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The rainfall forecast of global forecasting system (GFS) model running at the India Meteorological Department (IMD), i.e., GFS T574 (25 km) and GFS T1534 (12.5 km) is verified at its natural resolution. A verification study is conducted for the rainfall forecast over Indian window during the summer monsoon season (June–September) 2016. Regional verification of rainfall forecast was also carried out over five homogeneous regions of India, i.e., north India, west coast of India, northeast India, central India and peninsular India. The skill of the model forecast is also evaluated for yes/no, categorical and few heavy rainfall cases over India. Results show that both T1534 and T574 models are able to provide more realistic spatial distribution of seasonal mean rainfall over India. This study also shows that both the model forecasts are skillful in capturing the 24 h accumulated rainfall over climatologically heavy rainfall regions, but the magnitude and location fluctuated. The rainfall forecast for different rainfall categories in terms of different statistical skill scores, i.e., ratio score, HK, bias, hit rate and HKQ showed a relatively high skill for T1534 compared to T574 over most parts of the country from the day 1 to day 5 forecasts. It is also found from this study that the skill in predicting day 1 to day 3 forecasts of both the models was found to be good and usable for all parts of the country except for high terrain regions in India. The results documented here are expected to be useful to the operational forecasters in day-to-day weather forecasting over Indian monsoon regions.

During August 2018 and 2019 the southern state of India, Kerala received unprecedented heavy rainfall which led to widespread flooding. We aim to characterize the convective nature of these events and the large-scale atmospheric forcing, while exploring their predictability by three state of the art global prediction systems, the National Centre for Environmental Prediction (NCEP) based India Meteorological Department (IMD) operational Global Forecast System (GFS), the European Centre for Medium Range Weather Forecast (ECMWF) integrated forecast system (IFS) and the Unified Model based NCUM being run at the National Centre for Medium Range Weather Forecasting (NCMRWF).Satellite, radar and lightning observations suggest that these rain events were dominated by cumulus congestus and shallow convection with strong zonal flow leading to orographically enhanced rainfall over the Ghats mountain range, sporadic deep convection was also present during the 2019 event. A moisture budget analyses using the ERA5 (ECMWF Reanalyses version 5) reanalyses and forecast output revealed significantly increased moisture convergence below 800 hPa during the main rain events compared to August climatology. The total column integrated precipitable water tendency, however is found to be small throughout the month of August, indicating a balance between moisture convergence and drying by precipitation. By applying a Rossby wave filter to the rainfall anomalies it is shown that the large-scale moisture convergence is associated with westward propagating barotropic Rossby waves over Kerala, leading to increased predictability of these events, especially for 2019.Evaluation of the deterministic and ensemble rainfall predictions revealed systematic rainfall differences over the Ghats mountains and the coastline. The ensemble predictions were more skilful than the deterministic forecasts, as they were able to predict rainfall anomalies (>3 standard deviations from climatology) beyond day 5 for August 2019 and up to day 3 for 2018.

A global forecast system model at a horizontal resolution of T1534 ( $${\sim }12.5\, \hbox {km}$$ ) has been evaluated for the monsoon seasons of 2016 and 2017 over the Indian region. It is for the first time that such a high-resolution global model is being run operationally for monsoon weather forecast. A detailed validation of the model therefore is essential. The validation of mean monsoon rainfall for the season and individual months indicates a tendency for wet bias over the land region in all the forecast lead time. The probability distribution of forecast rainfall shows an overestimation (underestimation) of rainfall for the lighter (heavy) categories. However, the probability distribution functions of moderate rainfall categories are found to be reasonable. The model shows fidelity in capturing the extremely heavy rainfall categories with shorter lead times. The model reasonably predicts the large-scale parameters associated with the Indian summer monsoon, particularly, the vertical profile of the moisture. The diurnal rainfall variability forecasts in all lead times show certain biases over different land and oceanic regions and, particularly, over the north–west Indian region. Although the model has a reasonable fidelity in capturing the spatio-temporal variability of the monsoon rain, further development is needed to enhance the skill of forecast of a higher rain rate with a longer lead time.

