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Tropical Cyclones (TCs), 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 approximately 7 % of the global annual tropical storms form over this region with two cyclone seasons in a year (Gray, Mon Weather Rev 96:669–700, 1968). Much of the TC related damage is attributed to storm surges, high winds, damage associated with strong thunderstorm complexes and TC-induced heavy rainfall. Predicting rainfall associated with TCs is a major operational challenge. Over the last few decades flooding from TCs at landfall has become a threat to human lives in India. Although track-forecasts continue to improve, quantitative precipitation forecasts (QPF) for TCs have shown little skill. One of the uncertainties in QPF is a lack of precipitation data over the open oceans to evaluate and validate numerical weather prediction (NWP) model results. TC rainfall forecasting techniques are lagging behind those of the track forecast. However, significant progress has been made in recent years due to the advance in remote sensing observations and the improvement of mesoscale models and data assimilation techniques. Until relatively recently, TC rainfall prediction was carried out mainly using empirical methods and subjective experience on the part of the forecaster. However, advanced techniques for Quantitative Precipitation Estimate (QPE) are currently employed in operational applications in some major forecasting centres, which already have greatly improved the forecasting for TC-related rainfall. Minakshi Devi et al. (Nat Hazard Risk 5(2):93–114. doi: 10.1080/19475705.2013.775186, 2014) have shown predicted tracks of a few cyclonic events such as SIDR (Nov, 2007), Aila (May, 2009) and Laila (May, 2010) along with their contribution to precipitation in the NE India. Recent studies have indicated that some high resolution dynamical model simulations are capable of capturing the rainfall pattern of TCs. Lee and Choi (J Geophys Res 115:12105. doi: 10.1029/2009JD012581, 2010) investigated the torrential rainfall associated with Typhoon Rusa in South Korea in 2002 through numerical simulation using Weather Research Forecast (WRF) model. Haggag and Yamashita (Jl. of Int. Dev Coop 15(1–2):47–63, 2009) studied the hydro-meteorological features of TC Gonu using coupled atmosphere, ocean and land surface modelling with an atmospheric component based on the MM5 model. There are other studies on performance of models including, Raju et al. (Nat Hazard 63:1361–1374. doi: 10.1007/s11069-011-9918-1, 2012), Osuri et al. (Int J Remote Sens 33:5, 2012), Abhilash et al. (Pure Appl Geophys 169:2047–2070, 2012), Routray et al. (Pure Appl Geophys 170:2329–2350. doi: 10.1007/s00024-013-0648-z, 2013) and Srivastava, et al. (Nat Hazard. doi: 10.1007/s11069-011-9835-3, 2011). All these studies indicate that the high resolution along with the improved data assimilation, especially DWR and satellite based data can improve the rainfall forecast by the models. Though heavy rainfall prediction is still a challenge, Hurricane WRF (HWRF) is a promising model for this purpose. A number of studies have examined the track, intensity, structure and genesis of TCs, however very few studies have considered the rainfall dynamics associated with TCs. The Hydro-meteorological aspects of TC have been dealt mainly in terms of coastal storm surge and inundation studies only.
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Abhilash, S, Sahai, A.K, Mohanakumar, K, George, J.P. and Das, S. (2012). Assimilation of Doppler Weather Radar Radial Velocity and Reflectivity Observations in WRF-3DVAR system for Short-Range Forecasting of Convective Storms. Pure and Applied Geophysics, 169, 2047–2070. CrossRef
Chakraborty, M, Panigrahy, S. and Sharma, S.A. (1997). Discrimination of rice crop grown under different cultural practices using temporal ERS-1 SAR data. ISPRS Journal of Photogrammetry and Remote Sensing, 52, 183–191. CrossRef
Debnath, G.C. and Mandal, P. (2012). The role of orography in producing extremely on the role of orography in producing extremely heavy rainfall induced by cyclonic storm Aila – A WRF model analysis for disaster management. In: WMO Technical Document on Proceedings of Second International Conference on Indian Ocean Tropical Cyclones and Climate Change (IOTCCC-II) held at New Delhi, India during 14–17 Feb, 2012, WWRP 2013–4.
Gray, M.W. (1968). Global view of the origin of tropical disturbances and storms. Monthly Weather Review, 96, 669–700. CrossRef
Haggag, M. and Yamashita, T. (2009). Environmental simulator application to the analysis of the tropical cyclone Gonu in 2007. Jl. of Int. Development and Cooperation, 15, 1–2, 47–63.
Mitra, A.K, Bohra, A.K, Rajeevan, M. and Krishnalmurti, T.N. (2009). Daily Indian precipitation analysis formed from a merge of rain-guage data with TRMM TMPA satellite derived rainfall estimates. Journal of the Meteorological Society of Japan, 87A, 265–279. CrossRef
Misra, T, Rana, S.S, Desai, N.M, Dave, D.B, Jyoti, R, Arora, R.K, Rao, C.V.N, Bakori, B.V, Neelakanthan, R. and Vachchani, J.G. (2013). Aperture Radar payload on-board RISAT-1: Configuration, technology and Performance. Current Science, 104, 446–461.
Osuri, K.K, Mohanty, U.C, Routray, A. and Mohapatra, M. (2012). The impact of satellite-derived wind data assimilation on track, intensity and structure of tropical cyclones over the North Indian Ocean. International Journal of Remote Sensing, 33, 5. CrossRef
Srivastava, K, Rashmi, B. and Roy Bhowmik, S.K. (2011). Assimilation of Indian Doppler Weather Radar observations for simulation of mesoscale features of a land-falling cyclone. Natural Hazards, doi: 10.1007/s11069-011-9835-3.
- Hydro-Meteorological Aspects of Tropical Cyclone Phailin in Bay of Bengal in 2013 and the Assessment of Rice Inundation due to Flooding
S. S. Ray
S. K. Singh
A. K. Das
B. A. M. Kannan
B. K. Bandyopadhyay