Tropical cyclone activity in global warming scenario

Research efforts focused on assessing the potential for changes in tropical cyclone activity in the greenhouse-warmed climate have progressed since the IPCC assessment in 1996. Vulnerability to tropical cyclones becoming more pronounced due to the fastest population growth in tropical coastal regions makes it practically important to explore possible changes in tropical cyclone activity due to global warming. This paper investigates the tropical cyclone activity over whole globe and also individually over six different ocean basins. The parameters like storm frequency, storm duration, maximum intensity attained and location of formation of storm have been examined over the past 30-year period from 1977 to 2006. Of all, the north Atlantic Ocean shows a significant increasing trend in storm frequency and storm days, especially for intense cyclones. Lifetime of intense tropical cyclones over south Indian Ocean has been increased. The intense cyclonic activity over north Atlantic, south-west Pacific, north and south Indian Ocean has been increased in recent 15 years as compared to previous 15 years, whereas in the east and west-north Pacific it is decreased, instead weak cyclone activity has been increased there. Examination of maximum intensity shows that cyclones are becoming more and more intense over the south Indian Ocean with the highest rate. The study of the change in the cyclogenesis events in the recent 15 years shows more increase in the north Atlantic. The Arabian Sea experiences increase in the cyclogenesis in general, whereas Bay of Bengal witnesses decrease in these events. Shrinking of cyclogenesis region occurs in the east-north Pacific and south-west Pacific, whereas expansion occurs in west-north Pacific. The change in cyclogenesis events and their spatial distribution in association with the meteorological parameters like sea surface temperature (SST), vertical wind shear has been studied for Indian Ocean. The increase in SST and decrease in wind shear correspond to increase in the cyclogenesis events and vice versa for north Indian Ocean; however, for south Indian Ocean, it is not one to one.     

Experimental superensemble forecasts of tropical cyclones over the Bay of Bengal

This study entails the implementation of an experimental real time forecast capability for tropical cyclones over the Bay of Bengal basin of North Indian Ocean. This work is being built on the experience gained from a number of recent studies using the concept of superensemble developed at the Florida State University (FSU). Real time hurricane forecasts are one of the major components of superensemble modeling at FSU. The superensemble approach of training followed by real time forecasts produces the best forecasts for tracks and intensity (up to 5 days) of Atlantic hurricanes and Pacific typhoons. Improvements in track forecasts of about 25–35% compared to current operational forecast models has been noted over the Atlantic Ocean basin. The intensity forecasts for hurricanes are only marginally better than the best models. In this paper, we address tropical cyclone forecasts over the Bay of Bengal for the years 1996–2000. The main result from this study is that the position and intensity errors for tropical cyclone forecasts over the Bay of Bengal from the multimodel superensemble are generally less than those of all of the participating models during 1- to 3-day forecasts. Some of the major tropical cyclones, such as the November 1996 Andhra Pradesh cyclone and October 1999 Orissa super cyclone were well handled by this superensemble approach. A conclusion from this study is that the proposed approach may be a viable way to construct improved forecasts of Bay of Bengal tropical cyclone positions and intensity.     

Intensity of tropical cyclones during pre- and post-monsoon seasons in relation to accumulated tropical cyclone heat potential over Bay of Bengal

The aim of the present study is to understand the impact of oceanic heat potential in relation to the intensity of tropical cyclones (TC) in the Bay of Bengal during the pre-monsoon (April–May) and post-monsoon (October–November) cyclones for the period 2006–2010. To accomplish this, the two-layer gravity model (TLGM) is employed to estimate daily tropical cyclone heat potential (TCHP) utilizing satellite altimeter data, satellite sea surface temperature (SST), and a high-resolution comprehensive ocean atlas developed for Indian Ocean, subsequently validated with in situ ARGO profiles. Accumulated TCHP (ATCHP) is estimated from genesis to the maximum intensity of cyclone in terms of minimum central pressure along their track of all the cyclones for the study period using TLGM generated TCHP and six-hourly National Centre for Environmental Prediction Climate Forecast System Reanalysis data. Similarly, accumulated sea surface heat content (ASSHC) is estimated using satellite SST. In this study, the relationship between ATCHP and ASSHC with the central pressure (CP) which is a function of TC intensity is developed. Results reveal a distinct relationship between ATCHP and CP during both the seasons. Interestingly, it is seen that requirement of higher ATCHP during pre-monsoon cyclones is required to attain higher intensity compared to post-monsoon cyclones. It is mainly attributed to the presence of thick barrier layer (BL) resulting in higher enthalpy fluxes during post-monsoon period, where as such BL is non-existent during pre-monsoon period.     

