Maximum Sustained Wind Research Articles

Increased severe landfall typhoons in China since 2004

AbstractSevere landfall typhoons (SLTYs), defined as those with maximum sustained wind speed ≥41.5 m s−1 at landfalls, strongly affect the coastal regions of China and cause grave losses of life and property. In this study, we analyse the SLTYs in China in peak summer (July–September) for the period of 1973–2017, during which typhoon landfall intensity (LFI) experienced significantly abrupt strengthening in 1987 and 2003. The period is divided into three sub‐periods: period‐I (1973–1987), period‐II (1988–2003), and period‐III (2004–2017). It is found that the increase in LFI is dominated by the enhancement of intensification rate but not intensification duration. Meanwhile, the SLTYs in China have also abruptly increased since 2004, characterized by the count of about 65.9% (72.7%) of SLTYs in China (South China) in period‐III. Further analysis shows that the warming subsurface ocean heat content (OHC) over the northern South China Sea (SCS) plays a key role in modulating typhoon's intensification, which subsequently enhances the easterly steering flow leading typhoons to move westward. Low‐level water vapour convergence and vorticity are enhanced accompanied by warming northern SCS along the coastal region of southeastern China, resulting rapid typhoon intensification before landfall. About 9.7% of typhoons with landfall in South China experienced a rapid intensification process in 24 hr before landfalls in period‐III. However, the percentage is only 1.6 and 3.1% for period‐I and period‐II, respectively. The warming land surface over South China plays a minor role in attracting typhoons to landfall in South China, which named as “warmward landfall”. Overall, the abrupt enhancement of LFI and more SLTYs in China since 2004 are governed by the warming ocean, while the abnormal easterly steering flow and warming land surface over South China leading typhoons to move westward play a secondary role.

Quasi-operational forecast guidance of extremely severe cyclonic storm Fani over the Bay of Bengal using high-resolution mesoscale models

Tropical cyclone (TC) “Fani” (April 26–May 04, 2019) over the Bay of Bengal (BoB) is the first extreme severe cyclonic storm (ESCS) of the season that struck Puri coast with a maximum sustained wind speed of 110 knots. In the past 130 years, TC Fani is the first of its kind with the peculiarity of being persistent with the intensity of severe cyclonic storm (SCS) over land for more than 24 h, thus causing substantial casualties in the major cities of Odisha. Two high-resolution mesoscale modeling systems, (1) state-of-the-art advanced research version of weather research and forecasting (WRF-ARW) model with a single fixed domain (9 km horizontal resolution) and (2) TC specific hurricane WRF (HWRF) with three domains (27/9/3 km horizontal resolution) are used for real-time prediction of Fani in quasi-operational setup. Storms surge prediction is also carried out using IIT Delhi dynamical storm surge model. Both the models, WRF and HWRF, were able to predict the system from its genesis stage (5 days in advance of landfall) with reasonable accuracy in position and strength. The tracks from both models are in good agreement with the observed track. The average track errors for WRF and HWRF are 101 km and 85 km, respectively. The landfall time and position are well predicted, with 60 h of lead time. HWRF model performance in predicting landfall time, position, and intensity is significant. Along with the statistical analysis, structural and vertical analysis of wind, enthalpy flux, and thermal distribution are carried out by considering two represented initial conditions from both WRF and HWRF models. Maximum reflectivity and precipitable water diagnostic studies depict that both the models show asymmetricity at the peak intensity of ESCS. The rainfall during the landfall day from both the models is comparable with the gauge-satellite merged observed rainfall datasets. Along with TC track, intensity, and structural forecasts associated with storm surge at different vulnerable coastal cities are attempted to predict with reasonable accuracy.

Field surveys and numerical modeling of the August 2016 Typhoon Lionrock along the northeastern coast of Japan: the first typhoon making landfall in Tohoku region

