasting. Mon Weather Rev 140(8):2689–2705CrossRefGoogle Scholar
  6. Bosilovich MG, Chen J, Robertson FR, Adler RF (2008) Evaluation of global precipitation in reanalyses. J Appl Meteorol 47(9):2279–2299CrossRefGoogle Scholar
  7. Bryan GH, Morrison H (2012) Sensitivity of a simulated squall line to horizontal resolution and parameterization of microphysics. Mon Weather Rev 140(1):202–225CrossRefGoogle Scholar
  8. Bukovsky MS, Gochis DJ, Mearns LO (2013) Towards assessing narccap regional climate model credibility for the North American Monsoon: current climate simulations. J Clim 26(22):8802–8826CrossRefGoogle Scholar
  9. Chan SC, Kendon EJ, Fowler HJ, Blenkinsop S, Ferro CA, Stephenson DB (2013) Does increasing the spatial resolution of a regional climate model improve the simulated daily precipitation? Clim Dyn 41(5–6):1475–1495CrossRefGoogle Scholar
  10. Chang W, Stein ML, Wang J, Kotamarthi VR, Moyer EJ (2016) Changes in spatiotemporal precipitation patterns in changing climate conditions. J Clim 29(23):8355–8376CrossRefGoogle Scholar
  11. Chen F, Dudhia J (2001) Coupling an advanced land surface-hydrology model with the Penn State—NCAR MM5 modeling system. Part I: model implementation and sensitivity. Mon Weather Rev 129(4):569–585CrossRefGoogle Scholar
  12. Clark AJ, Gallus WA Jr, Chen TC (2007) Comparison of the diurnal precipitation cycle in convection-resolving and non-convection-resolving mesoscale models. Mon Weather Rev 135(10):3456–3473CrossRefGoogle Scholar
  13. Clark AJ, Gallus WA Jr, Xue M, Kong F (2009) A comparison of precipitation forecast skill between small convection-allowing and large convection-parameterizing ensembles. Weather Forecast 24(4):1121–1140CrossRefGoogle Scholar
  14. Clark AJ, Bullock RG, Jensen TL, Xue M, Kong F (2014) Application of object-based time-domain diagnostics for tracking precipitation systems in convection-allowing models. Weather Forecast 29(3):517–542CrossRefGoogle Scholar
  15. Crétat J, Pohl B (2012) How physical parameterizations can modulate internal variability in a regional climate model. J Atmos Sci 69(2):714–724CrossRefGoogle Scholar
  16. Dai A (2006) Precipitation characteristics in eighteen coupled climate models. J Clim 19(18):4605–4630CrossRefGoogle Scholar
  17. Dai A, Trenberth KE (2004) The diurnal cycle and its depiction in the Community Climate System Model. J Clim 17(5):930–951CrossRefGoogle Scholar
  18. Davis C, Brown B, Bullock R (2006a) Object-based verification of precipitation forecasts. Part I: methodology and application to mesoscale rain areas. Mon Weather Rev 134(7):1772–1784CrossRefGoogle Scholar
  19. Davis C, Brown B, Bullock R (2006b) Object-based verification of precipitation forecasts. Part II: application to convective rain systems. Mon Weather Rev 134(7):1785–1795CrossRefGoogle Scholar
  20. Done J, Davis CA, Weisman M (2004) The next generation of NWP: explicit forecasts of convection using the Weather Research and Forecasting (WRF) model. Atmos Sci Lett 5(6):110–117CrossRefGoogle Scholar
  21. Fosser G, Khodayar S, Berg P (2015) Benefit of convection permitting climate model simulations in the representation of convective precipitation. Clim Dyn 44(1–2):45–60CrossRefGoogle Scholar
  22. Gao J, Hou W, Xue Y, Wu S (2015) Validating the dynamic downscaling ability of WRF for East Asian summer climate. Theor Appl Climatol 128(1–2):1–13Google Scholar
  23. Gao Y, Leung LR, Zhao C, Hagos S (2017) Sensitivity of US summer precipitation to model resolution and convective parameterizations across gray zone resolutions. J Geophys Res Atmos 122(5):2714–2733CrossRefGoogle Scholar
  24. Giorgi F, Mearns LO (1999) Introduction to special section: regional climate modeling revisited. J Geophys Res-Atmos 104(D6):6335–6352CrossRefGoogle Scholar
  25. Gochis DJ, Shuttleworth WJ, Yang ZL (2002) Sensitivity of the modeled North American monsoon regional climate to convective parameterization. Mon Weather Rev 130(5):1282–1298CrossRefGoogle Scholar
