Skip to main content

Advertisement

SpringerLink
Log in

An evaluation of the performance of a WRF multi-physics ensemble for heatwave events over the city of Melbourne in southeast Australia

  • Published:
Climate Dynamics Aims and scope Submit manuscript

Abstract

Appropriate choice of physics options among many physics parameterizations is important when using the Weather Research and Forecasting (WRF) model. The responses of different physics parameterizations of the WRF model may vary due to geographical locations, the application of interest, and the temporal and spatial scales being investigated. Several studies have evaluated the performance of the WRF model in simulating the mean climate and extreme rainfall events for various regions in Australia. However, no study has explicitly evaluated the sensitivity of the WRF model in simulating heatwaves. Therefore, this study evaluates the performance of a WRF multi-physics ensemble that comprises 27 model configurations for a series of heatwave events in Melbourne, Australia. Unlike most previous studies, we not only evaluate temperature, but also wind speed and relative humidity, which are key factors influencing heatwave dynamics. No specific ensemble member for all events explicitly showed the best performance, for all the variables, considering all evaluation metrics. This study also found that the choice of planetary boundary layer (PBL) scheme had largest influence, the radiation scheme had moderate influence, and the microphysics scheme had the least influence on temperature simulations. The PBL and microphysics schemes were found to be more sensitive than the radiation scheme for wind speed and relative humidity. Additionally, the study tested the role of Urban Canopy Model (UCM) and three Land Surface Models (LSMs). Although the UCM did not play significant role, the Noah-LSM showed better performance than the CLM4 and NOAH-MP LSMs in simulating the heatwave events. The study finally identifies an optimal configuration of WRF that will be a useful modelling tool for further investigations of heatwaves in Melbourne. Although our results are invariably region-specific, our results will be useful to WRF users investigating heatwave dynamics elsewhere.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22

Similar content being viewed by others

References

  • Arugueso D, Evans JP, Fita L, Bormann KJ (2014) Temperarture response to future urbanization and climate change. Clim Dyn 42:2183–2199. doi:10.1007/s00382-013-1789-6

    Article  Google Scholar 

  • Beniston M et al (2007) Future extreme events in European climate: an exploration of regional climate model projections. Clim Change 81:71–95. doi:10.1007/s10584-006-9226-z

    Article  Google Scholar 

  • Berry G, Reeder MJ, Jakob C (2011) A global climatology of atmospheric fronts. Geophys Res Lett 38:L04809. doi:10.1029/2010GL046451

    Google Scholar 

  • Borge R, Alexandrov V, José del Vas J, Lumbreras J, Rodríguez E (2008) A comprehensive sensitivity analysis of the WRF model for air quality applications over the Iberian Peninsula. Atmos Environ 42:8560–8574. doi:10.1016/j.atmosenv.2008.08.032

    Article  Google Scholar 

  • Boschat G, Pezza A, Simmonds S, Perkins S, Cowan T, Purich A (2015) Large scale and sub-regional connections in the lead up to summer heat wave and extreme rainfall events in eastern Australia. Clim Dyn 44:1823–1840

    Article  Google Scholar 

  • Bukovsky MS, Karoly DJ (2009) Precipitation simulations using WRF as a nested regional climate model. J Appl Meteorol Climatol 48:2152–2159. doi:10.1175/2009JAMC2186.1

    Article  Google Scholar 

  • Cai W, Cowan T, Raupach M (2009) Positive Indian Ocean Dipole events precondition southeast Australia bushfires. Geophys Res Lett 36:L19710. doi:10.1029/2009GL039902

    Article  Google Scholar 

  • Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)?—Arguments against avoiding RMSE in the literature. Geosci Model Dev 7:1247–1250. doi:10.5194/gmd-7-1247-2014

    Article  Google Scholar 

  • Chatterjee A, Engelen RJ, Kawa SR, Sweeney C, Michalak AM (2013) Background error covariance estimation for atmospheric CO2 data assimilation. J Geophys Res Atmos 118:10,140–110,154. doi:10.1002/jgrd.50654