Climatology of landfalling tropical cyclones in Bangladesh 1877–2003

Bangladesh is highly susceptible to tropical cyclones. Unfortunately, there is a dearth of climatological studies on the tropical cyclones of Bangladesh. The Global Tropical Cyclone Climatic Atlas (GTCCA) lists historical storm track information for all the seven tropical cyclone ocean basins including the North Indian Ocean. Using GIS, tropical cyclones that made landfall in Bangladesh during 1877–2003 are identified and examined from the climatological perspective. For the convenience of study, the coast of Bangladesh is divided into five segments and comparisons are made among the coastal segments in terms of cyclone landfall and vulnerability. There is a large variability in the year-to-year occurrence of landfalling tropical cyclones in Bangladesh. Most of the tropical cyclones (70%) hit in the months of May–June and October–November generally show the well-known pattern of pre- and post-monsoon cyclone seasons in that region.     

Customization of WRF-ARW model with physical parameterization schemes for the simulation of tropical cyclones over North Indian Ocean

The convection and planetary boundary layer (PBL) processes play significant role in the genesis and intensification of tropical cyclones (TCs). Several convection and PBL parameterization schemes incorporate these processes in the numerical weather prediction models. Therefore, a systematic intercomparison of performance of parameterization schemes is essential to customize a model. In this context, six combinations of physical parameterization schemes (2 PBL Schemes, YSU and MYJ, and 3 convection schemes, KF, BM, and GD) of WRF-ARW model are employed to obtain the optimum combination for the prediction of TCs over North Indian Ocean. Five cyclones are studied for sensitivity experiments and the out-coming combination is tested on real-time prediction of TCs during 2008. The tracks are also compared with those provided by the operational centers like NCEP, ECMWF, UKMO, NCMRWF, and IMD. It is found that the combination of YSU PBL scheme with KF convection scheme (YKF) provides a better prediction of intensity, track, and rainfall consistently. The average RMSE of intensity (13?hPa in CSLP and 11?m?s^?1 in 10-m wind), mean track, and landfall errors is found to be least with YKF combination. The equitable threat score (ETS) of YKF combination is more than 0.2 for the prediction of 24-h accumulated rainfall up to 125?mm. The vertical structural characteristics of cyclone inner core also recommend the YKF combination for Indian seas cyclones. In the real-time prediction of 2008 TCs, the 72-, 48-, and 24-h mean track errors are 172, 129, and 155?km and the mean landfall errors are 125, 73, and 66?km, respectively. Compared with the track of leading operational agencies, the WRF model is competing in 24?h (116?km error) and 72?h (166?km) but superior in 48-h (119?km) track forecast.     

Impact of sea surface temperature in modulating movement and intensity of tropical cyclones

It is well recognized that sea surface temperature (SST) plays a dominant role in the formation and intensification of tropical cyclones. A number of observational/empirical studies were conducted at different basins to investigate the influence of SST on the intensification of tropical cyclones and in turn, modification in SST by the cyclone itself. Although a few modeling studies confirmed the sensitivity of model simulation/forecast to SST, it is not well quantified, particularly for Bay of Bengal cyclones. The present study is designed to quantify the sensitivity of SST on mesoscale simulation of an explosively deepening storm over the Bay of Bengal, i.e., Orissa super cyclone (1999). Three numerical experiments are conducted with climatological SST, NCEP (National Center for Environmental Prediction) skin temperature as SST, and observed SST (satellite derived) toward 5-day simulation of the storm using mesoscale model MM5. At model initial state, NCEP skin temperature and observed SST over the Bay of Bengal are 1–2°C warmer than climatological SST, but cooler by nearly 1°C along the coastline. Observed SST shows a number of warm patches in the Bay of Bengal compared with NCEP skin temperature. The simulation results indicate that the sea surface temperature has a significant impact on model-simulated track and intensity of the cyclonic storm. The track and intensity of the storm is better simulated with the use of satellite-observed SST.     

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.     

