This study is an attempt to introduce a new strategy combining bogus vortex initialization and FDDA (Four Dimensional Data Assimilation) to predict the tropical cyclones, validated for Bay of Bengal region. Two intense tropical cyclone systems (SIDR and NARGIS) over Bay of Bengal were studied. SIDR was graded as a very severe cyclonic storm, nominated as super cyclone at landfall time, and had the life cycle during 11-16 November 2007, with an attained intensity in terms of the minimum central sea level pressure (CSP) of 944 hPa and the maximum wind speed (MW) of 115 knots. This cyclone system had movement first as northwesterly, later northward and finally northeast direction, crossing west Bangladesh coast around 17 UTC of 15 December. Cyclone SIDR was the most powerful cyclone to impact Bangladesh since 1991, with the death toll at approximately 3,406 and with estimated total damage and losses to be US$1.6 billion. NARGIS was graded as a very severe cyclonic storm, had the life cycle during 27 April-03 May 2008, with an attained intensity in terms of the minimum central sea level pressure (CSP) of 962 hPa and the maximum wind speed (MW) of 90 knots. The system moved in an eastnortheasterly direction followed by easterly direction crossing the southwest coast of Myanmar between 1200 to 1400 UTC of 2 May near 16.N. NARGIS was the most devastating cyclonic storm over the north Indian Ocean in recent years in terms of loss of life and property, with 84000 human deaths, and a total loss of about US $ 4 billion. Advanced Research WRF (Weather Research and Forecasting) model, developed and sourced from National Center for Atmospheric Research (NCAR), was used to make the numerical prediction experiments of the two cyclones under study. This ARW model system has versatility to choose the domain region of interest; horizontal resolution; interactive nested domains and with various options to choose parameterization schemes for convection, planetary boundary layer (PBL), explicit moisture; radiation and soil processes. ARW is designed to be a flexible, state-of-the-art atmospheric simulation system that is portable and efficient on available parallel computing platforms. ARW is suitable for use in a broad range of applications across scales ranging from meters to thousands of kilometers. Numerical prediction experiments were conducted, with/ without bogus vortex and with/ without FDDA. The model was designed to have two-way interactive three nested domains of 81-27-9 km, with the innermost domain covering the path of the cyclone system. ARW model was integrated starting from 72 hours prior to the landfall time of the cyclone system to have a prediction of the landfall. The initial and time varying boundary conditions were taken from the NCEP GFS fields, available at 0.5 degree spatial resolution and 6-hour time interval. For all the experiments the parameterization schemes of physical processes were same. For the experiments with bogus vortex, a vortex designed with observed characteristics of central sea level pressure, maximum wind and radius of maximum wind at the corresponding times as available from India Meteorological Department (IMD) was ingested into the analyses. The adopted methodology is first to generate initial conditions using NCEP FNL global analysis at two times, model initial time and at preceding 12-hour time point. FDDA nudging is adopted to run the model for the first 12-hour period, and prediction is obtained for the next 24-hours. From then on predicted vortex intensity and location were ingested into 12-hour prediction state using bogus vortex initialization, FDDA performed for the 12-hour period and prediction obtained for further 24-hours. This procedure of FDDA and vortex is performed till 72-hour prediction is obtained. This methodology is tested as errors of model prediction tend to increase with time and FDDA would check the deviation and under the assumption that the model has capability to produce good prediction up to 24-hours. The results from these experiments for each of the cyclone system were compared with IMD estimates for validation. The results indicate that ingestion of bogus vortex is necessary to properly initialize the vortex at the initial time, without which the intensity of the vortex is underestimated, especially during the first 24-36 hours of integration. FDDA without bogus vortex leads to underestimation during the FDDA period, as the model is driven towards a lower intensity in the global analyses. However 6-hour FDDA helped to reduce model predicted track errors, as the model integration during the first 6-hours was subjected to analysis nudging. These results are not shown in this abstract. The results from experiments with bogus vortex and FDDA are shown in Figure 1. The results show significant improvements, both in terms of intensification and movement when FDDA is performed along with ingestion of bogus vortex. The experiments with FDDA for 24 hours, with ingested bogus vortex during the FDDA period, show consistent reduction of track errors together with better prediction of intensification. FDDA is noted to reduce the overestimation of intensification and also reduction of track errors by 50% in the 72-hour prediction period. Out of the two case studies, the results pertaining to SIDR were much better as compared to those of NARGIS. In both the cases, the FDDA affects to control the intensity, thereby leading to underestimation of intensity but after the period of FDDA, the prediction improves with the model predicted intensification and movement as of the observations. The general bias of numerical models overestimating the weaker systems and underestimating the stronger systems seems to be alleviated by the adopted strategy to some extent. Though the methodology does not completely rectify the deficiencies, the suggested strategy of combining FDDA and bogus vortex ingestion shows promise in real time prediction.