    Google Scholar 

  • Chen F, Kusaka H, Bornstein R, Ching J, Grimmond CSB, Grossman-Clarke S, Loridan T, Manning KW, Martilli A, Miao S, Sailor D, Salamanca FP, Taha H, Tewari M, Wang X, Wyszogrodzki AA, Zhang C (2011) The integrated WRF/urban modelling system: development, evaluation, and applications to urban environmental problems. Int J Climatol 31:273–288. doi:10.1002/joc.2158

    Article  Google Scholar 

  • Chen F, Yang X, Zhu W (2014) WRF simulations of urban heat island under hot-weather synoptic conditions: The case study of Hangzhou City, China. Atmos Res 138:364–377. doi:10.1016/j.atmosres.2013.12.005

    Article  Google Scholar 

  • Chou M-D, Suarez MJ (1999) A solar radiation parameterization for atmospheric studies. NASA Tech Memo 104606:40

    Google Scholar 

  • Chou M. D., Suarez M. J. (2001) A thermal infrared radiation parameterization for atmospheric studies. NASA/TM-2001-104606 19:55

    Google Scholar 

  • Chow WTL, Brennan D, Brazel AJ (2012) Urban heat island research in Phoenix, Arizona: theoretical contributions and policy applications. Bull Am Meteorol Soc 93:517–530. doi:10.1175/BAMS-D-11-00011.1

    Article  Google Scholar 

  • Cowan T, Purich A, Perkins S, Pezza A, Boschat G, Sadler K (2014) More frequent, longer, and hotter heat waves for Australia in the twenty-first century. J Clim 27:5851–5871. doi:10.1175/JCLI-D-14-00092.1

    Article  Google Scholar 

  • Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H, Ho’lm EV, Isaksen L, Kallberg P, Kohler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette J-J, Park BK, Peubey C, de Rosnay P, Tavolato C, The ́paut J-N, Vitart F (2011) The ERA-interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597. doi:10.1002/qj.828

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Emery C, Tai E, Yarwood G (2001) Enhanced meteorological modeling and performance evaluation for two Texas ozone episodes. Final report. The Texas Natural Resource Conservation Commission, Austin. https://www.tceq.texas.gov/assets/public/implementation/air/am/contracts/reports/mm/EnhancedMetModelingAndPerformanceEvaluation.pdf. Accessed 14 Nov 2016

  • Emmanuel R, Krüger E (2012) Urban heat island and its impact on climate change resilience in a shrinking city: the case of Glasgow, UK. Build Environ 53:137–149. doi:10.1016/j.buildenv.2012.01.020

    Article  Google Scholar 

  • Engel CB, Lane TP, Reeder MJ, Rezny M (2013) The meteorology of Black Saturday. Q J R Meteorol Soc 139:585–599

    Article  Google Scholar 

  • Evans JP, Boyer-Souchet I (2012) Local sea surface temperatures add to extreme precipitation in northeast Australia during La Niña. Geophys Res Lett. doi:10.1029/2012GL052014

    Google Scholar 

  • Evans JP, Ekström 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

    Article  Google Scholar 

  • Fallmann J, Emeis S, Suppan P (2014) Mitigation of urban heat stress—a modelling case study for the area of Stuttgart. DIE ERDE 144(3–4):202–216. doi:10.12854/erde14415

    Google Scholar 

  • Fischer EM, Oleson KW, Lawrence DM (2012) Contrasting urban and rural heat stress responses to climate change. Geophys Res Lett. doi:10.1029/2011GL050576

    Google Scholar 

  • García-Díez M, Fernández J, Fita L, Yagüe C (2013) Seasonal dependence of WRF model biases and sensitivity to PBL schemes over Europe. Q J R Meteorol Soc 139:501–514

    Article  Google Scholar 

  • Giannaros TM, Melas D, Daglis IA, Keramitsoglou I, Kourtidis K (2013) Numerical study of the urban heat island over Athens (Greece) with the WRF model. Atmos Environ 73:103–111. doi:10.1016/j.atmosenv.2013.02.055