Typhoon Lionrock, also known as the national number 1610 in Japan, caused severe flooding in east Japan in August 28–31, 2016, leaving a death toll of 22. With a maximum sustained wind speed of ~ 220 km/h from the Joint Typhoon Warning Center’s best track, Lionrock was classified as a category 4 hurricane in Saffir–Simpson Hurricane Wind Scale and as a typhoon in Japan Meteorological Agency’s scale. Lionrock was among unique typhoons as it started its landfall from north of Japan. Here, we studied the characteristics of this typhoon through tide gauge data analysis, field surveys and numerical modeling. Tide gauge analysis showed that the surges generated by Lionrock were in the ranges of 15–55 cm with surge duration of 0.8–3.1 days. Our field surveys revealed that the damage to coastal communities/structures was moderate although it caused severe flooding inland. We measured a maximum coastal wave runup of 4.3 m in Iwaisaki. Such a runup was smaller than that generated by other category 4 typhoons hitting Japan in the past. Our numerical model was able to reproduce the storm surge generated by the 2016 Typhoon Lionrock. This validated numerical model can be used in the future for typhoon-hazard studies along the coast of northeastern Japan. Despite relatively small surge/wave runups in coastal areas, Lionrock’s death toll was more than that of some other category 4 typhoons. We attribute this to various primary (e.g., flooding, surges, waves, strong winds) and secondary (e.g., landslides, coastal erosions, debris flows, wind-blown debris) mechanisms and their combinations and interactions that contribute to damage/death during a typhoon event.

A Novel Deep Learning Approach for Tropical Cyclone Track Prediction Based on Auto-Encoder and Gated Recurrent Unit Networks

Under global climate change, the frequency of typhoons and their strong wind, heavy rain, and storm surge increase, seriously threatening the life and property of human society. However, traditional tropical cyclone track prediction methods have difficulties in processing large amounts of complex data in terms of prediction efficiency and accuracy. Recently, deep learning methods have shown a potential capability to process complex data efficiently and accurately. In this paper, we propose a novel data-driven approach based on auto-encoder (AE) and gated recurrent unit (GRU) models to forecast tropical cyclone landing locations using the historical tropical cyclone tracks and various meteorological attributes. This approach fuses a data preprocessing layer, an AE layer, and a GRU layer with a customized batch process. The model is trained on a real-world tropical cyclone dataset from the years 1945–2017. Through a comparison with existing forecasting methods, the results verified that our proposed model performed around 15%, 42%, and 56% better than the Numerical Weather Prediction model (NWP) in 24, 48, and 72 h forecasts, and 27%, 13%, 17%, and 17% better than RNN, AE-RNN, GRU, and LSTM, respectively, in 24 h forecasts, using the absolute position error. In addition, a comparison of the meteorological variables indicated that the variable maximum sustained wind speed had the most significant effect on tropical cyclone track prediction.

Assessment of NCMRWF Global Ensemble System with differing ensemble populations for Tropical cyclone prediction

Ensemble Prediction System (EPS) estimates the probability distribution of atmospheric states through multiple integrations of the Numerical Weather Prediction (NWP) model/s with number of perturbed initial conditions. The choice of an ensemble size can affect the skill of the ensemble forecast. Since more ensemble members require more computational requirements, it is important to determine whether the larger ensemble size significantly improves the forecast uncertainty estimates. The EPS at National Centre for Medium Range Weather Forecasting (NEPS) is assessed for the prediction of track and intensity of Tropical Cyclones (TC) over Bay of Bengal (BoB). Medium range forecast of four tropical cyclones over BoB with three different ensemble sizes namely E11 (11 perturbed members), E22 (22 perturbed members) and E44 (44 perturbed members) are presented here. The results of the study indicate that doubling of the ensemble population reduced the mean initial position errors by 25% and increase of ensemble size by four times (E11 to E44) reduced the errors by about 38%. The ensemble mean forecast track has lower errors compared to the deterministic forecast (CNTL) at shorter lead times for all ensemble sizes. The impact of ensemble size is significant on the improvement of mean track prediction skill at longer lead times. Median of central pressure error is very close to zero for all the sizes and the forecasts are slightly underestimated compared to the observed values. The initial error in Maximum Sustained surface Wind speed (MSW) is smaller for E44 as compared to E22 and E11. Interestingly, in all lead time forecasts and all the ensemble sizes, ensemble mean MSW errors are nearly within ±5 ms−1. There is only slight improvement in the ensemble mean precipitation forecasts with the increase of ensemble size. NEPS fails to resolve the heavier rainfall distribution and has the tendency to over-predict light precipitation. Ensembles with larger size predict the TC strike area of intense storms with higher probabilities due to better consensus among member tracks fueled by improved steering flow. But few cases have shown E11 or E22 perform equally well in forecasting TC propagation and in some cases even better compared to E44. Threshold oriented verification was carried out for winds at 850 hPa considering all the cases collectively. Brier Score (BS) and its components are very close to one another for higher thresholds of winds. In general, E44 has lower BS for all different thresholds with small lead time forecasts. Smaller ensembles have slightly better reliability while large ensembles improved the resolution for lower thresholds of wind speed. Continuous ranked probability score indicate that predicted probability distribution agrees well with the analyzed cumulative distribution. Area under relative operating characteristic curve exhibits better discriminating ability of probabilistic events with larger ensemble sizes. This study clearly showed that an increase in the ensemble size has significant beneficial impact on the reduction of the percentage of outliers thereby improving the potential predictive skill of the unexpected events.

