Theoretical and Applied Climatology

, Volume 115, Issue 1–2, pp 297–304 | Cite as

Evaluating rainfall patterns using physics scheme ensembles from a regional atmospheric model

  • Fei Ji
  • Marie Ekström
  • Jason P. Evans
  • Jin Teng
Original Paper


This study evaluated the ability of Weather Research and Forecasting (WRF) multi-physics ensembles to simulate storm systems known as East Coast Lows (ECLs). ECLs are intense low-pressure systems that develop off the eastern coast of Australia. These systems can cause significant damage to the region. On the other hand, the systems are also beneficial as they generate the majority of high inflow to coastal reservoirs. It is the common interest of both hazard control and water management to correctly capture the ECL features in modeling, in particular, to reproduce the observed spatial rainfall patterns. We simulated eight ECL events using WRF with 36 model configurations, each comprising physics scheme combinations of two planetary boundary layer (pbl), two cumulus (cu), three microphysics (mp), and three radiation (ra) schemes. The performance of each physics scheme combination and the ensembles of multiple physics scheme combinations were evaluated separately. Results show that using the ensemble average gives higher skill than the median performer within the ensemble. More importantly, choosing a composite average of the better performing pbl and cu schemes can substantially improve the representation of high rainfall both spatially and quantitatively.


Root Mean Square Error Ensemble Average Mean Absolute Error Rainfall Threshold Equitable Threat Score 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work is made possible by funding from the NSW Environmental Trust for the ESCCI-ECL project, the NSW Office of Environment and Heritage backed NSW/ACT Regional Climate Modelling Project (NARCliM), and the Australian Research Council as part of the Discovery Project DP0772665 and Linkage Project LP120200777. Thanks to the South Eastern Australian Climate Initiative (SEACI) for funding the CSIRO contribution to this study. This research was undertaken on the NCI National Facility in Canberra, Australia, which is supported by the Australian Commonwealth Government.