    Article  Google Scholar 

  • Gilliam RC, Pleim JE (2010) Performance assessment of new land surface and planetary boundary layer physics in the WRF-ARW. J Appl Meteorol Climatol 49:760–774. doi:10.1175/2009JAMC2126.1

    Article  Google Scholar 

  • Gilliam RC, Hogrefe C, Rao ST (2006) New methods for evaluating meteorological models used in air quality applications. Atmos Environ 40:5073–5086. doi:10.1016/j.atmosenv.2006.01.023

    Article  Google Scholar 

  • Hall WD, Rasmussen RM, Thompson G (2005) The new Thompson microphysical scheme in WRF. Preprints, 2005 WRF/MM5 user’s workshop, Boulder, CO. NCAR, 6.1

  • Han Z, Ueda H, An J (2008) Evaluation and intercomparison of meteorological predictions by five MM5-PBL parameterizations in combination with three land-surface models. Atmos Environ 42:233–249. doi:10.1016/j.atmosenv.2007.09.053

    Article  Google Scholar 

  • Hariprasad KBRR, Srinivas CV, Singh AB, Rao SVB, Baskaran R, Venkatraman B (2014) Numerical simulation and intercomparison of boundary layer structure with different PBL schemes in WRF using experimental observations at a tropical site. Atmos Res 145:27–44

    Article  Google Scholar 

  • Herold N, Kala J, Alexander L (2016) The influence of soil moisture deficits on Australian heat waves. Environ Res Lett 11:064003. doi:10.1088/1748-9326/11/6/064003

    Article  Google Scholar 

  • Hirsch AL, Pitman AJ, Kala J (2014) The role of land cover change in modulating the soil moisture-temperature land–atmosphere coupling strength over Australia. Geophys Res Lett 41:5883–5890. doi:10.1002/2014GL061179

    Article  Google Scholar 

  • Hong S-Y, Lim J-O (2006) The WRF single-moment 6-class microphysics scheme (WSM6). J Korean Meteor Soc 42:129–151

    Google Scholar 

  • Hu X-M, Nielsen-Gammon JW, Zhang F (2010) Evaluation of three planetary boundary layer schemes in the WRF model. J Appl Meteorol Climatol 49:1831–1844. doi:10.1175/2010JAMC2432.1

    Article  Google Scholar 

  • Hu X-M, Klein PM, Xue M, Lundquist JK, Zhang F, Qi Y (2013) Impact of low-level jets on the nocturnal urban heat island intensity in Oklahoma city. J Appl Meteorol Climatol 52:1779–1802. doi:10.1175/JAMC-D-12-0256.1

    Article  Google Scholar 

  • Hutchinson MF, Mckenney DW, Lawrence K, Pedlar J, Hopkinson R, Milewska E, Papadopol P (2009) Development and testing of Canada-wide interpolated spatial models of daily minimum/maximum temperature and precipitation for 1961–2003. J Appl Meteorol Climatol 48:725–741. doi:10.1175/2008JAMC1979.1

    Article  Google Scholar 

  • Hutchinson MF, Kesteven J, Xu T (2014) Daily maximum temperature: ANUClimate 1. 1, 0.01 degree, Australian coverage, 1970–2014. Australian National University, Canberra. http://dap.nci.org.au, made available by the Ecosystem Modelling and Scaling Infrastructure (eMAST, http://www.emast.org.au) of the Terrestrial Ecosystem Research Network (TERN, http://www.tern.org.au)

  • Iacono MJ, Delamere JS, Mlawer EJ, Shephard MW, Clough SA, Collins WD (2008) Radiative forcing by long-lived greenhouse gases: calculations with the AER radiative transfer models. J Geophys Res 113:D13103

    Article  Google Scholar 

  • Imhoff ML, Zhang P, Wolfe RE, Bounoua L (2010) Remote sensing of the urban heat island effect across biomes in the continental USA. Remote Sens Environ 114:504–513. doi:10.1016/j.rse.2009.10.008

    Article  Google Scholar 

  • Jankov I, Jr. WAG, 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 20:1048–1060. doi:10.1175/WAF888.1

