Bay Of Bengal Tropical Cyclones Research Articles

Forecasting of tropical cyclones ASANI (2022) and MOCHA (2023) over the Bay of Bengal - real time challenges to forecasters

This study examines the track and intensity forecasts of two typical Bay of Bengal tropical cyclones (TC) ASANI and MOCHA. The analysis of various Numerical Weather Prediction (NWP) model forecasts [ECMWF (European Centre for Medium range Weather Forecast), NCEP (National Centers for Environmental Prediction), NCUM (National Centre for Medium Range Weather Forecast-Unified Model), IMD (India Meteorological Department), HWRF (Hurricane Weather Research and Forecasting)], MME (Multi-model Ensemble), SCIP (Statistical Cyclone Intensity Prediction) model, and OFCL (Official) forecasts shows that intensity forecasts of ASANI and track forecasts of MOCHA were reasonably good, but there were large errors and wide variation in track forecasts of ASANI and in intensity forecasts of MOCHA. Among all model forecasts, the track forecast errors of IMD model and MME were least in general for ASANI and MOCHA respectively. Also, the landfall point forecast errors of IMD were least for ASANI, and the MME and OFCL forecast errors were least for MOCHA. No model is found to be consistently better for landfall time forecast for ASANI, and the errors of ECMWF, IMD and HWRF were least and of same order for MOCHA. The intensity forecast errors of OFCL and SCIP were least for ASANI, and the forecast errors of HWRF, IMD, NCEP, SCIP and OFCL were comparable and least for MOCHA up to 48 h forecast and HWRF errors were least thereafter in general. The ECMWF model forecast errors for intensity were found to be highest for both the TCs. The results also show that although there is significant improvement of track forecasts and limited or no improvement of intensity forecast in previous decades but challenges still persists in real time forecasting of both track and intensity due to wide variation and inconsistency of model forecasts for different TC cases.

Synergistic Effects of Bay of Bengal Tropical Cyclones and Tibetan Plateau Vortices on Water Vapor Transport over the Tibetan Plateau in Early Summer

Synergistic Effects of Bay of Bengal Tropical Cyclones and Tibetan Plateau Vortices on Water Vapor Transport over the Tibetan Plateau in Early Summer

Variations in intensity of Bay of Bengal tropical cyclones with surface latent heat flux and other parameters

This study has been undertaken to find out the variation of central pressure (intensity) of intense Tropical Cyclones (TCs) with Sea Surface Temperature (SST), Mid-tropospheric Relative Humidity (MRH), Mid-tropospheric Instability (MI), Vertical Wind Shear (VWS), 200-hPa divergence, and Surface Latent Heat Flux (SLHF) during the lifetime intense TCs. This study also aims to determine the most crucial parameter which shows the highest correlation with central pressure (intensity) of intense TCs during their lifetime. Out of all these parameters, SLHF is highly correlated (R = 0.74) with the central pressure (intensity) of intense TCs. Increase and decrease of SLHF correspond to decrease and increase of TCs central pressure (increase and decrease in TCs intensity). The highest SLHF corresponds to the lowest central pressure (highest intensity).

Generation of Extreme Precipitation over the Southeastern Tibetan Plateau Associated with TC Rashmi (2008)

Abstract In this study, the development of an extreme precipitation event along the southeastern margin of the Tibetan Plateau (TP) by the approach of Tropical Cyclone (TC) Rashmi (2008) from the Bay of Bengal is examined using a global reanalysis and all available observations. Results show the importance of an anomalous southerly flow, resulting from the merging of Rashmi into a meridionally deep trough at the western periphery of a subtropical high, in steering the storm and transporting tropical warm–moist air, thereby supplying necessary moisture for precipitation production over the TP. A mesoscale data analysis reveals that (i) the Rashmi vortex maintained its TC identity during its northward movement in the warm sector with weak-gradient flows; (ii) the extreme precipitation event occurred under potentially stable conditions; (iii) topographical uplifting of the southerly warm–moist air, enhanced by the approaching vortex with some degree of slantwise instability, led to the development of heavy to extreme precipitation along the southeastern margin of the TP; and (iv) the most influential uplifting of the intense vortex flows carrying ample moisture over steep topography favored the generation of the record-breaking daily snowfall of 98 mm (in water depth), and daily precipitation of 87 mm with rain–snow–rain changeovers at two high-elevation stations, respectively. The extreme precipitation and phase changeovers could be uncovered by an unusual upper-air sounding that shows a profound saturated layer from the surface to upper troposphere with a moist adiabatic upper 100-hPa layer and a bottom 100-hPa melting layer. The results appear to have important implications to the forecast of TC-related heavy precipitation over high mountains. Significance Statement This study attempts to gain insight into the multiscale dynamical processes leading to the development of an extreme precipitation event over the southeastern margin of the Tibet Plateau as a Bay of Bengal tropical cyclone (TC) approached. Results show (i) the importance of an anomalous southerly flow with a wide zonal span in steering the relatively large-sized TC and transporting necessary moisture into the region; and (ii) the subsequent uplifting of the warm and moist TC vortex by steep topography, producing the extreme precipitation event under potentially stable conditions, especially the record-breaking daily snowfall of 98 mm (in water depth). The results have important implications to the forecast of TC-related heavy precipitation over the Tibet Plateau and other high mountainous regions.