  26. Grell GA, Dévényi D (2002) A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys Res Lett 29(14):38-1–38-4CrossRefGoogle Scholar
  27. Hong SY, Lim JOJ (2006) The WRF single-moment 6-class microphysics scheme (WSM6). J Korean Meteorol Soc 42(2):129–151Google Scholar
  28. Husak GJ, Michaelsen J, Funk C (2007) Use of the gamma distribution to represent monthly rainfall in Africa for drought monitoring applications. Int J Climatol 27(7):935–944CrossRefGoogle Scholar
  29. Jankov I, Gallus WA Jr, Segal M, Shaw B, Koch SE (2005) The impact of different WRF model physical parameterizations and their interactions on warm season MCS rainfall. Weather Forecast 20(6):1048–1060CrossRefGoogle Scholar
  30. Kain JS (2004) The Kain-Fritsch convective parameterization: an update. J Appl Meteorol 43(1):170–181CrossRefGoogle Scholar
  31. Kala J, Andrys J, Lyons TJ, Foster IJ, Evans BJ (2015) Sensitivity of WRF to driving data and physics options on a seasonal time-scale for the southwest of Western Australia. Clim Dyn 44(3–4):633–659CrossRefGoogle Scholar
  32. Kanamitsu M, Ebisuzaki W, Woollen J, Yang SK, Hnilo J, Fiorino M, Potter G (2002) NCEP-DOE AMIP-II reanalysis (r-2). Bull Am Meteorol Soc 83(11):1631–1643CrossRefGoogle Scholar
  33. Liang XZ, Li L, Dai A, Kunkel KE (2004) Regional climate model simulation of summer precipitation diurnal cycle over the united states. Geophys Res Lett 31(24):L24208CrossRefGoogle Scholar
  34. Lin Y, Mitchell KE (2005) The NCEP stage II/IV hourly precipitation analyses: development and applications. In: 19th conference on hydrology, CiteseerGoogle Scholar
  35. McMillen JD, Steenburgh WJ (2015) Impact of microphysics parameterizations on simulations of the 27 October 2010 Great Salt Lake—effect snowstorm. Weather Forecast 30(1):136–152CrossRefGoogle Scholar
  36. McNeall D, Challenor P, Gattiker J, Stone E (2013) The potential of an observational data set for calibration of a computationally expensive computer model. Geosci Model Dev 6(5):1715–1728CrossRefGoogle Scholar
  37. Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J Geophys Res Atmos 102(D14):16663–16682CrossRefGoogle Scholar
  38. Morrison H, Curry J, Khvorostyanov V (2005) A new double-moment microphysics parameterization for application in cloud and climate models. Part I: description. J Atmos Sci 62(6):1665–1677CrossRefGoogle Scholar
  39. Murthy CR, Gao B, Tao AR, Arya G (2015) Automated quantitative image analysis of nanoparticle assembly. Nanoscale 7(21):9793–9805CrossRefGoogle Scholar
  40. Noh Y, Cheon W, Hong S, Raasch S (2003) Improvement of the k-profile model for the planetary boundary layer based on large eddy simulation data. Bound Layer Meteorol 107(2):401–427CrossRefGoogle Scholar
  41. Pieri AB, von Hardenberg J, Parodi A, Provenzale A (2015) Sensitivity of precipitation statistics to resolution, microphysics, and convective parameterization: a case study with the high-resolution WRF climate model over Europe. J Hydrometeorol 16(4):1857–1872CrossRefGoogle Scholar
  42. Pollard D, Chang W, Haran M, Applegate P, DeConto R (2016) Large ensemble modeling of the last deglacial retreat of the west antarctic ice sheet: comparison of simple and advanced statistical techniques. Geosci Model Dev 9(5):1697–1723CrossRefGoogle Scholar
  43. Prein A, Gobiet A, Suklitsch M, Truhetz H, Awan N, Keuler K, Georgievski G (2013) Added value of convection permitting seasonal simulations. Clim Dyn 41(9–10):2655–2677CrossRefGoogle Scholar
  44. Racherla P, Shindell D, Faluvegi G (2012) The added value to global model projections of climate change by dynamical downscaling: a case study over the continental us using the GISS-ModelE2 and WRF models. J Geophys Res Atmos.  https://doi.org/10.1029/2012JD018091 Google Scholar