    Article  Google Scholar 

  • Jankov I et al (2011) An evaluation of five ARW-WRF microphysics schemes using synthetic GOES imagery for an atmospheric river event affecting the California coast. J Hydrometeorol 12:618–633. doi:10.1175/2010JHM1282.1

    Article  Google Scholar 

  • Jerez S, Montavez JP, Jimenez-Guerrero P, Gomez-Navarro JJ, Lorente-Plazas R, Zorita E (2012) A multi-physics ensemble of present-day climate regional simulations over the Iberian Peninsula. Clim Dyn 40:3023–3046. doi:10.1007/s00382-012-1539-1

    Article  Google Scholar 

  • Jones DA, Trewin BC (2000) On the relationships between the El Niño–Southern oscillation and Australian land surface temperature. Int J Climatol 20:697–719

    Article  Google Scholar 

  • 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 44:633–659. doi:10.1007/s00382-014-2160-2

    Article  Google Scholar 

  • Konopac KS, Akbari H (2002) Energy savings for heat island reduction strategies in Chicago and Houston (including updates for baton rouge, Sacramento, and Salt Lake city). Draft final report, LBNL-49638. University of California, Berkeley

    Google Scholar 

  • Kunkel KE, Changnon SA, Reinke BC, Arritt RW (1996) The July 1995 heat wave in the midwest: a climatic perspective and critical weather factors. Bull Am Meteorol Soc 77:1507–1518. doi:10.1175/1520-0477(1996)

    Article  Google Scholar 

  • Lee SH, Kim SW, Angevine WM, Bianco L, McKeen SA, Senff CJ, Trainer M, Tucker SC, Zamora RJ (2011) Evaluation of urban surface parameterizations in the WRF model using measurements during the Texas Air Quality Study 2006 field campaign. Atmos Chem Phys 11:2127–2143

  • Li D, Bou-Zeid E (2013) Synergistic interactions between urban heat islands and heat waves: the impact in cities is larger than the sum of its parts. J Appl Meteorol Climatol 52:2051–2064

    Article  Google Scholar 

  • Marshall AG, Hudson D, Wheeler MC, Alves O, Hendon HH, Pook MJ, Risbey JS (2014) Intra-seasonal drivers of extreme heat over Australia in observations and POAMA-2. Clim Dyn 43(7–8):1915–1937. 10.1007/s00382-013-2016-1

    Article  Google Scholar 

  • Min SK, Cai W, Whetton P (2013) Influence of climate variability on seasonal extremes over Australia. J Geophys Res Atmos 118(2):643–654

    Article  Google Scholar 

  • Miralles DG, Teuling AJ, van Heerwaarden CC, Vilà- Guerau de Arellano J (2014) Mega-heatwave temperatures due to combined soil desiccation and atmospheric heat accumulation. Nat Geosci 7:345–349 doi:10.1038/ngeo2141

    Article  Google Scholar 

  • 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 102:16663–16682. doi:10.1029/97JD00237

    Article  Google Scholar 

  • Mooney PA, Mulligan FJ, Fealy R (2013) Evaluation of the sensitivity of the weather research and forecasting model to parameterization schemes for regional climates of Europe over the period 1990–95. J Clim 26:1002–1017. doi:10.1175/jcli-d-11-00676.1

    Article  Google Scholar 

  • Nairn J, Fawcett R (2013) Defining heatwaves: heatwave defined as a heat impact event servicing all community and business sectors in Australia. The Centre for Australian Weather and Climate Research. http://www.cawcr.gov.au/technical-reports/CTR_060.pdf. Accessed 22 Nov 2016

  • Nicholls N, Larsen S (2011) Impact of drought on temperature extremes in Melbourne, Australia. Aust Meteorol Oceanogr J 61:113–6

    Article  Google Scholar 

  • Nielsen-Gammon J, Powell C, Mahoney M, Angevine W, Senff C, White A, Berkowitz C, Doran C, Knupp K (2008) Multisensor estimation of mixing heights over a coastal city. J Appl Meteorol Climatol 47:27–43