Sensitivity of inland decay of North Atlantic tropical cyclones to soil parameters

Using the HURDAT best track analysis of track and intensity of tropical cyclones that made landfall over the continental United States during the satellite era (1980?C2005), we analyze the role of land surface variables on the cyclone decay process. The land surface variables considered in the present study included soil parameters (soil heat capacity and its surrogate soil bulk density), roughness, topography and local gradients of topography. The sensitivity analysis was carried out using a data-adaptive genetic algorithm approach that automatically selects the most suitable variables by fitting optimum empirical functions that estimates cyclone intensity decay in terms of given observed variables. Analysis indicates that soil bulk density (soil heat capacity) has a dominant influence on cyclone decay process. The decayed inland cyclone intensities were found to be positively correlated with the cube of the soil bulk density (heat capacity). The impact of the changes in soil bulk density (heat capacity) on the decayed cyclone intensity is higher for higher intensity cyclones. Since soil bulk density is closely related to the soil heat capacity and inversely proportional to the thermal diffusivity, the observed relationship can also be viewed as the influence of cooling rate of the land surface, as well as the transfer of heat and moisture underneath a land-falling storm. The optimized prediction function obtained by statistical model processes in the present study that predicts inland intensity changes during 6-h interval showed high fitness index and small errors. The performance of the prediction function was tested on inland tracks of eighteen hurricanes and tropical storms that made landfall over the United States between 2001 and 2010. The mean error of intensity prediction for these cyclones varied from 1.3 to 15.8 knots (0.67?C8.12?m?s^?1). Results from the data-driven analysis thus indicate that soil heat flux feedback should be an important consideration for the inland decay of tropical cyclones. Experiments were also undertaken using Weather Research Forecasting (WRF) Advanced Research Version (ARW ver 3.3) to assess the sensitivity of the soil parameters (roughness, heat capacity and bulk density) on the post-landfall structure of select storms. The model was run with 1-km grid spacing, limited area single domain with boundary conditions from the North American Regional Reanalysis. Of different experiments, only the surface roughness change and soil bulk density (heat capacity) change experiments showed some sensitivity to the intensity change. The WRF results thus have a low sensitivity to the land parameters (with only the roughness length showing some impact). This calls for reassessing the land surface response on post-landfall characteristics with more detailed land surface representation within the mesoscale and hurricane modeling systems.     

Multi-Physics ensemble prediction of tropical cyclone movement over Bay of Bengal

Ensemble prediction methodology based on variations in physical process parameterizations in tropical cyclone track prediction has been assessed. Advanced Research Weather Research and Forecasting model with 30-km resolution was used to make 5-day simulation of the movement of Orissa super cyclone (1999), one of the most intense tropical cyclones over the North Indian Ocean. Altogether 36 ensemble members with all possible combinations of three cumulus convection, two planetary boundary layer and six cloud microphysics parameterization schemes were produced. A comparison of individual members indicated that Kain–Fritsch cumulus convection scheme, Mellor–Yamada–Janjic planetary boundary layer scheme and Purdue Lin cloud microphysics scheme showed better performance. The best possible ensemble formulation is identified based on SPREAD and root mean square error (RMSE). While the individual members had track errors ranging from 96–240 km at 24 h to 50–803 km at 120 h, most of the ensemble predictions show significant betterment with mean errors less than 130 km up to 120 h. The convection ensembles had large spread of the cluster, and boundary layer ensembles had significant error disparity, indicating their important roles in the movement of tropical cyclones. Six-member ensemble predictions with cloud microphysics schemes of LIN, WSM5, and WSM6 produce the best predictions with least of RMSE, and large SPREAD indicates the need for inclusion of all possible hydrometeors in the simulation and that six-member ensemble is sufficient to produce the best ensemble prediction of tropical cyclone tracks over Bay of Bengal.     

