Impact of Variational Data Assimilation for Simulating Tropical Cyclones over Bay of Bengal Using WRF-ARW

  • V. Yesubabu
  • C. V. Srinivas
  • K. B. R. R. Hari Prasad
  • S. S. V. S. Ramakrishna


Tropical cyclones, one of the most destructive of all the natural disasters, are capable of causing loss of life and extensive damage to property. The Bay of Bengal is a potentially energetic region for the development of cyclonic storms and about 7% of the global annual tropical storms form over this region with two cyclone seasons in a year. Tropical cyclones have great socio-economic concern for the Indian subcontinent. Precise forecasting of tropical cyclone intensity and track are important for the countries bordering the Bay of Bengal, especially India, Bangladesh and Myanmar due to significant socio-economic impact. There has been remarkable improvement in forecasting of the tropical cyclones with the development of high resolution atmospheric models and the global forecasting systems such as the National Centers for Environmental Predictions (NCEP) Global Forecasting System (GFS). Assimilation of available observations has been considered to be very important for accurate description of initial conditions in numerical models (Park and Zupanski, 2003; Navon, 2009; Pu et al., 2009). In particular, assimilation methods like variational approach has the additional advantage of assimilating observations by satisfying model dynamic and thermodynamic constraints through a set of independent balance equations (in 3DVAR) (Courtier et al., 1998).


Tropical Cyclone India Meteorological Department Global Forecast System Assimilation Experiment Tropical Cyclone Intensity 
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  1. Courtier, P. et al. (1998). The ECMWF implementation of three dimensional variational (3DVAR) data assimilation. Part I: Formulation. Quant. J. Roy. Meteor. Soc., 123: 1-26.Google Scholar
  2. Govindakutty, M., Chandrasekar, A. and Pradhan, Devendra (2010). Impact of 3DVAR assimilation of Doppler Weather Radar wind data and IMD observation for the prediction of a tropical cyclone. Int. Journal of Remote Sensing, 31(24): 6327- 6345.CrossRefGoogle Scholar
  3. Navon, I.M. (2009). Data assimilation for numerical weather prediction: A review. In: Park, S.K. and Xu, L. (eds), Data assimilation for atmospheric, oceanic and hydrologic applications. Springer, Berlin.Google Scholar
  4. 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
  5. Park, S.K. and Zupanski, D. (2003). Four-dimensional variational data assimilation for mesoscale and storm-scale applications. Meteor Atmos Phys, 82: 173-208.CrossRefGoogle Scholar
  6. Pattanayak, Sujata and Mohanty, U.C. (2008). A comparative study on performance of MM5 and WRF models in simulation of tropical cyclones over Indian seas. Current Science, 95(7): 925-936.Google Scholar
  7. Pattanayak, Sujata and Mohanty, U.C. (2010). Simulation of Track and Intensity of Gonu and Sidr with WRF-NMM Modeling System. In: Yassine Charabi (ed.), Indian Ocean Tropical Cyclones and Climate Change. Springer, Netherlands. DOI 10.1007/ 978-90-481-3109-9_12.Google Scholar
  8. Pu, Z., Li, X., Velden, C.S., Aberson, S.O. and Liu, W.T. (2009). The impact of aircraft dropsonde and satellite wind data on numerical simulation of two landfalling tropical storms during the tropical cloud system and processes experiment. Weather and Forecasting, 23: 62-79.CrossRefGoogle Scholar
  9. 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. Wea. Forecasing, 23: 460-476.CrossRefGoogle Scholar
  10. Singh, Randhir, Kishtawal, C.M., Pal, P.K. and Joshi, P.C. (2011). Assimilation of the multi-satellite data into the WRF model for track and intensity simulation of the Indian Ocean tropical cyclones. Meteor Atmos Phys, 111(3-4): 103-119.Google Scholar
  11. Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Duda, M.G., Huang, X-Y., Wang, W., Powers, J.G. (2008). A Description of the Advanced Research WRF Version 3. NCAR Boulder, Colarado.Google Scholar
  12. Srinivas, C.V., Bhaskar Rao, D.V., Yesubabu, V., Baskaran, R. and Venkatraman, B. (2012). Tropical cyclone predictions over the Bay of Bengal using the high- resolution advanced research weather research and forecasting model. Q. J. R. Meteorol. Soc. DOI: 10.1002/qj.2064.
  13. Srinivas, C.V., Yesubabu, V., Venkatesan, R. and Ramakrishna, S.S.V.S. (2010). Impact of assimilation of conventional and satellite meteorological observations on the numerical simulation of a Bay of Bengal Tropical Cyclone of November 2008 near Tamilnadu using WRF model. Meteor. Atmos. Phys., 110: 19-44.Google Scholar

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© Capital Publishing Company 2014

Authors and Affiliations

  • V. Yesubabu
    • 1
  • C. V. Srinivas
    • 2
  • K. B. R. R. Hari Prasad
    • 2
  • S. S. V. S. Ramakrishna
    • 3
  1. 1.Computational Earth Sciences, Centre for Development of Advanced ComputingPuneIndia
  2. 2.Radiological Safety DivisionIndira Gandhi Centre for Atomic ResearchKalpakkamIndia
  3. 3.Department of Meteorology and OceanographyAndhra UniversityVishakhapatnamIndia

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