    Article  Google Scholar 

  • Parker TJ, Berry GJ, Reeder MJ (2013) The influence of tropical cyclones on heat waves in Southeastern Australia. Geophys Res Lett 40:6264–6270

    Article  Google Scholar 

  • Parker TJ, Berry GJ, Reeder MJ (2014) The structure and evolution of heat waves in Southeastern Australia. J Clim 27:5768–5785. doi:10.1175/JCLI-D-13-00740.1

    Article  Google Scholar 

  • Perkins SE (2015) A review on the scientific understanding of heatwaves—their measurement, driving mechanisms, and changes at the global scale. Atmos Res 164(165):242–267

    Article  Google Scholar 

  • Perkins SE, Alexander LV (2013) On the measurement of heat waves. J Clim 26:4500–4517

    Article  Google Scholar 

  • Perkins SE, Argüeso D, White CJ (2015) Relationships between climate variability, soil moisture, and Australian heatwaves. J Geophys Res Atmos 120(81):44–64

    Google Scholar 

  • Pezza AB, Van Rensch P, Cai W (2012) Severe heat waves in Southern Australia: synoptic climatology and large scale connections. Clim Dyn 38(1–2):209–224

    Article  Google Scholar 

  • Pitman AJ (2003) The evolution of, and revolution in, land surface schemes designed for climate models. Int J Climatol 23:479–510. doi:10.1002/joc.893

    Article  Google Scholar 

  • Pleim JE (2007) A combined local and nonlocal closure model for the atmospheric boundary layer. Part II: application and evaluation in a mesoscale meteorological model. J Appl Meteorol Climatol 46:1396–1409. doi:10.1175/JAM2534.1

    Article  Google Scholar 

  • Rosenfeld AH, Akbari H, Romn JJ, Pomerantz M (1998) Cool communities: strategies for heat island mitigation and smog reduction. Energy Build 28:51–62

    Article  Google Scholar 

  • Rosenzweig C, Solecki WD, Parshall L, Chopping M, Pope G, Goldberg R (2005) Characterizing the urban heat island in current and future climates in New Jersey. Glob Environ Change Part B Environ Hazards 6:51–62. doi:10.1016/j.hazards.2004.12.001

    Google Scholar 

  • Russell A, Dennis R (2000) NARSTO critical review of photochemical models and modeling. Atmos Environ 34:2283–2324. doi:10.1016/S1352-2310(99)00468-9

    Article  Google Scholar 

  • Salamanca F, Martilli A, Yague C (2011) A numerical study of the urban heat island over Madrid during the DESIREX (2008) campaign with WRF and an evaluation of simple mitigation strategies. Int J Climatol 32:2372–2386

    Article  Google Scholar 

  • Schoetter R, Cattiaux J, Douville H (2015) Changes of western European heat wave characteristics projected by the CMIP5 ensemble. Clim Dyn 45:1601–1616. doi:10.1007/s00382-014-2434-8

    Article  Google Scholar 

  • Shi JJ, Matsui T, Tao WK, Lidard CP, Chin M, Tan Q, Kemp E (2014) Implementation of an aerosol cloud microphysics radiation coupling into the NASA unified WRF: simulation results for the 67 August 2006 AMMA special observing period. Q J R Meteorolog Soc 140:2158–2175. doi:10.1002/qj.2286

    Article  Google Scholar 

  • Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Wang W, Power JG (2005) A description of the advanced research WRF version 2. NCAR technical note, NCAR/TND468 + STR. National Center for Atmospheric Research, Boulder

    Google Scholar 

  • Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Duda MG, Huang X-Y, Wang W, Powers JG (2008) A description of the advanced research WRF version 3. NCAR technical note NCAR/TN-475 + STR, National Center for Atmospheric Research, Boulder

    Google Scholar 

  • Srikanth M, Satyanarayana ANV, Srinivas CV, Kumar MK (2015) Mesoscale atmospheric flow-field simulations for air quality modelling over complex terrain region of Ranchi in eastern India using WRF. Atmos Environ 107:315–328