ENSO influence on Bay of Bengal cyclogenesis confined to low latitudes

The low-latitudinal cyclones (LLCs, originating between 5°N–10°N) constitute ≈40% of tropical cyclones (TCs) formed in the Bay of Bengal (BoB). We investigate the interannual variability of post-monsoonal (October to December) BoB LLCs and their teleconnection with El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). It is found that the years with the fewer number of BoB LLCs are associated with anomalous equatorial easterlies that are largely connected with the El Niño and positive IOD. Likewise, equatorial westerly phases, often associated with the La Niña and negative IOD years, favour the LLC formation by providing the initial spin-up required for cyclogenesis. This teleconnection between ENSO/IOD and BoB TC frequency is confined in the low-latitudinal region with little influence for cyclogenesis north of 10°N during ENSO and IOD except during negative IOD. These results may help extend the lead time and improve the seasonal prediction of BoB TCs.

Impact of climate change on intense Bay of Bengal tropical cyclones of the post-monsoon season: a pseudo global warming approach

Tropical cyclones (TCs) that make landfall over India’s east coast are responsible for significant loss of life along affected coastlines. TCs forming over the Bay of Bengal (BoB) region in October, November, and December have, in the past, intensified significantly at the time of landfall. The effects of climate change on TCs of different strengths and their characteristics such as track, intensity, precipitation, and convective available potential energy over the BoB region have not been well studied. This study explores the effects of climate change on two TCs of very severe cyclonic storm (VSCS) category (TC Vardah and TC Madi), and two other TCs of extremely severe cyclonic storm (ESCS) category (TC Hudhud and TC Phailin) formed over BoB, both in the short term (2035) and long term (2075). The high-resolution Weather Research and Forecasting (WRF) model is used to simulate the TCs under current and future climate conditions. The simulated TC track and intensity in the current climate agree well with the observations. To explore the impacts of climate change on TCs, the mean climate change signal, computed from future projections of the Community Climate System Model (CCSM4) in different representative concentration pathway (RCP) scenarios, is added to current climate conditions by using the pseudo global warming method. Results show a climate change-related reduction in TC translation speed, deepening of TC core, increased maximum surface wind, and increased precipitation over land in future RCP (4.5, 6.0, and 8.5) scenarios. The TCs in future RCP scenarios are seen to be more intensified compared to current climate simulations. Results demonstrate that all VSCS and ESCS category TCs considered in this study are likely to further intensify to the next higher category level with respect to their current classification, particularly in the far future RCP 6.0 and in the far future RCP 8.5 scenarios. The cyclone damage potential index of TC Vardah, TC Hudhud, and TC Phailin is projected to increase in a future warming climate.

An improved potential intensity estimate for Bay of Bengal tropical cyclones

The Bay of Bengal (BoB) is the most dangerous tropical cyclone (TC) region to humans in the world due to a combination of geographical and environmental factors. TC potential intensity (PI) estimates are a useful forecasting tool for evaluating favorable thermodynamic conditions for TC intensification; however, it assumes an idealized environment which omits several TC–environmental interactions. The present study compares the PI calculated using the SST (SST-PI) and the PI calculated taking into account ocean surface cooling and vertical wind shear (Environmental-PI) to the intensification rates of BoB TCs. A significant improvement is found in terms of explained variance when using Env-PI (13.5%) rather than SST-PI (3.5%). For slower moving TCs, the improvement is larger between Env-PI (25.5%) and SST-PI (4.9%). This study demonstrates that including an estimate of both oceanic and dynamical TC interactions in PI estimates lead to an improved estimate of future TC intensification in the BoB.