  45. Rajeevan M, Kesarkar A, Thampi S, Rao T, Radhakrishna B, Rajasekhar M (2010) Sensitivity of WRF cloud microphysics to simulations of a severe thunderstorm event over southeast India. Ann Geophys Atmos Hydrol Sp Sci 28:603Google Scholar
  46. Randall DA, Dazlich DA (1991) Diurnal variability of the hydrologic cycle in a general circulation model. J Atmos Sci 48(1):40–62CrossRefGoogle Scholar
  47. Ratna SB, Ratnam J, Behera S, Ndarana T, Takahashi K, Yamagata T et al (2014) Performance assessment of three convective parameterization schemes in WRF for downscaling summer rainfall over South Africa. Clim Dyn 42(11–12):2931–2953CrossRefGoogle Scholar
  48. Richardson CW (1981) Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resour Res 17(1):182–190CrossRefGoogle Scholar
  49. Sansó B, Forest C (2009) Statistical calibration of climate system properties. J R Stat Soc Ser C (Appl Stat) 58(4):485–503CrossRefGoogle Scholar
  50. Schneider T, Lan S, Stuart A, Teixeira J (2017) Earth system modeling 2.0: a blueprint for models that learn from observations and targeted high-resolution simulations. Geophys Res Lett 44(24):12396–12417CrossRefGoogle Scholar
  51. Stern R, Coe R (1984) A model fitting analysis of daily rainfall data. J R Stat Soc Ser A 147(1):1–34CrossRefGoogle Scholar
  52. Wang J, Kotamarthi VR (2014) Downscaling with a nested regional climate model in near-surface fields over the contiguous United States. J Geophys Res Atmos 119(14):8778–8797CrossRefGoogle Scholar
  53. Wang J, Swati F, Stein ML, Kotamarthi VR (2015) Model performance in spatiotemporal patterns of precipitation: new methods for identifying value added by a regional climate model. J Geophys Res Atmos 120(4):1239–1259CrossRefGoogle Scholar
  54. Wang Y, Leung LR, McGregor JL, Lee DK, Wang WC, Ding Y, Kimura F (2004) Regional climate modeling: progress, challenges, and prospects. J Meteorol Soc Jpn Ser II 82(6):1599–1628CrossRefGoogle Scholar
  55. Warner TT, Hsu HM (2000) Nested-model simulation of moist convection: the impact of coarse-grid parameterized convection on fine-grid resolved convection. Mon Weather Rev 128(7):2211–2231CrossRefGoogle Scholar
  56. Woolhiser DA, Pegram G (1979) Maximum likelihood estimation of Fourier coefficients to describe seasonal variations of parameters in stochastic daily precipitation models. J Appl Meteorol 18(1):34–42CrossRefGoogle Scholar
  57. Xu J, Small EE (2002) Simulating summertime rainfall variability in the North American monsoon region: the influence of convection and radiation parameterizations. J Geophys Res Atmos 107(D23):ACL 22-1–ACL 22-17CrossRefGoogle Scholar
  58. Xue Y, Janjic Z, Dudhia J, Vasic R, De Sales F (2014) A review on regional dynamical downscaling in intraseasonal to seasonal simulation/prediction and major factors that affect downscaling ability. Atmos Res 147:68–85CrossRefGoogle Scholar
  59. Yang H, Wang B (2012) Reduction of systematic biases in regional climate downscaling through ensemble forcing. Clim Dyn 38(3–4):655–665CrossRefGoogle Scholar
  60. Yu E, Wang H, Gao Y, Sun J (2011) Impacts of cumulus convective parameterization schemes on summer monsoon precipitation simulation over China. Acta Mete Sin 25:581–592CrossRefGoogle Scholar
  61. Zhang L, Wu P, Zhou T, Roberts MJ, Schiemann R (2016) Added value of high resolution models in simulating global precipitation characteristics. Atmos Sci Lett 17(12):646–657CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Won Chang
    • 1
  • Jiali Wang
    • 2
  • Julian Marohnic
    • 3
  • V. Rao Kotamarthi
    • 2
  • Elisabeth J. Moyer
    • 4
  1. 1.Department of Mathematical SciencesUniversity of CincinnatiCincinnatiUSA
  2. 2.Environmental Science DivisionArgonne National LaboratoryLemontUSA
  3. 3.Center for Robust Decision Making on Climate and Energy PolicyUniversity of ChicagoChicagoUSA
  4. 4.Department of the Geophysical SciencesUniversity of ChicagoChicagoUSA

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