Prediction of severe tropical cyclones over the Bay of Bengal during 2007–2010 using high-resolution mesoscale model

In this paper, the performance of a high-resolution mesoscale model for the prediction of severe tropical cyclones over the Bay of Bengal during 2007?C2010 (Sidr, Nargis, Aila, and Laila) is discussed. The advanced Weather Research Forecast (WRF) modeling system (ARW core) is used with a combination of Yonsei University PBL schemes, Kain-Fritsch cumulus parameterization, and Ferrier cloud microphysics schemes for the simulations. The initial and boundary conditions for the simulations are derived from global operational analysis and forecast products of the National Center for Environmental Prediction-Global Forecast System (NCEP-GFS) available at 1°lon/lat resolution. The simulation results of the extreme weather parameters such as heavy rainfall, strong wind and track of those four severe cyclones, are critically evaluated and discussed by comparing with the Joint Typhoon Warning Center (JTWC) estimated values. The simulations of the cyclones reveal that the cyclone track, intensity, and time of landfall are reasonably well simulated by the model. The mean track error at the time of landfall of the cyclone is 98?km, in which the minimum error was found to be for the cyclone Nargis (22?km) and maximum error for the cyclone Laila (304?km). The landfall time of all the cyclones is also fairly simulated by the model. The distribution and intensity of rainfall are well simulated by the model as well and were comparable with the TRMM estimates.     

Performance evaluation of HWRF model during post monsoon tropical cyclone Nilofar

An accurate tropical cyclone track and intensity forecast is very important for disaster management. Specialized numerical prediction models have been recently used to provide high-resolution temporal and special forecasts. Hurricane Weather Research and Forecast (HWRF) model is one of the emerging numerical models for tropical cyclone forecasting. This study evaluates the performance of HWRF model during the post monsoon tropical cyclone Nilofar on the north Indian Ocean basin. The evaluation uses the best track data provided by the Indian Meteorological Department (IMD) and the Joint Typhoon Warning Centre (JTWC). Cyclone track, central pressure, and wind speed are covered on this evaluation. Generally, HWRF was able to predict the Nilofar track with track error less than 230 km within the first 66 h of forecast time span. HWRF predicted more intense tropical cyclone. It predicted the lowest central pressure to be 922 hPa while it reached 950 hPa according to IMD and 937 hPa according to JTWC. Wind forecast was better as it predicted maximum wind speed of 122 kt while it reached 110 and 115 kt according to IMD and JTWC, respectively.     

Storm surge modelling for the Bay of Bengal and Arabian Sea

Most of the countries around the North Indian Ocean are threatened by storm surges associated with severe tropical cyclones. The destruction due to the storm surge flooding is a serious concern along the coastal regions of India, Bangladesh, Myanmar, Pakistan, Sri Lanka, and Oman. Storm surges cause heavy loss of lives and property damage to the coastal structures and losses of agriculture which lead to annual economic losses in these countries. About 300,000 lives were lost in one of the most severe cyclones that hit Bangladesh (then East Pakistan) in November 1970. The Andhra Cyclone devastated part of the eastern coast of India, killing about 10,000 persons in November 1977. More recently, the Chittagong cyclone of April 1991 killed 140,000 people in Bangladesh, and the Orissa coast of India was struck by a severe cyclonic storm in October 1999, killing more than 15,000 people besides enormous loss to the property in the region. These and most of the world’s greatest natural disasters associated with the tropical cyclones have been directly attributed to storm surges. The main objective of this article is to highlight the recent developments in storm surge prediction in the Bay of Bengal and the Arabian Sea.     

Tropical cyclones over north Indian Ocean during El-Niño Modoki years

Tropical cyclones are the most hazardous weather systems, which form over warm ocean waters. The frequencies of tropical cyclones show variabilities over all the oceanic basins, during the El-Niño and El-Niño Modoki years. Recent studies have shown significant impact of air–sea interaction processes like El-Niño and El-Niño Modoki on the cyclone activity over different ocean basins. The results suggest in most cases, El-Niño events suppress the formation of cyclones over various basins. A recent study indicated that concurrent occurrence of El-Niño Modoki and positive Indian Ocean dipole (IOD) events can generate more cyclones over north-west Pacific. We propose to study the impact of El-Niño Modoki events on the formation of tropical cyclones over north Indian Ocean (NIO). Our present study suggests that the cyclogenesis over the NIO is a complex phenomenon, as it is influenced by several coupled ocean atmospheric phenomena such as El-Niño, El-Niño Modki, IOD and Madden–Julian oscillation.     