Cyclone Fani: the tug-of-war between regional warming and anthropogenic aerosol effects

Before Cyclone Amphan took place in 2020, Cyclone Fani (May 2019) is the strongest pre-monsoon cyclone in the Bay of Bengal (BOB) since 1991, killing 90 people in eastern India and Bangladesh while causing US$1.81 billion of damages. Fani developed during a period of high concentration of anthropogenic aerosols in the BOB with abnormally high sea surface temperature (SST), thereby presenting an opportunity to understand the compound effects of atmospheric aerosols and regional climate warming on a tropical cyclone. A quantitative attribution analysis was conducted using the Weather Research and Forecasting model with chemistry (WRF-Chem) run at the convection-permitting (4 km) grid spacing, accompanied by an ensemble of coarser-resolution simulations to quantify the uncertainty. The removal of post-1990 trends in the tropospheric variables and SST from WRF-Chem’s initial conditions (IC) and boundary conditions (BC, including the lateral and lower boundary conditions) resulted in a reduction of cyclone precipitation by about 51% during the 5 d of April 28-May 2. The removal of tropospheric warming shows approximately twice as strong an effect on Fani (39% reduction in precipitation) as that of SST warming (22% reduction). When aerosol’s direct and indirect effects were removed from the simulations, i.e., no aerosol influence on radiation and cloud microphysics, Fani initially strengthened but later weakened, as measured by geopotential height and precipitation amounts. These results suggest that aerosol and its interaction with the atmosphere acted to mitigate the strengthening effect of anthropogenic warming on Fani, but was not strong enough to entirely counteract it. Although the ensemble of coarser simulations appears to overestimate Cyclone Fani in terms of precipitation, the direction of the effects is in agreement with that obtained from the 4 km simulations. Given the increasing anthropogenic aerosols in the BOB, future attribution studies using more sophisticated dynamical aerosol models on BOB tropical cyclones are urged.

Climatological characteristics of Bay of Bengal tropical cyclones: 1972–2017

The present study is an attempt to investigate the spatial and temporal climatology of tropical cyclone (TC) activity in the Bay of Bengal (BoB) based on the Joint Typhoon Warning Centre (JTWC) best track data for the period 1972–2017. A total of 152 TCs, with a rate of 3.30 TCs per year, were formed in the BoB during the 46-year period. A large interannual variability was witnessed in TC activity with non-significant upward or downward trend in their frequency, intensity, duration, accumulated cyclone energy (ACE), and power dissipation index (PDI). Majority of TCs were formed between the 5° N to 16° N latitudes and made their landfall over the coasts of India, Bangladesh, Myanmar, and Sri Lanka. About two-thirds of annual TCs occurred during the post-monsoon season, whereas monthly distribution exhibited a unique bimodal pattern. The number of cyclonic storms (34–47 kt) showed a slightly increasing trend, whereas intense cyclonic storms (≥ 48 kt) showed a slightly decreasing trend. Additionally, about 40% and 11% of the TC events were found intensifying at 15 kt 24 h−1 and rapidly intensifying at 30 kt 24 h−1, respectively in the BoB. It is believed that the results of the present study will update the current knowledge which will assist the scientific community as well as academicians.

Tropical cyclone activity over Bay of Bengal in relation to El Niño‐Southern Oscillation

AbstractThe present paper investigates the impact of El Niño‐Southern Oscillation (ENSO) on the Bay of Bengal tropical cyclone (TC) activity and associated alterations in environmental conditions during post‐monsoon (October–December) season for a period of 44 years (1972–2015). The analysis reveals that the post‐monsoon season TCs frequency, accumulated cyclone energy (ACE) and power dissipation index (PDI) values are negatively correlated with the Niño 3.4 sea surface temperature (SST) anomalies (significant at the 95% confidence level). La Niña years are characterized by more frequent and intense cyclonic events compared with El Niño years. The mean ACE and PDI values are approximately two times higher in La Niña than El Niño years. The mean number of TC days per year is also higher in La Niña (7.64 days) than El Niño (3.68 days) years (significant at 95% confidence level). In addition, a significant shift in genesis locations, tracks and landfalling locations of TCs has been observed under different ENSO phases. The mean genesis location of TCs have shifted eastward with tendency of more recurving tracks in La Niña than El Niño years. The presence of strong convective activity, reduced vertical wind shear, high SST (≥28°C), enhanced mid‐tropospheric relative humidity and low‐level cyclonic circulation aids the TCs formation and strengthening during La Niña and vice‐versa in El Niño conditions.