In the present study, a model output statistics (MOS) guidance model was developed by using the neural network technique for a bias-corrected rainfall forecast. The model was developed over the Indian window (0– $$40{^{\circ }}\hbox {N}$$ and 60– $$100{^{\circ }}\hbox {E}$$ ) by using the observed and global forecast system (GFS) T-1534 model output (up to 5 days) at a $$0.125{^{\circ }} \times \,0.125{^{\circ }}$$ regular grid during the summer monsoon (June–September) 2016. The skill of the developed MOS model forecast against the observed $$0.125{^{\circ }} \times 0.125{^{\circ }}$$ grid rainfall data is obtained for the summer monsoon (June–September) 2017. The skill of the MOS model rainfall forecast is found to show good improvement over the T-1534 model’s direct forecast over the Indian window. In general, the T-1534 model’s direct forecast shows high skill but the forecast obtained by using the MOS model shows better skill than the direct model’s forecast, although a major improvement is seen for the Day 1 forecast at the national level. So the skill of the bias-corrected rainfall forecast by using the MOS guidance and the T-1534 model output is high and has the potential of being used as an operational forecast over the Indian region.

The forecast for 655 districts and 6500 blocks had been prepared and implemented on 1st June, 2014. The procedure for getting forecast for the districts and blocks in India including altitude corrections is based upon regular (0.25 × 0.25) grid output from the T-574 Model and output from 9 km WRF model. A verification study for rainfall forecast at 0.25 × 0.25 degree grid for Indian Window (0-40° N and 60-100° N) is also conducted, which had indicated that skill of the rainfall forecast is good for all parts of the country except oceanic islands and high terrain regions and one can down scale to any level, down to the blocks, the skill scores will not differ much. A detailed verification study for the skill of the forecast at block level for all the eight weather parameters for which the forecast was issued is conducted. The skill of the rainfall forecast is obtained for categorical forecast and as well as for yes/no forecast. The skill scores for rainfall had indicated that highest value of Hanssen and Kuiper (HK) score is 0.44, Hanssen and Kuiper score for quantitative rainfall (HKQ) is 0.18, Ratio score for yes/no forecast is 90 percent and Hit rate (HR) is 0.83. The detailed verification study for the block level weather forecast for monsoon 2014 is presented in the paper and the skill found is good. The study indicates that model forecast has the potential to be used for the block level forecast after making the quick value additions for which hints are given in the conclusion part.

A new approach to ensemble forecasting of rainfall over India based on daily outputs of four operational numerical weather prediction (NWP) models in the medium-range timescale (up to 5 days) is proposed in this study. Four global models, namely ECMWF, JMA, GFS and UKMO available on real-time basis at India Meteorological Department, New Delhi, are used simultaneously with adequate weights to obtain a multi-model ensemble (MME) technique. In this technique, weights for each NWP model at each grid point are assigned on the basis of unbiased mean absolute error between the bias-corrected forecast and observed rainfall time series of 366 daily data of 3 consecutive southwest monsoon periods (JJAS) of 2008, 2009 and 2010. Apart from MME, a simple ensemble mean (ENSM) forecast is also generated and experimented. The prediction skill of MME is examined against observed and corresponding outputs of each constituent model during monsoon 2011. The inter-comparison reveals that MME is able to provide more realistic forecast of rainfall over Indian monsoon region by taking the strength of each constituent model. It has been further found that the weighted MME technique has higher skill in predicting daily rainfall compared to ENSM and individual member models. RMSE is found to be lowest in MME forecasts both in magnitude and area coverage. This indicates that fluctuations of day-to-day errors are relatively less in the MME forecast. The inter-comparison of domain-averaged skill scores for different rainfall thresholds further clearly demonstrates that the MME algorithm improves slightly above the ENSM and member models.

The output from Global Forecasting System (GFS) T574L64 operational at India Meteorological Department (IMD), New Delhi is used for obtaining location specific quantitative forecast of maximum and minimum temperatures over India in the medium range time scale. In this study, a statistical bias correction algorithm has been introduced to reduce the systematic bias in the 24–120 hour GFS model location specific forecast of maximum and minimum temperatures for 98 selected synoptic stations, representing different geographical regions of India. The statistical bias correction algorithm used for minimizing the bias of the next forecast is Decaying Weighted Mean (DWM), as it is suitable for small samples. The main objective of this study is to evaluate the skill of Direct Model Output (DMO) and Bias Corrected (BC) GFS for location specific forecast of maximum and minimum temperatures over India. The performance skill of 24–120 hour DMO and BC forecast of GFS model is evaluated for all the 98 synoptic stations during summer (May-August 2012) and winter (November 2012–February 2013) seasons using different statistical evaluation skill measures. The magnitude of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for BC GFS forecast is lower than DMO during both summer and winter seasons. The BC GFS forecasts have higher skill score as compared to GFS DMO over most of the stations in all day-1 to day-5 forecasts during both summer and winter seasons. It is concluded from the study that the skill of GFS statistical BC forecast improves over the GFS DMO remarkably and hence can be used as an operational weather forecasting system for location specific forecast over India.

