Sensitivity of physical parameterizations on prediction of tropical cyclone Nargis over the Bay of Bengal using WRF model

Original Paper

Abstract

Comprehensive sensitivity analyses on physical parameterization schemes of Weather Research Forecast (WRF-ARW core) model have been carried out for the prediction of track and intensity of tropical cyclones by taking the example of cyclone Nargis, which formed over the Bay of Bengal and hit Myanmar on 02 May 2008, causing widespread damages in terms of human and economic losses. The model performances are also evaluated with different initial conditions of 12 h intervals starting from the cyclogenesis to the near landfall time. The initial and boundary conditions for all the model simulations are drawn from the global operational analysis and forecast products of National Center for Environmental Prediction (NCEP-GFS) available for the public at 1° lon/lat resolution. The results of the sensitivity analyses indicate that a combination of non-local parabolic type exchange coefficient PBL scheme of Yonsei University (YSU), deep and shallow convection scheme with mass flux approach for cumulus parameterization (Kain-Fritsch), and NCEP operational cloud microphysics scheme with diagnostic mixed phase processes (Ferrier), predicts better track and intensity as compared against the Joint Typhoon Warning Center (JTWC) estimates. Further, the final choice of the physical parameterization schemes selected from the above sensitivity experiments is used for model integration with different initial conditions. The results reveal that the cyclone track, intensity and time of landfall are well simulated by the model with an average intensity error of about 8 hPa, maximum wind error of 12 m s−1and track error of 77 km. The simulations also show that the landfall time error and intensity error are decreasing with delayed initial condition, suggesting that the model forecast is more dependable when the cyclone approaches the coast. The distribution and intensity of rainfall are also well simulated by the model and comparable with the TRMM estimates.

References

  1. Bengtsson L (2001) Hurricane threats. Science 293:440–441CrossRefGoogle Scholar
  2. Betts AK (1986) A new convective adjustment scheme. Part I: observational and theoretical basis. Quart J Roy Meteor Soc 112:677–691Google 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. Quart J Roy Meteor Soc 112:693–709Google Scholar
  4. Braun SA, Tao WK (2000) Sensitivity of high resolution simulations of hurricane Bob (1991) to the planetary boundary layer parameterization. Mon Weather Rev 128:3941–3961CrossRefGoogle Scholar
  5. Chang HI, Kumar A, Niyogi D, Mohanty UC, Chen F, Dudhia J (2009) The role of land surface processes on the mesoscale simulation of the July 26, 2005 heavy rain event over Mumbai, India, Global Planet. Change. doi:10.1016/j.gloplacha.2008.12.005
  6. Cheng WYY, Steenbyrgh WJ (2005) Evaluation of surface sensible weather forecasts by WRF and ETA models over the Western United States. Weather Forecast 20:812–821CrossRefGoogle Scholar
  7. Dudhia J (1989) Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J Atmos Sci 46:3077–3177CrossRefGoogle Scholar
  8. Dudhia J (2004) The weather research and forecasting model (version 2.0) 2nd international workshop on next generation NWP model. Yonsei University Seoul, Korea, pp 19–23Google Scholar
  9. Emanuel KA (2005) Increasing destructiveness of tropical cyclones over the past 30 years. Nature 436:686–688CrossRefGoogle Scholar
  10. Fovell RG, Su H (2007) Impact of cloud microphysics on hurricane track forecast. Geophy Res Lett 34:L24810. doi:10.1029/2007/GL031723 CrossRefGoogle Scholar
  11. Grell GA, Devenyi D (2002) A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophy Res Lett 29(14):1693–1697CrossRefGoogle Scholar
  12. 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
  13. Huffman GJ, Adler RF, Bolvin DT, Gu G, Nelkin EJ, Bowman KP, Hong Y, Stocker EF, Wolff DB (2007) The TRMM multi-satellite precipitation analysis: quasi-global, multi-year, combined-sensor precipitation estimates at fine scale. J Hydrometeor 8(1):38–55CrossRefGoogle Scholar
  14. Indian Meteorological Department (2008) A preliminary report on Cyclone season of 2008Google Scholar
  15. Janjic ZI (1994) The step-mountain eta coordinate model: further developments of the convection, viscous sub layer and turbulence closure schemes. Mon Weather Rev 122:927–945CrossRefGoogle Scholar
  16. Kain JS, Fritsch JM (1993) Convective parameterization for mesoscale models: the Kain–Fritsch scheme. In: Emanual KA, Raymond DJ (eds) The representation of cumulus convection in numerical models, Am Meteor Soc, 246 ppGoogle Scholar
  17. Mandal M, Mohanty UC, Raman S (2004) A study of impact of parameterization of physical processes on prediction of tropical cyclone over the Bay of Bengal with NCAR/PSU mesoscale model (MM5). Nat Hazard 31:391–414CrossRefGoogle Scholar
  18. Michalakes J, Dudhia J, Gill DO, Henderson T, Klemp J, Skamarock W, Wand W (2005) The weather research and forecast model: software architecture and performance. In: 11th workshop on high performance computing in meteorology, World Scientific, pp 156–168Google Scholar
  19. Mohanty UC, Mandal M, Raman S (2003) Simulation of Orissa super cyclone (1999) using PSU/NCAR mesoscale model. Nat Hazard 31:373–390CrossRefGoogle Scholar
  20. Neumann CJ (1993) Global guide to tropical cyclone forecasting. WMO/TC-No. 560, Report No. TCP-31, World Meteorological Organization; Geneva, SwitzerlandGoogle Scholar
  21. Rao DVB, Prasad DH (2007) Sensitivity of tropical cyclone intensification to boundary layer and convective processes. Nat Hazard 41(3):429–445Google Scholar
  22. Skamarock WC, Klemp JB (2008) A time-split non-hydrostatic atmospheric model for weather research and forecasting applications. J Comput Phys 227:3465–3485CrossRefGoogle Scholar
  23. Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Wang W, Powers JG (2005) A description of the advanced research WRF Version 2. NCAR Technical Note TN-468+ST, 88 ppGoogle Scholar
  24. Sousounis PJ, Hutchinson TA, Marshall, SF (2004) A comparison of MM5, WRF, RUC, ETA performance for great plains heavy precipitation event during the spring of 2003. In: 20th conference on weather analysis and forecasting, Seattle, Am Meteor Soc vol J24.6Google Scholar
  25. Sujatha P, Mohanty UC (2008) A Comparative study on prediction of MM5 and WRF models in simulation of tropical cyclones over Indian seas. Curr Sci 95(7):923–936Google Scholar
  26. Tenerelli JE, Chen SS (2001) High resolution simulation of hurricane Floyd (1999) using MM5 with vertex following mesh refinements, Preprint, 18th conference on weather analysis and forecasting/14th conference on numerical weather prediction 30 July–2 August Ft-Lauderdale, Florida, AMS, pp J54–J56Google Scholar
  27. Webster PJ (2008) Myanmar’s deadly daffodil. Nat Geosci. doi:10.1038/ngeo257

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • P. V. S. Raju
    • 2
  • Jayaraman Potty
    • 2
  • U. C. Mohanty
    • 1
  1. 1.Centre for Atmospheric SciencesIndian Institute of TechnologyNew DelhiIndia
  2. 2.Regional Integrated Multi-Hazard Early Warning System (RIMES)Asian Institute of Technology CampusKlong Luang, PathumthaniThailand

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