A Conceptual Framework for the Scale‐Specific Stochastic Modeling of Transitions in Tropical Cyclone Intensities

At any given time, a tropical cyclone (TC) vortex has multiple intensity pathways that are possible. We conceptualize this problem as a scenario where each of the TC's intensity pathways is a distinct attractor basin, and a combination of several external and internal factors across multiple scales dictates as to which of the many pathways the TC vortex actually takes. As with any complex system, it is difficult to know the details of the multiscale processes that cause or initiate the tipping of the TC vortex into an attractor basin. A stochastic shock arising from any of the various scales within a TC vortex and the subsequent cross‐scale energy transactions may rapidly increase the probability of the vortex intensifying or weakening. To address this problem and apply our conceptual framework to actual TC case studies, we formulate a novel scale‐specific stochastic model that examines the multiscale energetics at and across individual wave numbers within the TC vortex. The stochastic term is modeled in a realistic manner in that the lower and higher wave numbers are treated differently. High‐resolution Hurricane Weather and Research Forecast model outputs of two Bay of Bengal TCs, Phailin (intensifying) and Lehar (weakening), are used as case studies. An ensemble of intensity pathways is generated, and the nonstationary probability distributions of the intensity transitions at each time are examined. Our approach is another step toward an improved understanding of the stochastic dynamics of multiscale transitions of a TC vortex.

Premonsoon/Postmonsoon Bay of Bengal Tropical Cyclones Intensity: Role of Air‐Sea Coupling and Large‐Scale Background State

AbstractThe densely populated Bay of Bengal (BoB) rim witnesses the deadliest tropical cyclones (TCs) globally, before and after the summer monsoon. Previous studies indicated that enhanced salinity and reduced thermal stratification reduce cooling under BoB TCs after the monsoon, suggesting that air‐sea coupling may favor stronger TCs during that season. Using observations and simulations from a one fourth degree regional ocean‐atmosphere model, we show that BoB TCs are stronger before the monsoon due to a more favorable large‐scale background state (less vertical wind shear and higher sea surface temperature). Air‐sea coupling however alleviates this background state influence, by reducing the number of premonsoon intense TCs, because of larger cooling and reduced upward enthalpy fluxes below TCs during that season. As the impact of air‐sea interactions on BoB TCs is largest for intense TCs, it should be further investigated for Category 3 and above TCs, which are not reproduced at one fourth degree resolution.

Moisture Budget of the Tropical Cyclones Formed over the Bay of Bengal: Role of Soil Moisture After Landfall

In the present study, the water budget of the Bay of Bengal tropical cyclones at varying intensities is analyzed. Results show that rainfall is not directly related to the intensities of tropical cyclones. A secondary peak in precipitation after landfall causes huge damage through floods and mud slides. The analysis of the water budget shows that the moisture flux convergence was the dominant term before landfall and contributes to 61% of the rainfall, while the remaining 39% is contributed by evaporation. After landfall, evaporation contributed 63% of the rainfall and 37% of rainfall was contributed by moisture flux convergence. The contribution of evaporation changed little with time in all the 12 case studies. Out of the 12 cyclones of varying intensities, seven cyclones either showed a secondary peak in precipitation or maintained a high rainfall over land. For the high rainfall over land, after the landfall, soil moisture was found to be important both in the observation and simulations of the Weather Research and Forecasting model. The predicted cyclone track errors are reduced in the model experiment with soil moisture, while the predicted cyclone intensity errors are less in the experiment without soil moisture. Accurate soil-moisture data are required for better prediction of cyclone track and their intensities.