Statistical bias correction methods for numerical weather prediction (NWP) forecasts of maximum and minimum temperatures over India in the medium-range time scale (up to 5 days) are proposed in this study. The objective of bias correction is to minimize the systematic error of the next forecast using bias from past errors. The need for bias corrections arises from the many sources of systematic errors in NWP modeling systems. NWP models have shortcomings in the physical parameterization of weather events and have the inability to handle sub-grid phenomena successfully. The statistical algorithms used for minimizing the bias of the next forecast are running-mean (RM) bias correction, best easy systematic estimator, simple linear regression and the nearest neighborhood (NN) weighted mean, as they are suitable for small samples. Bias correction is done for four global NWP model maximum and minimum temperature forecasts. The magnitude of the bias at a grid point depends upon geographical location and season. Validation of the bias correction methodology is carried out using daily observed and bias-corrected model maximum and minimum temperature forecast over India during July–September 2011. The bias-corrected NWP model forecast generally outperforms direct model output (DMO). The spatial distribution of mean absolute error and root-mean squared error for bias-corrected forecast over India indicate that both the RM and NN methods produce the best skill among other bias correction methods. The inter-comparison reveals that statistical bias correction methods improve the DMO forecast in terms of accuracy in forecast and have the potential for operational applications.

Performance of national centers for environmental prediction based global forecast system (GFS) T574/L64 and GFS T382/L64 over Indian region has been evaluated for the summer monsoon season of 2011. The real-time model outputs are generated daily at India Meteorological Department, New Delhi for the forecasts up to 7 days. Verification of rainfall forecasts has been carried out against observed rainfall analysis. Performance of the model is also examined in terms of lower tropospheric wind circulation, vertical structure of specific humidity and precipitable water content. Case study of a monsoon depression is also illustrated. Results obtained show that, in general, both the GFS T382 and T574 forecasts are skillful to capture climatologically heavy rainfall regions. However, the accuracy in prediction of location and magnitude of rainfall fluctuates considerably. The verification results, at the spatial scale of 50 km resolution, in a regional spatial scale and country as a whole, in terms of continuous skill score, time series and categorical statistics, have demonstrated superiority of GFS T574 against T382 over Indian region. Both the model shows bias of lower tropospheric drying and upper tropospheric moistening. A bias of anti-cyclonic circulation in the lower tropospheric level lay over the central India, where rainfall as well as precipitable water content shows negative bias. Considerable differences between GFS T574 and T382 are noticed in the structure of model bias in terms of lower tropospheric wind circulation, vertical structure of specific humidity and precipitable water contents. The magnitude of error for these parameters increases with forecast lead time in both GFS T574 and T382. The results documented are expected to be useful to the forecasters, monsoon researchers and modeling community.

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India Meteorological Department has implemented district level medium range rainfall forecast system applying multimodel ensemble technique, making use of model outputs of state-of-the-art global models from the five leading global NWP centres. The pre-assigned grid point weights on the basis of anomaly correlation coefficients (CC) between the observed values and forecast values are determined for each constituent model at the resolution of 0.25 ◦ ×0.25 ◦ utilizing two season datasets (1 June–30 September, 2007 and 2008) and the multimodel ensemble forecasts (day-1 to day-5 forecasts) are generated at the same resolution on a real-time basis. The ensemble forecast fields are then used to prepare forecasts for each district, taking the average value of all grid points falling in a particular district. In this paper, we describe the development strategy of the technique and performance skill of the system during summer monsoon 2009. The study demonstrates the potential of the system for improving rainfall forecasts at five days time scale over Indian region. Districtwise performance of the ensemble rainfall forecast reveals that the technique, in general, is capable of providing reasonably good forecast skill over most states of the country, particularly over the states where the monsoon systems are more dominant.