    Article  Google Scholar 

  • Srinivas CV, Venkatesan R, Bagavath Singh A (2007) Sensitivity of mesoscale simulations of land–sea breeze to boundary layer turbulence parameterization. Atmos Environ 41:2534–2548. doi:10.1016/j.atmosenv.2006.11.027

    Article  Google Scholar 

  • Stegehuis Al, Vautard R, Ciais P, Teuling AJ, Jung M, Yiou P (2013) Summer temperatures in Europe and land heat fluxes in observation-based data and regional climate model simulations. Clim Dyn 41:455–477. doi:10.1007/s00382-012-1559-x

    Article  Google Scholar 

  • Stegehuis AI, Vautard R, Ciais P, Teuling AJ, Miralles DG, Wild M (2015) An observation-constrained multi-physics WRF ensemble for simulating European mega heat waves. Geosci Model Dev 8:2285–2298. doi:10.5194/gmd-8-2285-2015

    Article  Google Scholar 

  • Sukoriansky S, Galperin B, Perov V (2005) Application of a new spectral theory of stably stratified turbulence to the atmospheric boundary layer over ice. Bound Layer Meteorol 117:231–257

    Article  Google Scholar 

  • Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res 106:7183–7192

    Article  Google Scholar 

  • Tewari MF, Chen F, Kusaka H, Miao S (2007) Coupled WRF/unified Noah/urban-canopy modeling system. NCAR WRF documentation. NCAR, Boulder, pp 1–20. https://ral.ucar.edu/sites/default/files/public/product-tool/WRF-LSM-Urban.pdf. Accessed 13 Dec 2016

  • Users’ s Guide for the Advanced Research WRF (ARW) (2015) Modeling system version 3.7 (April 2015). WRF user’s webpage: http://www2.mmm.ucar.edu/wrf/users/docs/user_guide_V3.7/ARWUsersGuideV3.7.pdf. Accessed 11 Oct 2016

  • Willmott CJ, Matsuura K, Robeson SM (2009) Ambiguities inherent in sums-of-squares-based error statistics. Atmos Environ 43:749–752. doi:10.1016/j.atmosenv.2008.10.005

    Article  Google Scholar 

  • Zempila MM, Giannaros T, Bais A, Melas D, Kazantzidis A (2016) Evaluation of WRF shortwave radiation parameterizations in predicting global horizontal irradiance in Greece. Renew Energy 86:831–840

    Article  Google Scholar 

Download references

Acknowledgements

Data support by the Bureau of Meteorology (BoM) Australia, ECMWF (ERA-interim) data server (http://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=ml/) and University of Wyoming (atmospheric sounding data) data server (http://weather.uwyo.edu/upperair/sounding.html) are gratefully acknowledged. Authors also acknowledge Dr. Sachindra Dhanapala Arachchige for his cooperation in this study. The creation of the ANUClim data was funded by the Terrestrial Ecosystem Research Network (TERN) Ecosystem Modelling and Scaling Infrastructure (eMAST) Facility under the National Collaborative Research Infrastructure Strategy (NCRIS) 2013–2014 budget initiative of the Australian Government Department of Industry, the Australian Government Department of Environment in support of the National Carbon Accounting System, and the Australian National University. The comments of an anonymous reviewer helped to improve the manuscript. All this assistance is gratefully acknowledged.

Author information

Authors and Affiliations

  1. College of Engineering and Science, Victoria University, PO Box 14428, Melbourne, Victoria, Australia

    H. M. Imran, A. W. M. Ng & S. Muthukumaran

  2. Environmental and Conservation Sciences, School of Veterinary and Life Sciences, Murdoch University, Perth, Western Australia

    J. Kala

Authors
  1. H. M. Imran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. Kala
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. W. M. Ng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S. Muthukumaran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to H. M. Imran.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Imran, H.M., Kala, J., Ng, A.W.M. et al. An evaluation of the performance of a WRF multi-physics ensemble for heatwave events over the city of Melbourne in southeast Australia. Clim Dyn 50, 2553–2586 (2018). https://doi.org/10.1007/s00382-017-3758-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00382-017-3758-y

Keywords

Search

Navigation