Predecessor rain events over China’s low-latitude highlands associated with Bay of Bengal tropical cyclones

A predecessor rain event (PRE) is defined as organized heavy rainfall that occurs far ahead of tropical cyclone (TC), yet directly associated with the deep tropical moisture originating from the TC vicinity. The PREs occurring over China’s low-latitude highlands (CLLH) in association with Bay of Bengal TCs were investigated from 1981 to 2012. Results indicate that about 19% of Bay of Bengal TCs produces PRE. The PREs frequently occur when TCs are active in the northern Bay of Bengal with northeastward paths. The PREs are more likely to appear in the pre-monsoon and post-monsoon seasons, with peaks in May and October, respectively. The median lifetime of the PREs was approximately two days. The distances between PREs and parent TCs are ranged from 745 to 1849 km, with a median distance of 1118 km. Composite results of PREs suggested that deep moisture is advected from the TC vicinity to the remote CLLH region, which is situated beneath the equatorward entrance of the upper-level East Asia subtropical jet (EASJ). The lower-level southwesterly flow, which is associated with a strong pressure gradient between the western Pacific subtropical high and a trough in Bay of Bengal, steers TCs conveying abundant moisture from the Bay of Bengal to the CLLH region. The enhancement of the ascent and frontogenesis within the equatorward entrance region of the EASJ is favorable for occurrence of PREs in CLLH. TCs may also have a dynamical role in occurrence of PREs by intensifying the upper-level EASJ.

A meridional dipole in premonsoon Bay of Bengal tropical cyclone activity induced by ENSO

AbstractAnalysis of Bay of Bengal tropical cyclone (TC) track data for the months of May–June during 1979–2014 reveals a meridional dipole in TC intensification: TC intensification rates increased significantly in the northern region and decreased in the southern region. The dipole is consistent with changes in the large‐scale TC environment estimated using the Genesis Potential Index (GPI) for the same period. While an increase in lower troposphere cyclonic vorticity and midtroposphere humidity in the northern Bay of Bengal made the environment more favorable for TC intensification, enhanced vertical wind shear in the southern Bay of Bengal tended to reduce TC development. These environmental changes were associated with a strengthening of the monsoon circulation for the months of May–June, driven by a La Niña‐like shift in tropical Pacific SSTs and associated tropical wave dynamics. Finally, analysis of a suite of climate models from the Coupled Model Intercomparison Project Phase 5 archive shows that most models correctly reproduce the link between ENSO and premonsoon Bay of Bengal TC activity at interannual timescales, demonstrating the robustness of our main conclusions.

Impact of Satellite Radiance Data on Simulations of Bay of Bengal Tropical Cyclones Using the WRF-3DVAR Modeling System

This study attempts to evaluate whether assimilating radiance observations in the Weather Research and Forecasting (WRF) model could improve track, intensity, and precipitation forecasts of tropical cyclones (TCs) that occurred over the Bay of Bengal. The bias correction coefficients obtained from offline statistics, along with the quality control for radiances, were computed in the variational assimilation system. For this study, three numerical experiments named CNTL (no assimilation), GTS (with Global Telecommunication System observations), and RAD (radiance data along with GTS observations) were carried out with ten different model initial conditions for two TCs. The averaged root-mean-square errors of the analysis were relatively lower in the RAD experiments in comparison to the GTS experiments for all assimilation cycles of the meteorological variables. The mean initial position errors of TCs were reduced by 30%–47% in the RAD runs over the other runs. The results indicate that the assimilation of radiance data has a positive impact on the prediction of track, intensity, thermodynamic structures, and reflectivity associated with the storms. Improvements in mean landfall position errors were shown to range from 40% to 70% in the RAD experiments as compared to the CNTL and GTS simulations. This is because the RAD analyses are able to successfully reproduce the initial vortex and vertical structures as well as the prominent synoptic features associated with TC storms; therefore, the performance of the WRF modeling system is enhanced for simulations of track, structures, and intensity of TCs.

Improved Prediction of Bay of Bengal Tropical Cyclones through Assimilation of Doppler Weather Radar Observations

Abstract The impact on tropical cyclone (TC) prediction from assimilating Doppler weather radar (DWR) observations obtained from the TC inner core and environment over the Bay of Bengal (BoB) is studied. A set of three operationally relevant numerical experiments were conducted for 24 forecast cases involving 5 unique severe/very severe BoB cyclones: Sidr (2007), Aila (2009), Laila (2010), Jal (2010), and Thane (2011). The first experiment (CNTL) used the NCEP FNL analyses for model initial and boundary conditions. In the second experiment [Global Telecommunication System (GTS)], the GTS observations were assimilated into the model initial condition while the third experiment (DWR) used DWR with GTS observations. Assimilation of the TC environment from DWR improved track prediction by 32%–53% for the 12–72-h forecast over the CNTL run and by 5%–25% over GTS and was consistently skillful. More gains were seen in intensity, track, and structure by assimilating inner-core DWR observations as they provided more realistic initial organization/asymmetry and strength of the TC vortex. Additional experiments were conducted to assess the role of warm-rain and ice-phase microphysics to assimilate DWR reflectivity observations. Results indicate that the ice-phase microphysics has a dominant impact on inner-core reflectivity assimilation and in modifying the intensity evolution, hydrometeors, and warm core structure, leading to improved rainfall prediction. This study helps provide a baseline for the credibility of an observational network and assist with the transfer of research to operations over the India monsoon region.