The prediction of Asian summer monsoon rainfall on various space and time scales is still a difficult task. Compared to the mid-latitudes, proportional improvement in the skill in prediction of monsoon rainfall in medium range has been less in recent years. Global models and data assimilation techniques are being further improved for monsoons and the tropics. However, multi-model ensemble (MME) forecasting is gaining popularity, as it has the potential to provide more information for practical forecasting in terms of making a consensus forecast and reducing the model uncertainties. As major centres are exchanging the model output in near real time, MME is a viable, inexpensive, way for enhancing the forecast skill. Apart from a simple ensemble mean, the MME predictions of large-scale monsoon precipitation in the medium range was carried out during the 2009 monsoon at NCMRWF/MoES, India. The neural network weights were obtained and a neural network was trained based upon forecast data from four global models for the 2007 and 2008 monsoons in order to develop the multi-model ensemble system. The skill score for country and sub-regions, indicates that a multi-model ensemble forecast has a higher skill than individual model forecasts and also higher skill than the simple ensemble mean in general. Although the skill of the global models falls beyond day 3, a significant improvement could be seen by employing the MME technique up to day 5. Copyright © 2011 Royal Meteorological Society

Realistic simulation/prediction of the Asian summer monsoon rainfall on various space–time scales is a challenging scientific task. Compared to mid-latitudes, a proportional skill improvement in the prediction of monsoon rainfall in the medium range has not happened in recent years. Global models and data assimilation techniques are being improved for monsoon/tropics. However, multi-model ensemble (MME) forecasting is gaining popularity, as it has the potential to provide more information for practical forecasting in terms of making a consensus forecast and handling model uncertainties. As major centers are exchanging model output in near real-time, MME is a viable inexpensive way of enhancing the forecasting skill and information content. During monsoon 2008, on an experimental basis, an MME forecasting of large-scale monsoon precipitation in the medium range was carried out in real-time at National Centre for Medium Range Weather Forecasting (NCMRWF), India. Simple ensemble mean (EMN) giving equal weight to member models, bias-corrected ensemble mean (BCEMn) and MME forecast, where different weights are given to member models, are the products of the algorithm tested here. In general, the aforementioned products from the multi-model ensemble forecast system have a higher skill than individual model forecasts. The skill score for the Indian domain and other sub-regions indicates that the BCEMn produces the best result, compared to EMN and MME. Giving weights to different models to obtain an MME product helps to improve individual member models only marginally. It is noted that for higher rainfall values, the skill of the global model rainfall forecast decreases rapidly beyond day-3, and hence for day-4 and day-5, the MME products could not bring much improvement over member models. However, up to day-3, the MME products were always better than individual member models.

The study provides a concise and synthesized documentation of the current level of skill of the satellite (3B42RT, 3B42V-6, KALPANA-1) products over Indian regions based on the data gathered during the summer monsoon seasons of 2006, 2007 and 2008. The inter-comparison of satellite products with the rain gauge observations suggests that the TRMM 3B42V6 product could distinctly capture characteristic features of the summer monsoon, such as north–south oriented belt of heavy rainfall along the Western Ghats with sharp gradient of rainfall between the west coast heavy rain region and the rain shadow region to the east, pockets of heavy rainfall along the location of monsoon trough, over the east central parts of the country, over north-east India, along the foothills of Himalayas and over the north Bay of Bengal. The KALPANA-1 and 3B42RT products reproduce only the broadest features of mean monsoon seasonal rainfall. The near real-time products 3B42RT and KALPANA-1 underestimate the orographic heavy rainfall along the Western Ghats of India. The precipitation estimates from TRMM 3B42V6 product, when accumulated over the whole season, could reproduce the observed pattern. However, the TRMM 3B42RT and KALPANA-1 estimates are comparatively lower than the observed rainfall over most parts of the country during the season. Inter comparison reveals that the TRMM 3B42V6 product showed better skill in estimating the daily and seasonal mean rainfall over all India and also over four homogeneous regions of India.

