Abstract
There has been a massive up-gradation of weather forecasting capabilities in India under the modernization programme of the Government of India, which covers various components such as atmospheric observation network; strengthening of computing facilities; data integration and product generation; and dissemination of information to an optimum level. It has improved forecasting capabilities for high impact weather events like cyclones, severe thunderstorm, heavy rainfall and floods in a significant manner. IMD now has a network of automatic weather stations, Doppler Weather Radars (DWR), state-of-the-art upper air systems etc. These observations are now being used to run numerical prediction models on High Performance Computing Systems (HPCS). Global Forecast System (GFS T574/L64) was made operational at IMD New Delhi, incorporating Global Statistical Interpolation (GSI) scheme as the global data assimilation for the forecast up to seven days. Mesoscale forecast system WRF (ARW) with 3DVAR data assimilation is being operated daily twice, at 27 km, 9 km and 3 km horizontal resolutions for the forecast up to three days using initial and boundary conditions from the IMD GFS T574. At ten other regional centres, very high resolution mesoscale models (WRF at 3 km resolution) are made operational with the installation of High End Server. Doppler weather and mesoscale dynamical model-based Nowcast system was made operational for the national Capital of Delhi. Polar WRF is implemented to provide day-to-day short range (48 hours) weather forecast for the Maitri region over Antarctica. District level quantitative five days weather forecasts based on Multi-Model Ensemble (MME) system are being generated to support Agro-Meteorological Advisory Service of India. All these NWP products are routinely made available on the IMD web site www.imd.gov.in.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Elsberry, R.L., T.D.B. Lambert and M.A. Boothe, 2007: Accuracy of Atlantic and eastern North Pacific tropical cyclone intensity forecast guidance. Wea. Forecasting, 22, 747-762.
Goerss, J.S., 2000: Tropical cyclone track forecasts using an ensemble of dynamical models. Mon. Wea. Rev., 128, 1187-1193.
Houze, R.A., S.S. Chen, B.F. Smull, W.C. Lee and M.M. Bell, 2007: Hurricane intensity and eyewall replacement. Science, 315, 1235-1238.
Kotal, S.D., Ajit Tyagi and S.K. Roy Bhowmik, 2011: Potential vorticity diagnosis of rapid intensification of very severe cyclone Giri (2010) over the Bay of Bengal. Natural Hazards, online DOI 10.1007/s/11069-011-0024-1.
Kotal, S.D. and S.K. Roy Bhowmik, 2011: A multi-model ensemble (MME) technique for cyclone track prediction over the North Indian Sea. Geofizika, 28: 275-291.
Kotal, S.D., P.K. Kundu and S.K. Roy Bhowmik, 2009: Analysis of cyclogenesis parameter for developing and non-developing low pressure systems over the Indian Sea. Nat. Hazards, 50, 389-402.
Kotal, S.D., S.K. Roy Bhowmik, P.K. Kundu and A.K. Das, 2008a: A Statistical Cyclone Intensity Prediction (SCIP) Model for Bay of Bengal. J. Earth. Sys. Sci., 117, 157-168.
Kotal, S.D., P.K. Kundu and S.K. Roy Bhowmik, 2008b: An analysis of Sea Surface Temperature and Maximum Potential Intensity of Tropical Cyclones over the Bay of Bengal between 1981 and 2000. Meteorological Applications, 16, 169-177.
McBride, J.L. and R.M. Zehr, 1981: Observational analysis of tropical cyclone formation. Part II: Comparison of non-developing versus developing systems. J. Atmos. Sci., 38, 1132-1151.
Mackey, B.P. and T.N. Krishnamurti, 2001: Ensemble Forecast of a Typhoon Flood Event. Wea. and Forecasting, 16, 399-415.
Roy Bhowmik, S.K. and S.D. Kotal, 2010: A dynamical statistical model for prediction of a tropical cyclone. Marine Geodesy, 33, 412-425.
Roy Bhowmik, S.K., S.D. Kotal and S.R. Kalsi, 2007: Operational tropical cyclone intensity prediction—An empirical technique. Nat. Hazards, 41, 447-455.
Roy Bhowmik, S.K., S.D. Kotal and S.R. Kalsi, 2005: An empirical model for predicting decaying rate of tropical cyclone wind speed after landfall over Indian region. Journal of Applied Meteorology, 44, 179-185.
Roy Bhowmik, S.K., 2003: An evaluation of cyclone genesis parameter over the Bay of Bengal using model analysis. Mausam, 54, 351-358.
Sen Roy, Soma, S.K. Roy Bhowmik, V. Lakshmanan and S.B. Thampi, 2010: Doppler Radar-based Nowcasting of the Bay of Bengal Cyclone Ogni of October 2006. J. Earth Sci. Sys., 119(2), 183-199.
Vijaya Kumar, T.S.V., T.N. Krishnamurti, M. Fiorino and M. Nagata, 2003: Multimodel superensemble Forecasting of Tropical Cyclones in the Pacific. Mon. Wea. Rev., 131, 574-583.
Weber, H.C., 2003: Hurricane Track Prediction Using a Statistical Ensemble of Numerical Models. Mon. Wea. Rev., 131, 749-770.
Williford, C.E., T.N. Krishnamurti, Correa Torres Ricardo, Cocke, Steven, Zaphiris Christidis and T.S. Vijaya Kumar, 2003: Real-Time Multimodel Superensemble Forecasts of Atlantic Tropical Systems of 1999. Mon. Wea. Rev., 131, 1878-1894.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Capital Publishing Company
About this chapter
Cite this chapter
Bhowmik, S.K.R. (2016). Operational Tropical Cyclone Forecasts Models at IMD and Their Performance. In: Mohanty, U.C., Gopalakrishnan, S.G. (eds) Advanced Numerical Modeling and Data Assimilation Techniques for Tropical Cyclone Prediction. Springer, Dordrecht. https://doi.org/10.5822/978-94-024-0896-6_17
Download citation
DOI: https://doi.org/10.5822/978-94-024-0896-6_17
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-024-0895-9
Online ISBN: 978-94-024-0896-6
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)