Increase in the intensity of postmonsoon Bay of Bengal tropical cyclones

AbstractThe postmonsoon (October–November) tropical cyclone (TC) season in the Bay of Bengal (BoB) has spawned many of the deadliest storms in recorded history. Here it is shown that the intensity of major TCs (wind speed > 49 m s−1) in the postmonsoon BoB increased during 1981–2010. It is found that changes in environmental parameters are responsible for the observed increases in TC intensity. Increases in sea surface temperature and upper ocean heat content made the ocean more conducive to TC intensification, while enhanced convective instability made the atmosphere more favorable for the growth of TCs. The largest changes in the atmosphere and ocean occurred in the eastern BoB, where nearly all major TCs form. These changes are part of positive linear trends, suggesting that the intensity of postmonsoon BoB TCs may continue to increase in the future.

Tropical cyclone prediction over Bay of Bengal: a comparison of the performance of NCEP operational HWRF, NCAR ARW, and MM5 models

Much progress has been made in the area of tropical cyclone prediction using high-resolution mesoscale models based on community models developed at National Centers for Environmental Predication (NCEP) and National Center for Atmospheric Research (NCAR). While most of these model research and development activities are focused on predicting hurricanes in the Atlantic and Eastern Pacific domains, there has been much interest in using these models for tropical cyclone prediction in the North Indian Ocean region, particularly for Bay of Bengal storms that are known historically causing severe damage to life and property. In this study, the advanced operational hurricane modeling system developed at NCEP, known as the Hurricane Weather Research and Forecast (HWRF) model, is used to simulate two recent Bay of Bengal tropical cyclones—Nargis of November 2007 and Sidr of April 2008. The advanced NCEP operational vortex initialization procedure is adapted for simulating these Bay of Bengal tropical cyclones. Two additional regional models, the NCAR Advanced Research WRF and NCAR/Penn State University Mesoscale Model version 5 (MM5) are also used in simulating these storms. Results from these experiments highlight the superior performance of HWRF model over other models in predicting the Bay of Bengal cyclones. These results also suggest the need for a sophisticated vortex initialization procedure in conjunction with a model designed exclusively for tropical cyclone prediction for operational considerations.

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

The objective of this study is to examine the impact of assimilation of conventional and satellite data on the prediction of a severe cyclonic storm that formed in the Bay of Bengal during November 2008 with the four-dimensional data assimilation (FDDA) technique. The Weather Research and Forecasting (WRF ARW) model was used to study the structure, evolution, and intensification of the storm. Five sets of numerical simulations were performed using the WRF. In the first one, called Control run, the National Centers for Environmental Prediction (NCEP) Final Analysis (FNL) was used for the initial and boundary conditions. In the remaining experiments available observations were used to obtain an improved analysis and FDDA grid nudging was performed for a pre-forecast period of 24 h. The second simulation (FDDAALL) was performed with all the data of the Quick Scatterometer (QSCAT), Special Sensor Microwave Imager (SSM/I) winds, conventional surface, and upper air meteorological observations. QSCAT wind alone was used in the third simulation (FDDAQSCAT), the SSM/I wind alone in the fourth (FDDASSMI) and the conventional observations alone in the fifth (FDDAAWS). The FDDAALL with assimilation of all observations, produced sea level pressure pattern closely agreeing with the analysis. Examination of various parameters indicated that the Control run over predicted the intensity of the storm with large error in its track and landfall position. The assimilation experiment with QSCAT winds performed marginally better than the one with SSM/I winds due to better representation of surface wind vectors. The FDDAALL outperformed all the simulations for the intensity, movement, and rainfall associated with the storm. Results suggest that the combination of land-based surface, upper air observations along with satellite winds for assimilation produced better prediction than the assimilation with individual data sets.