Radiative transfer simulations for the MADRAS imager of Megha-Tropiques

This paper reports the radiative transfer simulations for the passive microwave radiometer onboard the proposed Indian climate research satellite Megha-Tropiques due to be launched in 2011. These simulations have been performed by employing an in-house polarized radiative transfer code for raining systems ranging from depression and tropical cyclones to the Indian monsoon. For the sake of validation and completeness, simulations have also been done for the Tropical Rainfall Measuring Mission (TRMM)’s Microwave Imager (TMI) of the highly successful TRMM mission of NASA and JAXA. The paper is essentially divided into two parts: (a) Radiometer response with specific focus on high frequency channels in both the radiometers is discussed in detail with a parametric study of the effect of four hydrometeors (cloud liquid water, cloud ice, precipitating water and precipitating ice) on the brightness temperatures. The results are compared with TMI measurements wherever possible. (b) Development of a neural network-based fast radiative transfer model is elucidated here. The goal is to speed up the computational time involved in the simulation of brightness temperatures, necessitated by the need for quick and online retrieval strategies. The neural network model uses hydrometeor profiles as inputs and simulates spectral microwave brightness temperature at multiple frequencies as output. A huge database is generated by executing the in-house radiative transfer code for seven different cyclones occurred in North Indian Ocean region during the period 2001–2006. A part of the dataset is used to train the network while the remainder is used for testing purposes. For the purpose of testing, a typical scene from the southwest monsoon rain is also considered. The results obtained are very encouraging and show that the neural network is able to mimic the underlying physics of the radiative transfer simulations with a correlation coefficient of over 99%.     

Swarm intelligence and neural nets in forecasting the maximum sustained wind speed along the track of tropical cyclones over Bay of Bengal

Tropical cyclones are well-known extreme weather and the cause of considerable damages, injuries and loss of life. The assessment of the maximum sustained wind speed along the track of the tropical cyclones is very important for estimating the strength of the cyclones. The swarm intelligence in the form of ant colony optimization (ACO) technique is introduced in this study to compute the pheromone deposition along the track of tropical cyclones followed by neural nets to forecast the maximum sustained wind speed of the cyclones occurring over the Bay of Bengal of North Indian Ocean. The ACO is a nonlinear problem-based meta-heuristic optimization method for finding approximate solutions to discrete optimization problems and simulates the decision-making processes of ant colony similar to other adaptive learning techniques. The method has shown its application potential in various fields including the prediction of monsoon rainfall. In this study, the amount of pheromone deposition during the successive stages of the cyclones has been estimated. A range of minimum central pressure (MCP), central pressure drop (PD), maximum sustained wind speed (MSWS) and intensity (T-No) associated with the cyclones of Bay of Bengal are utilized to form the input matrix of the neural nets. The neural nets are trained to forecast the maximum sustained wind speed along the track of the tropical cyclones over Bay of Bengal. The result reveals that the errors in forecasting the MSWS along the track of tropical cyclones with 6, 12, 18 and 24 h lead time are 2.6, 2.9, 3.1 and 4.8, respectively. The result is compared with the existing dynamical, statistical and adaptive models to evaluate the skill of the present model. The result is well validated with observation.     

Sensitivity of simulated cyclone Gonu intensity and track to variety of parameterizations: Advanced hurricane WRF model application

Domain configuration and several physical parameterization settings such as planetary boundary layer, cumulus convection, and ocean–atmosphere surface flux parameterizations can play significant roles in numerical prediction of tropical cyclones. The present study focuses to improve the prediction of the TC Gonu by investigating the sensitivity of simulations to mentioned configurations with the Advanced Hurricane WRF model. The experiments for domain design sensitivity with 27 km resolution has been shown moving the domains towards the east improve the results, due to better account for the large-scale process. The fixed and movable nests on a 9-km grid were considered separately within the coarse domain and their results showed that despite salient improvement in simulated intensity, an accuracy reduction in simulated track was observed. Increasing horizontal resolution to 3 km incredibly reduced the simulated intensity accuracy when compared to 27 km resolution. Thereafter, different initial conditions were experimented and the results have shown that the cyclone of 1000 hPa sea level pressure is the best simulation initial condition in predicting the track and intensity for cyclone Gonu. The sensitivity of simulations to ocean–atmosphere surface-flux parameterizations on a 9-km grid showed the combination of ‘Donelan scheme’ for momentum exchanges along with ‘Large and Pond scheme’ for heat and moisture exchanges provide the best prediction for cyclone Gonu intensity. The combination of YSU and MYJ PBL scheme with KF convection for prediction of track and the combination of YSU PBL scheme with KF convection for prediction of intensity are found to have better performance than the other combinations. These 22 sensitivity experiments also implicitly lead us to the conclusion that each particular forecast aspect of TC (e.g., track, intensity, etc.) will require its own special design.     