The study provides a concise and synthesized documentation of the current level of skill of the NCEP GFS day-1 to day-5 precipitation forecasts during Indian summer monsoon of 2008, making detailed inter-comparison with daily rainfall analysis from the use of rain gauge observations and satellite (KALPANA-1) derived Quantitative Precipitation Estimates (QPE) obtained from IMD. Model performance is evaluated for day-1 to day-5 forecasts of 24-hr accumulated precipitation in terms of several accuracy and skill measures. Forecast quality and potential value are found to depend strongly on the verification dataset, geographic region and precipitation threshold. Precipitation forecasts of the model, when accumulated over the whole season, reproduce the observed pattern. However, the model predicted rainfall is comparatively higher than the observed rainfall over most parts of the country during the season. The model showed considerable skill in predicting the daily and seasonal mean rainfall over all India and also over four broad homogeneous regions of India. The model bias for rainfall prediction changes from overestimation to underestimation at the threshold of 25 mm/day except for day-1 forecast. Model skill falls dramatically for occurrence rainfall thresholds greater than 10 mm/day. This implies that the model is much better at predicting the occurrence of rainfall than they are at predicting the magnitude and location of the peak values. Various skill score and categorical statistics for the NCEP GFS model rainfall forecast for monsoon 2008 are prepared and discussed.

During the summer monsoon (1 June to 30 September) 2007, real-time district level rainfall forecasts in short-range time scale were generated for Indian region applying multimodel ensemble technique. The pre-assigned grid point weights on the basis of correlation coefficients (CC) between the observed values and forecast values are determined for each constituent model at the resolution of 0.5° × 0.5° utilizing two seasons datasets (1 June to 30 September, 2005 and 2006), and the multimodel ensemble forecasts (day 1 and day 2 forecasts) are generated at the same resolution on a real-time basis. The ensemble forecast fields are then used to prepare forecasts for each district taking the average value of all grid points falling in a particular district. In this paper we examined the performance skill of the multimodel ensemble-based real-time district level short-range forecast of rainfall. It has clearly emerged from the results that the multimodel ensemble technique reported in this study is superior to each ensemble member. District wise performance of the ensemble rainfall forecast reveals that the technique, in general, is capable of providing reasonably good forecast skill over most districts of the country, particularly over the districts where the monsoon systems are dominant. Though the procedure shows appreciable skill to predict occurrence or non-occurrence of rainfall at the district level, it always underestimates rainfall amount, particularly in heavy rainfall events. Possible reasons of this failure may be due to model bias and poor data assimilation procedure.

Tropical cyclone (TC) formation involves interaction of a variety of processes, both on the synoptic scale as well as the mesoscale. Gray (1979) identified several large-scale conditions as necessary for tropical cyclogenesis, including preexisting low-level relative vorticity and high mid-tropospheric humidity. TCs are one of the most dangerous natural calamities throughout the globe. The Bay of Bengal TC disaster is the costliest and deadliest natural hazard in the Indian sub-continent. It has a significant socio-economic impact on the countries bordering the Bay of Bengal, especially India, Bangladesh and Myanmar. Every year, they cause considerable loss of life and do immense damage to property. India and Bangladesh have a coastline of more than 8,000 km, which is prone to very severe cyclone formations in the Arabian Sea and Bay of Bengal. Therefore, reasonably accurate prediction of these storms has great importance to avoid the loss of valuable lives.

Cyclogenesis of tropical storms has not yet been completely understood, but these storms develop over five major zones in the Northern hemisphere. Bay of Bengal (BoB) and the Arabian Sea are two such zones. Cyclones, tropical disturbances of all intensities with reference to wind speed greater than 33 knots, form in the BoB and in the Arabian Sea almost throughout the year. They are more frequent in the Bay and in Arabian Sea in the region between latitude 6°N and 15°N during the pre-monsoon and post-monsoon seasons and are least frequent during the winter. Tropical cyclone formation involves interaction of a variety of processes, both on the synoptic scale as well as the mesoscale. Gray (1979) identified several large-scale conditions as necessary for tropical cyclogenesis, including preexisting low-level relative vorticity and high mid-tropospheric humidity. Chen and Frank (1993) noted that mesoscale convective vortices (MCVs) spawned by tropical convection could be a possible mechanism for forming the initial low-level relative vorticity maximum. This variety of tropical cyclogenesis mechanisms provides a challenge in identifying the sufficient conditions for genesis.

