Sensitivity of simulated cyclone Gonu intensity and track to variety of parameterizations: Advanced hurricane WRF model application
- 42 Downloads
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.
KeywordsTropical cyclone Gonu surface fluxes PBL cumulus convection AHW
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).
- 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
- Anthes R A 1982 Tropical cyclones: Their evolution, structure and effects; Meteor. Mono. Am. Meteor. Soc., 208p.Google Scholar
- 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
- 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
- Garratt J R 1992 The Atmospheric Boundary Layer; Cambridge University Press, Cambridge.Google Scholar
- 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
- 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
- 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/.
- 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
- 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
- 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
- Mohanty U C and Gupta A 1997 Deterministic methods for prediction of tropical cyclone tracks; Mausam 48 257–272.Google Scholar
- 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. Rev. 137 3675–3698.CrossRefGoogle Scholar
- 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
- 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
- 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
- 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.