Mesoscale Modelling for Tropical Cyclone Forecasting over the North Indian Ocean

Abstract

The coastal regions of Bay of Bengal (BoB) and Arabian Sea (AS) experience severe damage due to landfalling tropical cyclones (TCs). The synoptic and statistical methods have limitations in predicting the track and intensity beyond 24 hours (Mohanty and Gupta, 1997). However, the numerical forecast using dynamical models can provide better forecast guidance for genesis, intensity and movement of TCs up to 72 hours (Rao and Bhaskar Rao, 2003; Mandal et al., 2004; Osuri et al., 2012a) and helps in the disaster mitigation planning. Hence, it is necessary to evaluate the comprehensive performance of such dynamical models in track and intensity forecasts of TCs. Davis et al. (2008) and Osuri et al. (2012a) showed that real-time TC forecast of ARW (Advanced Research Weather Research and Forecasting) model is generally competitive with, and occasionally superior to, other operational forecasts for track and intensity of landfalling TCs over Atlantic and BoB respectively. Wang et al. (2006) demonstrated that error growth in ARW model forecasts is noticeably slow as the forecast length increases.

Keywords

Doppler Weather Radar Simplify Arakawa Schubert Global Telecommunication System Tropical Cyclone Forecast Doppler Weather Radar Data 
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.

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References

  1. Davis, C.A., Wang, W., Chen, S., Chen, Y., Corbosiero, K., DeMaria, M., Dudhia, J., Holland, G., Klemp, J., Michalakes, J., Reeves, H., Rotunno, R. and Xiao, Q. (2008). Prediction of landfalling hurricanes with the advanced hurricane WRF model. Mon. Wea. Rev., 136: 1990-2005.CrossRefGoogle Scholar
  2. Gao,, J., Xue, M., Shapiro, A., Droegemeier, K.K. (1999). A variational method for the analysis of three-dimensional wind fields from two Doppler radars. Mon. Weather Rev., 127: 2128-2142.Google Scholar
  3. Janjic, Z.I. (2001). Nonsingular Implementation of the Mellor-Yamada level 2.5 scheme in the NCEP meso model. NCEP Office Note No. 437.Google Scholar
  4. Janjic, Z.I. (2003a). A non-hydrostatic model based on a new approach. Met. and Atmos. Phy., 82: 271-285.CrossRefGoogle Scholar
  5. Janjic, Z.I. (2003b). The NCEP WRF core and further development of its physical package. 5th International SRNWP workshop on Non-hydrostatic modeling, bad Orb, Germany, 27-29 October 2003.Google Scholar
  6. 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. Natural Hazards, 31: 391-414.CrossRefGoogle Scholar
  7. Mohanty, U.C. and Gupta, A. (1997). Deterministic methods for prediction of tropical cyclone tracks. Mausam, 48: 257-272.Google Scholar
  8. Osuri, K.K., Mohanty, U.C., Routray, A., Kulkarni, Makarand A. and Mohapatra, M. (2012a). Sensitivity of physical parameterization schemes of WRF model for the simulation of Indian seas tropical cyclones. Natural Hazards, 63: 1337-1359. DOI  10.1007/s11069-011-9862-0.CrossRefGoogle Scholar
  9. Osuri, K.K., Mohanty, U.C.. Routray, A. and Mohapatra, M. (2012b). Impact of Satellite Derived Wind Data Assimilation on track, intensity and structure of tropical cyclones over North Indian Ocean. International Journal of Remote Sensing, 33: 1627-1652. DOI: 10.1080/01431161.2011.596849.CrossRefGoogle Scholar
  10. Pattanayak, S., Mohanty, U.C. and Osuri, KK. (2012). Impact of parameterization of physical processes on simulation of track and intensity of tropical cyclone Nargis (2008) with WRF-NMM model. The World Scientific Journal, doi: 10.1100/2012/ 671437.Google Scholar
  11. Pu, Z., Xuanli, Li, Velden, C.S., Aberson, S.D. and Liu, W.T. (2008). The Impact of aircraft Dropsonde and Satellite Wind Data on Numerical Simulations of Two Landfalling Tropical Storms during the Tropical Cloud Systems and Processes Experiment. Weather and Forecasting, 23: 62-79.CrossRefGoogle Scholar
  12. 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 Ind Nat Sci Acad, 69: 523-541.Google Scholar
  13. Singh, R., Pal, P.K., Kishtawal, C.M. and Joshi, P.C. (2008). The Impact of variational assimilation of SSM/I and QuikSCAT Satellite observations on the Numerical Simulation of Indian Ocean Tropical Cyclones. Weather and Forecasting, 23: 460-476.CrossRefGoogle Scholar
  14. Wang, W., Davis, C., Klemp, J., Holland, G. and DeMaria, M. (2006). Evaluation of WRF-ARW High-Resolution Tropical Storm Forecasts in 2005 Season. 27th Conference on Hurricanes and Tropical Meteorology, CA 24-28 April 2006 (Paper 2A.5).Google Scholar
  15. Xiao, Q., Kuo, Y.-H., Sun, J., Lee, W.-C., Barker, D.M. and Lim, E. (2007). An approach of radar reflectivity assimilation and its assessment with the inland QPF of Typhoon Rusa (2002) at landfall. J. Appl. Meteorol. Climatol., 46: 14-22.CrossRefGoogle Scholar
  16. Zhang, X., Xiao, Qingnong and Fitzpatrick, Patrick J. (2007). The Impact of Multisatellite Data on the Initialization and Simulation of Hurricane Lili’s (2002) Rapid Weakening Phase. Mon. Wea. Rev., 135: 526-548.CrossRefGoogle Scholar

Copyright information

© Capital Publishing Company 2014

Authors and Affiliations

  • U. C. Mohanty
    • 1
  • Krishna K. Osuri
    • 1
  • S. Pattanayak
    • 1
  1. 1.Indian Institute of Technology DelhiNew DelhiIndia

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