Sensitivity of simulated cyclone Gonu intensity and track to variety of parameterizations: Advanced hurricane WRF model application

Article
  • 42 Downloads

Abstract

Domain configuration and several physical parameterization settings such as planetary boundary layer, cumulus convection, and ocean–atmosphere surface flux parameterizations can play significant roles in numerical prediction of tropical cyclones. The present study focuses to improve the prediction of the TC Gonu by investigating the sensitivity of simulations to mentioned configurations with the Advanced Hurricane WRF model. The experiments for domain design sensitivity with 27 km resolution has been shown moving the domains towards the east improve the results, due to better account for the large-scale process. The fixed and movable nests on a 9-km grid were considered separately within the coarse domain and their results showed that despite salient improvement in simulated intensity, an accuracy reduction in simulated track was observed. Increasing horizontal resolution to 3 km incredibly reduced the simulated intensity accuracy when compared to 27 km resolution. Thereafter, different initial conditions were experimented and the results have shown that the cyclone of 1000 hPa sea level pressure is the best simulation initial condition in predicting the track and intensity for cyclone Gonu. The sensitivity of simulations to ocean–atmosphere surface-flux parameterizations on a 9-km grid showed the combination of ‘Donelan scheme’ for momentum exchanges along with ‘Large and Pond scheme’ for heat and moisture exchanges provide the best prediction for cyclone Gonu intensity. The combination of YSU and MYJ PBL scheme with KF convection for prediction of track and the combination of YSU PBL scheme with KF convection for prediction of intensity are found to have better performance than the other combinations. These 22 sensitivity experiments also implicitly lead us to the conclusion that each particular forecast aspect of TC (e.g., track, intensity, etc.) will require its own special design.

Keywords

Tropical cyclone Gonu surface fluxes PBL cumulus convection AHW 

Notes

Acknowledgements

We would like to acknowledge the generous help of India Meteorological Department and Iran Meteorological Organization (IRIMO), for providing essential data. We also thank the anonymous reviewers for their valuable comments that improved the quality of manuscript. The funding was provided by the University of Hormozgan (Bandar Abbas, Iran).