Impacts of ENSO and IOD on tropical cyclone activity in the Bay of Bengal

Natural Hazards - The impacts of El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) on tropical cyclone (TC) activity (intensity, frequency, genesis location, track and average...     

Identification of clusters in tropical cyclone tracks of North Indian Ocean

Tropical cyclones are a key climate-related hazard in South Asia. Assessment of the risk of cyclone impacts requires a comprehensive characterization of historical cyclone climatology. This study analyzes the tracks of tropical cyclones in the North Indian Ocean. Based on their spatial characteristics, cyclone tracks appear to be grouped into five well-defined clusters. These clusters correspond to distinct regions of cyclonic activity and exhibit differences in characteristics such as genesis location, probability of landfall, duration, and maximum intensity. Some of the identified clusters appear particularly important with regard to impacts because events in these clusters have greater landfall probability and are more intense. The clustering approach is likely to provide useful insights for the characterization of cyclone risk.     

Developing a new sinuosity index for cyclone tracks in the tropical South Pacific

This paper introduces a new metric for tropical cyclone track shape within the tropical South Pacific (TSP) basin, based on measurements of track sinuosity. A sinuosity index (SI) is developed by applying a simple cube-root transformation to original track sinuosity values. Based on the resulting near-normal SI distribution, an ordinal four-category (quartile) naming system is then proposed for track-type classification. Track sinuosity patterns are also investigated over the last four decades (1970–2008). Analytical findings suggest that cyclone track sinuosity is an important parameter influencing the potential vulnerability of island archipelagoes to cyclone hazard. Principally, sinuously moving cyclones show some tendency for greater longevity and intensity than straighter-tracking storms and make up a larger proportion of systems forming in the western tropical South Pacific than those generated farther east. Although no long-term statistical trend can be established, track sinuosity is highly variable through time, implying that the TSP basin and the islands therein will continue to experience large but irregular inter-annual fluctuations in cyclone track morphology.     

Forecasting of cyclone Viyaru and Phailin by NWP-based cyclone prediction system (CPS) of IMD – an evaluation

An objective NWP-based cyclone prediction system (CPS) was implemented for the operational cyclone forecasting work over the Indian seas. The method comprises of five forecast components, namely (a) Cyclone Genesis Potential Parameter (GPP), (b) Multi-Model Ensemble (MME) technique for cyclone track prediction, (c) cyclone intensity prediction, (d) rapid intensification, and (e) predicting decaying intensity after the landfall. GPP is derived based on dynamical and thermodynamical parameters from the model output of IMD operational Global Forecast System. The MME technique for the cyclone track prediction is based on multiple linear regression technique. The predictor selected for the MME are forecast latitude and longitude positions of cyclone at 12-hr intervals up to 120 hours forecasts from five NWP models namely, IMD-GFS, IMD-WRF, NCEP-GFS, UKMO, and JMA. A statistical cyclone intensity prediction (SCIP) model for predicting 12 hourly cyclone intensity (up to 72 hours) is developed applying multiple linear regression technique. Various dynamical and thermodynamical parameters as predictors are derived from the model outputs of IMD operational Global Forecast System and these parameters are also used for the prediction of rapid intensification. For forecast of inland wind after the landfall of a cyclone, an empirical technique is developed. This paper briefly describes the forecast system CPS and evaluates the performance skill for two recent cyclones Viyaru (non-intensifying) and Phailin (rapid intensifying), converse in nature in terms of track and intensity formed over Bay of Bengal in 2013. The evaluation of performance shows that the GPP analysis at early stages of development of a low pressure system indicated the potential of the system for further intensification. The 12-hourly track forecast by MME, intensity forecast by SCIP model and rapid intensification forecasts are found to be consistent and very useful to the operational forecasters. The error statistics of the decay model shows that the model was able to predict the decaying intensity after landfall with reasonable accuracy. The performance statistics demonstrates the potential of the system for improving operational cyclone forecast service over the Indian seas.