  1. 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 24:3107–3123. doi: 10.1175/2011JCLI3674.1 CrossRefGoogle Scholar
  2. Betts AK (1986) A new convective adjustment scheme. Part I: observational and theoretical basis. Q J Roy Meteorol Soc 121:255–270Google Scholar
  3. Betts AK, Miller MJ (1986) A new convective adjustment scheme. Part II: single column tests using GATE wave, BOMEX, and arctic air-mass data sets. Q J Roy Meteorol Soc 121:693–709Google Scholar
  4. Carril AF, Mene’ndez CG, Remedio ARC (2012) Performance of a multi-RCM ensemble for South Eastern South America. Clim Dyn 39:2747–2768CrossRefGoogle Scholar
  5. Chen F, Dudhia J (2001) Coupling an advanced land-surface/ hydrology model with the Penn State/ NCAR MM5 modeling system. Part I: model description and implementation. Mon Weather Rev 129:569–585CrossRefGoogle Scholar
  6. Clough SA, Shephard MW, Mlawer EJ, Delamere JS, Iacono MJ, Cady-Pereira K, Boukabara S, Brown PD (2005) Atmospheric radiative transfer modeling: a summary of the AER codes. J Quant Spectrosc Radiat Transf 91:233–244CrossRefGoogle Scholar
  7. Cocke S, LaRow TE (2000) Seasonal predictions using a regional spectral model embedded within a coupled ocean–atmosphere model. Mon Weather Rev 128:689–708CrossRefGoogle Scholar
  8. Collins WD, Rash PJ, Boville BA, Hack JJ, McCaa JR, Williamson DL, Kiehl JT, Briegleb B (2004) Description of the NCAR community atmosphere model (CAM 3 0), NCAR technical note, NCAR/TN-464 + STR, 226 ppGoogle Scholar
  9. Dee DP et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597CrossRefGoogle Scholar
  10. Dudhia J (1989) Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J Atmos Sci 46:3077–3107CrossRefGoogle Scholar
  11. Evans J P, and McCabe M F (2010) Regional climate simulation over Australia’s Murray-Darling basin: A multitemporal assessment. J Geophys Res 115, Issue D14. doi: 10.1029/2010JD013816
  12. Evans JP, McCabe MF (2013) Effect of model resolution on a regional climate model simulation over southeast Australia. Clim Res. doi: 10.3354/cr01151 Google Scholar
  13. Evans JP, Westra S (2012) Investigating the mechanisms of diurnal rainfall variability using a regional climate model. J Clim 25:7232–7247. doi: 10.1175/JCLI-D-11-00616.1 CrossRefGoogle Scholar
  14. Evans JP, Ekstrom M, Ji F (2012) Evaluating the performance of a WRF physics ensemble over South-East Australia. Clim Dyn 39:1241–1258. doi: 10.1007/s00382-011-1244-5 CrossRefGoogle Scholar
  15. Fraedrich K, Leslie LM (1987) Combining predictive schemes in short-term forecasting. Mon Weather Rev 115:1640–1644. doi: 10.1175/1520-0493 CrossRefGoogle Scholar
  16. Hagedorn R, Doblas-Reyes F, Palmer T (2005) The rationale behind the success of multi-model ensembles in seasonal forecasting—I. basic concept. Tellus Ser A Dyn Meteorol Oceanogr 57:219–233CrossRefGoogle Scholar
  17. Hong S-Y, Lim J-OJ (2006) The WRF single-moment 6-class microphysics scheme (WSM6). J Kor Meteor Soc 42:129–151Google Scholar
  18. Hong S-Y, Dudhia J, Chen S-H (2004) A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon Weather Rev 132:103–120CrossRefGoogle Scholar
  19. Hong SY, Noh Y, Dudhia J (2006) A new vertical diffusion package with an explicit treatment of entrainment processes. Mon Weather Rev 134:2318–2341CrossRefGoogle Scholar
  20. Ishizaki Y, Nakaegawa T, Takayabu I (2012) Validation of precipitation over Japan during 1985–2004 simulated by three regional climate models and two multi-model ensemble means. Clim Dyn 39:185–206CrossRefGoogle Scholar
  21. Janjic ZI (1994) The step-mountain eta coordinate model: further developments of the convection, viscous sublayer and turbulence closure schemes. Mon Weather Rev 122:927–945CrossRefGoogle Scholar
  22. Janjic ZI (2000) Comments on “Development and evaluation of a convection scheme for use in climate models”. J Atmos Sci 57:3686CrossRefGoogle Scholar
  23. Jankov I, Gallus W Jr, Segal M, Shaw B, Koch S (2005) The impact of different WRF model physical parameterizations and their interactions on warm season MCS rainfall. Weather Forecast 20:1048–1060CrossRefGoogle Scholar
  24. Jones D, Wang W, Fawcett R (2009) High-quality spatial climate data-sets for Australia. Aust Meteorol Mag 58:233–248Google Scholar
  25. Kain JS (2004) The Kain-Fritsch convective parameterization: an update. J Appl Meteorol 43:170–181CrossRefGoogle Scholar
  26. Kain JS, Fritsch JM (1990) A one-dimensional entraining/ detraining plume model and its application in convective parameterization. J Atmos Sci 47:2784–2802CrossRefGoogle Scholar
  27. Kain JS, Fritsch JM (1993) Convective parameterization for mesoscale models: the Kain-Fritsch scheme, the representation of cumulus convection in numerical models. In: Emanuel KA, Raymond DJ (eds) Amer Meteor Soc 246 ppGoogle Scholar
  28. Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the long-wave. J Geophys Res 102:16663–16682CrossRefGoogle Scholar
  29. Paulson CA (1970) The mathematical representation of wind speed and temperature profiles in the unstable atmospheric surface layer. J Appl Meteorol 9:857–861CrossRefGoogle Scholar
  30. Pepler AS, Rakich CS (2010) Extreme inflow events and synoptic forcing in Sydney catchments. IOP Conf Ser: Earth Environ Sci (EES) 11(012010). doi: 10.1088/1755-1315/11/1/012010
  31. Phillips TJ, Gleckler PJ (2006) Evaluation of continental precipitation in 20th century climate simulations: the utility of multimodel statistics. Water Resour Res 42(3). doi:  10.1029/2005WR004313
  32. Schaller N, Mahlstein I, Cermak J, Knutti R (2011) Analyzing precipitation projections: a comparison of different approaches to climate model evaluation. J Geophys Res 116(10). doi:  10.1029/2010JD014963
  33. Schwartz CS, Kain JS, Weiss SJ, Xue M, Bright DR, Kong F, Thomas KW, Levit JJ, Coniglio MC, Wandishin MS (2010) Toward improved convection-allowing ensembles: model physics sensitivities and optimizing probabilistic guidance with small ensemble membership. Weather Forecast 25:263–280. doi: 10.1175/2009WAF2222267.1 CrossRefGoogle Scholar
  34. Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Duda M, Huang XY, Wang W, Powers JG (2008) A description of the advanced research WRF version 3. NCAR, Boulder, NCAR Technical NoteGoogle Scholar
  35. Speer M, Wiles P, Pepler A (2009) Low pressure systems off the New South Wales coast and associated hazardous weather: establishment of a database. Aust Meteorol Oceanogr J 58:29–39Google Scholar
  36. Webb EK (1970) Profile relationships: the log-linear range, and extension to strong stability. Q J Roy Meteorol Soc 96:67–90CrossRefGoogle Scholar
  37. Wilks DS (2006) Statistical methods in the atmospheric sciences, 2nd edn. Academic Press, Amsterdam, p 627, International Geophysics Series, 91Google Scholar
  38. Yuan X, Liang XZ (2011) Improving cold season precipitation prediction by the nested CWRF-CFS system. Geophys Res Lett 38, L02706. doi: 10.1029/2010GL046104 Google Scholar
  39. Yuan X, Liang XZ, Wood EF (2012) WRF ensemble downscaling seasonal forecasts of China winter precipitation during 1982–2008. Clim Dyn 39:2041–2058. doi: 10.1007/s00382-011-1241-8 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Wien 2013

Authors and Affiliations

  • Fei Ji
    • 1
  • Marie Ekström
    • 2
  • Jason P. Evans
    • 3
  • Jin Teng
    • 2
  1. 1.NSW Office of Environment and HeritageQueanbeyanAustralia
  2. 2.CSIRO Land and WaterCanberraAustralia
  3. 3.Climate Change Research CentreUniversity of New South WalesSydneyAustralia

Personalised recommendations