References

  1. Advanced Research WRF (ARW) Modeling System User’s Guides, Version 3 (2017) http://www2.mmm.ucar.edu/wrf/users/docs/user_guide_V3/contents.html
  2. Anthes R A 1982 Tropical cyclones: Their evolution, structure and effects; Meteor. Mono. Am. Meteor. Soc., 208p.Google Scholar
  3. Betts A K and Miller M J 1986 A new convective adjustment scheme. Part II: Single column tests using GATE wave, BOMEX, ATEX, and Arctic air-mass data sets; Quart. J. Roy. Meteorol. Soc. 112 693–709.Google Scholar
  4. Black P G, D’Asaro E A, Drennan W M, French J R, Niiler P P, Sanford T B, Terrill E J, Walsh E J and Zhang J A 2007 Air–sea exchange in hurricanes: Synthesis of observations from the coupled boundary layer air–sea transfer experiment; Bull. Am. Meteorol. Soc. 88 357–374.CrossRefGoogle Scholar
  5. Braun S A and Tao W K 2000 Sensitivity of high-resolution simulations of hurricane Bob (1991) to planetary boundary layer parameterizations; Mon. Wea. Rev. 128 3941–3961.CrossRefGoogle Scholar
  6. Carlson T N and Boland F E 1978 Analysis of urban-rural canopy using a surface heat flux/temperature model; J. Appl. Meteorol. 17 998–1013.CrossRefGoogle Scholar
  7. Charnock H 1955 Wind stress on a water surface; Quart. J. Roy. Meteorol. Soc. 81 639–640.CrossRefGoogle Scholar
  8. Chen S S, Price J F, Zhao W, Donelan M A and Walsh E J 2007 The CBLAST-Hurricane Program and the next-generation fully coupled atmosphere–wave–ocean models for hurricane research and prediction; Bull. Am. Meteorol. Soc. 88 311–317.CrossRefGoogle Scholar
  9. Chen S, Qian Y K and Peng S 2015 Effects of various combinations of boundary layer schemes and microphysics schemes on the track forecasts of tropical cyclones over the South China Sea; Nat. Hazards 78 61–74.CrossRefGoogle Scholar
  10. Davis C and Bosart F L 2001 Numerical simulations of the genesis of hurricane Diana (1984). Part II: Sensitivity of track and intensity prediction; Mon. Mon. Wea. Rev.  130 1100–1124.CrossRefGoogle Scholar
  11. Davis C, Wang W, Dudhia J and Torn R 2010 Does increased horizontal resolution improve hurricane wind forecasts?; Wea. Forecasting 25 1826–1841.CrossRefGoogle Scholar
  12. Davis C, Wang W, Chen S S, Chen Y, Corbosiero K, DeMaria M, Dudhia J, Holland G, Klemp J, Michalakes J, Reeves H, Rotunno R, Snyder C and Xiao Q 2008 Prediction of land falling hurricanes with the advanced hurricane WRF model; Mon. Wea. Rev. 136 1990–2005.CrossRefGoogle Scholar
  13. Deshpande M, Pattnaik S, Salvekar P S 2010 Impact of physical parameterization schemes on numerical simulation of super cyclone Gonu; Nat. Hazards 55 211–231.Google Scholar
  14. Donelan M, Haus B, Reul N, Plant W, Stiassnie M, Graber H, Brown O and Saltzman E 2004 On the limiting aerodynamic roughness of the ocean in very strong winds; Geophys. Res. Lett. 31 L18306.CrossRefGoogle Scholar
  15. Dudhia J 1989 Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model; J. Atmos. Sci46 3077–3107.CrossRefGoogle Scholar
  16. Emanuel K A 1995 Sensitivity of tropical cyclones to surface exchange coefficients and a revised steady-state model incorporating eye dynamics; J. Atmos. Sci. 52 3969–3976.CrossRefGoogle Scholar
  17. Fierro A O, Rogers R F and Marks F D 2009 The impact of horizontal grid spacing on the microphysical and kinematic structures of strong tropical cyclones simulated with the WRF-ARW Model; Mon. Wea. Rev. 137 3717–3743.CrossRefGoogle Scholar
  18. Frank W M and George S Y 2007 The interannual variability of tropical cyclones; Mon. Wea. Rev. 135 3587–3598.CrossRefGoogle Scholar
  19. Garratt J R 1992 The Atmospheric Boundary Layer; Cambridge University Press, Cambridge.Google Scholar
  20. Goerss J S 2006 Prediction of tropical cyclone track forecast error for hurricanes Katrina, Rita, and Wilma; Preprints, 27th Conf. on Hurricanes and Tropical Meteorology, Am. Meteor. Soc., Monterey, CA.Google Scholar
  21. Gopalakrishnan S, Liu Q, Marchok T, Sheinin D, Surgi N, Tuleya R, Yablonski R and Zhang X 2010 Hurricane Weather Research and Forecasting (HWRF) model scientific documentation.Google Scholar
  22. Hong S Y, Dudhia J and Chen S H 2004 A revised approach to ice microphysical processes for the bulk parameterization of cloud sand precipitation; Mon. Wea. Rev. 132 103–120.CrossRefGoogle Scholar
  23. Hong S Y, Noh Y and Dudhia J 2006 A new vertical diffusion package with an explicit treatment of entrainment processes; Mon. Wea. Rev.  134 2318–2341.CrossRefGoogle Scholar
  24. IMD 2008 Track of storm and depressions over the Indian Seas during 1891–2008; Cyclone e-Atlas published by IMD, http://www.imd.gov.in/section/nhac/dynamic/.
  25. Janjic Z I 1990 The step-mountain coordinate: Physics package; Mon. Wea. Rev. 118 1429–1443.CrossRefGoogle Scholar
  26. Janjic Z I 1994 The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer and turbulence closure schemes; Mon. Wea. Rev. 122 927–945.CrossRefGoogle Scholar
  27. Janjic Z I 2003 A nonhydrostatic model based on a new approach; Meteor. Atmos. Phys.  82 271–285.CrossRefGoogle Scholar
  28. Kain J S 2004 The Kain–Fritsch convective parameterization: An update; J. Appl. Meteorol. 43 170–181.CrossRefGoogle Scholar
