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Status and Plans for Operational Tropical Cyclone Forecasting and Warning Systems in the North Indian Ocean Region

  • M. Mohapatra
  • B. K. Bandyopadhyay
  • Ajit  Tyagi

Abstract

The tropical warm Indian Ocean, like the tropical North Atlantic, the South Pacific and the northwest Pacific, is a breeding ground for the disastrous tropical cyclone (TC) phenomenon. TCs are accompanied by very strong winds, torrential rains and storm surges. The reduction of cyclone disasters depends on several factors including hazard analysis, vulnerability analysis, preparedness and planning, early warning, prevention and mitigation. The early warning is a major component and it includes skill in monitoring and prediction of cyclone, effective warning products generation and dissemination, coordination with emergency response units and the public perception about the credibility of the official predictions and warnings.

Keywords

Tropical Cyclone Storm Surge India Meteorological Department Multi Model Ensemble North Indian Ocean 
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. Dube, S.K. (2012). Prediction of storm surge in the Bay of Bengal. Tropical Cyclone Research and Review, 1: 67-74.Google Scholar
  2. Goyal, Suman, Mohapatra, M. and Sharma, A.K. (2013). Comparison of best track parameters of RSMC, New Delhi over satellite estimates over north Indian Ocean, Mausam, 64: 25-34.Google Scholar
  3. IMD (2003). Cyclone Manual. India Metereological Department, New Delhi.Google Scholar
  4. Knaff, J.A., Brown, D.P., Courtney, J., Gallina, G.M. and Beven III, J.L. (2010). An evaluation of Dvorak technique–based tropical cyclone intensity estimates. Wea. Forecasting, 25: 1362-1379.CrossRefGoogle Scholar
  5. Kotal, S.D., Kundu, P.K. and Roy Bhowmik, S.K. (2009). Analysis of cyclogenesis parameter for developing and nondeveloping low-pressure systems over the Indian Sea. Natural Hazards, 50: 389-402.CrossRefGoogle Scholar
  6. Kotal, S.D. and Roy Bhowmik, S.K. (2011). A multi-model ensemble (MME) technique for cyclone track prediction over the north Indian Sea. Geofizika, 28: 275-291.Google Scholar
  7. Kotal, S.D., Roy Bhowmik, S.K., Kundu, P.K. and Das, A.K. (2008). A statistical cyclone intensity prediction (SCIP) model for the Bay of Bengal. Journal of Earth System Science, 117: 157-168.CrossRefGoogle Scholar
  8. Kotal, S.D. and Bhattacharya, S.K. (2013). Tropical cyclone genesis potential parameter (GPP) and its application over north Indian Sea. Mausam, 64: 149-170.Google Scholar
  9. Kummerow, C., Olson, W.S. and Giglow, L. (1996). A simplified scheme for obtaining precipitation and hydrometeor profile from passive microwave sensor. IEEE. Trans, Geosci. Remote Sense., 34: 1213-1232.Google Scholar
  10. Mohapatra, M., Nayak, D.P., Sharma, R.P. and Bandyopadhyay, B.K. (2013). Evaluation of official tropical cyclone track forecast over north Indian Ocean by India Meteorological Department. Journal of Earth System Sciences (Accepted).Google Scholar
  11. Mohapatra, M., Sikka, D.R., Bandyopadhyay, B.K. and Tyagi, Ajit (2013). Outcomes and challenges of forecast demonstration project (FDP) on landfalling cyclones over the Bay of Bengal. Mausam, 64: 1-12.Google Scholar
  12. Raghavan, S. (2013). Observational aspects including weather radar for tropical cyclone monitoring. Mausam, 64: 89-96.Google Scholar
  13. RSMC, New Delhi (2012). Report on cyclonic disturbances over north Indian Ocean during 2011. IMD, New Delhi.Google Scholar
  14. RSMC, New Delhi (2013). Report on cyclonic disturbances over north Indian Ocean during 2012. IMD, New Delhi.Google Scholar

Copyright information

© Capital Publishing Company 2014

Authors and Affiliations

  • M. Mohapatra
    • 1
  • B. K. Bandyopadhyay
    • 1
  • Ajit  Tyagi
    • 1
  1. 1.India Meteorological Department, Mausam BhavanNew DelhiIndia

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