In this paper a Multi-Model Ensemble (MM E) technique is experimented for improving day to day rainfall forecast over India in short to medium range time scale during summer monsoon of 2010. Four operational global Numerical Weather Prediction (NWP) models namely, ECMWF, JMA, NCEP GFS and UKMO available on real time basis at India Meteorological Department (IMD), New Delhi are used simultaneously with appropriate weights to obtain the MME Technique. In this technique, weights for each NWP model at each grid point is assigned on the basis of the correlation coefficient (CC) between model forecasts and observed daily rainfall time series of south west monsoon (JJAS) season. Apart from MM E, a simple ensemble mean (ENSM ) forecast are also generated and experimented. The rainfall prediction skill of the weighted MM E is examined against ENSM and member models. The inter-comparison reveals that the weighted MM E is able to provide more accurate forecast of rainfall over Indian monsoon region by taking the strength of each constituent member model. It has been further found that the rainfall prediction skill of MM E is higher as compared to ENSM and member models in the short range time scale. The rainfall prediction skill of weighted MM E technique improved significantly over India., U ovom radu primijenjena je metoda višemodelskog ansambla (MME) s ciljem poboljšanja kratkoročnih do srednjoročnih dnevnih prognoza količine oborine nad Indijom tijekom ljetnog monsuna 2010. godine. pri tome su istovremeno te s odgovarajućim težinama korištena četiri operativna globalna modela za numeričku prognozu vremena (NWP): ECMWF, JMA, NCEP, GFS i UKMO, a koji su na raspolaganju u realnom vremenu pri Indijskom meteorološkom odsjeku (IMD) u New Delhiju. Težine za svaki NWP model u svakoj točki mreže pridijeljene su na temelju koeficijenta korelacije (CC) između modelskih prognoza i mjerenog niza dnevne količine oborine za sezonu jugozapadnog monsuna (od lipnja do rujna). Pored MME, generirane su i ispitane jednostavne prognoze dobivene srednjakom ansambla (ENSM). Uspješnost prognoze količine oborine dobivene MME metodom procijenjena je usporedbom rezultata dobivenih tom metodom i onih na temelju ENSM te sa svakim pojedinačnim modelom. Međusobna usporedba pokazuje da metoda MME točnije prognozira količinu oborine u području indijskog monsuna ponderiranjem doprinosa svakog pojedinog modela u ansamblu. Nadalje, utvrđena je veća uspješnost kratkoročnih prognoza količine oborine pomoću metode mmE u odnosu na rezultate metode ENSM te u odnosu na prognoze pojedinačnih modela ansambla. Primjena ponderirane metode MME značajno poboljšava uspješnost prognoze količine oborine nad Indijom.

We have investigated the performance of four bias correction methods for improving both maximum and minimum temperature forecasts produced by the NWP model. The objective of bias correction is to minimize the systematic error of the next forecast using bias from past errors. The need for bias corrections arises from the many sources of systematic errors in NWP modeling systems. NWP models have shortcomings in the physical parameterization of weather events and have the inability to handle sub-grid phenomena successfully. The statistical algorithms used for minimizing the bias of the next forecast are Running-Mean (RM) bias correction, Best Easy Systematic (BES) estimator, simple Linear Regression (LR) and the Nearest Neighborhood Weighted (NNW) mean, as they are suitable for small samples. Bias correction is done for four global NWP model maximum and minimum temperature forecasts. The magnitude of the bias at a grid point depends upon geographical location and season. Validation of the bias correction methodology is carried out using daily observed and bias corrected model maximum and minimum temperature forecast over India during July-September 2011. The bias corrected NWP model forecast generally outperforms direct model output (DMO). The spatial distribution of Mean Absolute Error (MAE) and Root mean squared Error (RMSE) for bias corrected forecast over India indicate that both the RM and NNW methods produces the best skill among other bias correction methods. The inter-comparison reveals that statistical bias correction methods improves the DMO forecast in terms of accuracy in forecast and has the potential for operational applications.

The high-resolution global ensemble prediction system was implemented at IITM (Indian Institute of Tropical Meteorology) in June 2018 and handed over to India Meteorological Department (IMD) for operational running. During the Tropical Cyclone (TC) season over the North Indian Ocean, real time forecasts of ensemble tracks, intensity and strike probability of TCs are provided to IMD. This paper evaluates the skill of the model in predicting TCs during 2018, 2019 and two cases in 2020. A total of 13 cases are considered and various skill scores are calculated for the TC predictions. This study evaluates and highlights the importance of the Ensemble Prediction System for Tropical Cyclone forecasting. The key finding from this analysis is that, the higher skill of ensemble prediction system based on GEFS at longer lead time compared with the deterministic prediction. This is particularly beneficial for operational forecaster for issuing early warnings.