  29. Kain J S and Fritsch J M 1993 Convective parameterization for mesoscale models: The Kain–Fritsch scheme. The representation of cumulus convection in numerical models; Meteorol. Monogr. 46 165–170.Google Scholar
  30. Kanase R D and Salvekar P S 2011 Numerical simulation of severe cyclonic storm LAILA (2010): Sensitivity to initial condition & cumulus parameterization scheme; Proc. Disaster Risk Vulnerablity. Conf., Germany 1 165–170.Google Scholar
  31. Krishnamurti T N 2005 The hurricane intensity issue; Mon. Wea. Rev. 133 1886–1912.CrossRefGoogle Scholar
  32. Kumar A, Done J, Dudhia J and Niyogi D 2011 Simulations of cyclone Sidr in the Bay of Bengal with a high-resolution model: Sensitivity to large-scale boundary forcing; Meteorol. Atmos. Phys. 114 123–137.CrossRefGoogle Scholar
  33. Large W G and Pond S 1981 Open ocean momentum flux measurements in moderate to strong winds; J. Phys. Oceanogr. 11 324–336.CrossRefGoogle Scholar
  34. Mandal M, Mohanty U C and Raman S 2004 A study on the impact of parameterization of physical processes on prediction of tropical cyclones over the Bay of Bengal with NCAR/PSU mesoscale model; Nat. Hazards 31 391–414.Google Scholar
  35. Mellor G L and Yamada T 1982 Development of a turbulence closure model for geophysical fluid problems; Rev. Geophys. Space Phys. 20 851–875.CrossRefGoogle Scholar
  36. Mlawer E, Taubman S, Brown P, Lacono M and Clough S 1997 Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the long-wave; J. Geophys. Res. 102 16663–16682.CrossRefGoogle Scholar
  37. Mohandas S and Ashrit R 2013 Sensitivity of different convective parameterization schemes on tropical cyclone prediction using a mesoscale model; Nat. Hazards 73 213–235.CrossRefGoogle Scholar
  38. Mohanty U C and Gupta A 1997 Deterministic methods for prediction of tropical cyclone tracks; Mausam 48 257–272.Google Scholar
  39. Noh Y, Cheon W, Hong S and Raasch S 2003 Improvement of the k-profile model for the planetary boundary layer based on large eddy simulation data; Bound. Layer Meteorol. 107 401–427.CrossRefGoogle Scholar
  40. Nolan D S, Stern D P and Zhange J A 2009 Evaluation of planetary boundary layer parameterizations in tropical cyclones by comparison of in situ observations and high-resolution simulations of hurricane Isabel (2003). Part II: Inner-core boundary layer and eyewall structure; Mon. Wea. Rev137 3675–3698.CrossRefGoogle Scholar
  41. Osuri K K, Mohanty U C, Routray A, Mohapatra M and Niyogi D 2013 Real-time track prediction of tropical cyclones over the North Indian Ocean using the ARW Model; J. Appl. Meteorol. Climatol. 52 2476–2492.CrossRefGoogle Scholar
  42. Osuri K K, Mohanty U C, Routray A, Kulkarni M A and Mohapatra M 2012 Customization of WRF–ARW model with physical parameterization schemes for the simulation of tropical cyclones over North Indian Ocean; Nat. Hazards 63 1337–1359.CrossRefGoogle Scholar
  43. Pattanayak S and Mohanty U 2008 A comparative study on performance of MM5 and WRF models in simulation of tropical cyclones over Indian seas; Curr. Sci. 95 923–936.Google Scholar
  44. Pollard R T, Rhines P B and Thompson R O R Y 1973 The deepening of the wind-mixed layer; Geophys. Astrophys. Fluid. Dyn4 381–404.CrossRefGoogle Scholar
  45. Rao G V and Bhaskar Rao D V 2003 A review of some observed mesoscale characteristics of tropical cyclones and some preliminary numerical simulations of their kinematic features; Proc. Indian Nat. Sci. Acad. 69 523–541.Google Scholar
  46. Schwartz C S, Kain J S, Weiss S J, Xue M, Bright D R, Kong F, Thomas K W, Levit J J and Coniglio M C 2009 Next-day convection-allowing WRF model guidance: A second look at 2-km versus 4-km grid spacing; Mon. Wea. Rev. 137 3351–3372.CrossRefGoogle Scholar
  47. Srinivas C V, Bhaskar Rao D V, Vesubabu V, Baskaran R and Venkatesan R 2013 Tropical cyclone predictions over the Bay of Bengal using the high-resolution advanced research weather research and forecasting (ARW); Quart. J. Roy. Meteor. Soc.  139 1810–1825.CrossRefGoogle Scholar
  48. Tallapragada V, Kieu V, Kwon Y, Trahan S, Liu Q, Zhang Z and Kwon I H 2014 Evaluation of storm structure from the operational HWRF during 2012 Implementation; Mon. Wea. Rev. 142 4308-4325.CrossRefGoogle Scholar
  49. Tallapragada V, Bernardet L, Biswas M K, Gopalakrishnan M, Kwon Y, Liu Q, Marchok T, Sheinin D, Tong M, Trahan S, Tuleya R, Yablonsky R and Zhang X 2014 Hurricane Weather Research and Forecasting (HWRF) Model: 2014 Scientific Documentation.Google Scholar
  50. Torn R D 2016 Evaluation of atmosphere and ocean initial condition uncertainty and stochastic exchange coefficients on ensemble tropical cyclone intensity forecasts; Mon. Wea. Rev. 144 3487–3506.CrossRefGoogle Scholar
  51. WMO 2014 Tropical cyclone operational plan for the Bay of Bengal and the Arabian Sea; http://www.wmo.int/pages/prog/www/tcp/operational-plans.html.

Copyright information

© Indian Academy of Sciences 2018

Authors and Affiliations

  1. 1.Department of Marine Science and TechnologyUniversity of HormozganBandar AbbasIran

Personalised recommendations