A Long Short-Term Memory Model for Global Rapid Intensification Prediction

ABSTRACTRapid intensification (RI) is an outstanding source of error in tropical cyclone (TC) intensity predictions. RI is generally defined as a 24-h increase in TC maximum sustained surface wind speed greater than some threshold, typically 25, 30, or 35 kt (1 kt ≈ 0.51 m s−1). Here, a long short-term memory (LSTM) model for probabilistic RI predictions is developed and evaluated. The variables (features) of the model include storm characteristics (e.g., storm intensity) and environmental variables (e.g., vertical shear) over the previous 48 h. A basin-aware RI prediction model is trained (1981–2009), validated (2010–13), and tested (2014–17) on global data. Models are trained on overlapping 48-h data, which allows multiple training examples for each storm. A challenge is that the data are highly unbalanced in the sense that there are many more non-RI cases than RI cases. To cope with this data imbalance, the synthetic minority-oversampling technique (SMOTE) is used to balance the training data by generating artificial RI cases. Model ensembling is also applied to improve prediction skill further. The model’s Brier skill scores in the Atlantic and eastern North Pacific are higher than those of operational predictions for RI thresholds of 25 and 30 kt and comparable for 35 kt on the independent test data. Composites of the features associated with RI and non-RI situations provide physical insights for how the model discriminates between RI and non-RI cases. Prediction case studies are presented for some recent storms.

Surface Pressure a More Skillful Predictor of Normalized Hurricane Damage than Maximum Sustained Wind

AbstractAtlantic hurricane seasons have a long history of causing significant financial impacts, with Harvey, Irma, Maria, Florence, and Michael combining to incur more than 345 billion USD in direct economic damage during 2017–2018. While Michael’s damage was primarily wind and storm surge-driven, Florence’s and Harvey’s damage was predominantly rainfall and inland flood-driven. Several revised scales have been proposed to replace the Saffir–Simpson Hurricane Wind Scale (SSHWS), which currently only categorizes the hurricane wind threat, while not explicitly handling the totality of storm impacts including storm surge and rainfall. However, most of these newly-proposed scales are not easily calculated in real-time, nor can they be reliably calculated historically. In particular, they depend on storm wind radii, which remain very uncertain. Herein, we analyze the relationship between normalized historical damage caused by continental United States (CONUS) landfalling hurricanes from 1900–2018 with both maximum sustained wind speed (Vmax) and minimum sea level pressure (MSLP). We show that MSLP is a more skillful predictor of normalized damage than Vmax, with a significantly higher rank correlation between normalized damage and MSLP (rrank = 0.77) than between normalized damage and Vmax (rrank = 0.66) for all CONUS landfalling hurricanes. MSLP has served as a much better predictor of hurricane damage in recent years than Vmax, with large hurricanes such as Ike (2008) and Sandy (2012) causing much more damage than anticipated from their SSHWS ranking. MSLP is also a more accurately-measured quantity than is Vmax, making it an ideal quantity for evaluating a hurricane’s potential damage.

Response and recovery of Horn and Petit Bois Islands, Mississippi, USA to tropical cyclone impacts: 2004–2016

Horn and Petit Bois islands are two of five Mississippi (MS) barrier islands that provide physical protection from tropical cyclones threatening the MS Gulf Coast, in addition to critical habitat for the northern Gulf of Mexico (GOM). In September 2004, Hurricane Ivan removed a large volume of sediment from the eastern ends of Horn and Petit Bois islands with its 1 to 2 m storm surge and ~194 kph wind speeds. Then, in August 2005 Hurricane Katrina severely impacted the two islands again with its 3.5 to 5.5 m storm surge on Horn and Petit Bois islands, and estimated maximum sustained wind speeds of 204 kph at landfall in southeast Louisiana. Using topographic light detection and ranging (LIDAR) datasets from 2004 to 2016, spatial and temporal changes of the islands' area, sediment volumes, and shorelines were measured to ascertain their geomorphic responses and recovery rates following the impacts of these devastating tropical cyclones. During the 2004–05 hurricane seasons, Horn Island lost ~13.3% of its pre-hurricane Ivan land area, lost ~35.9% sediment volume, and had a total average shoreline change rate of −10 m/yr. Petit Bois Island also lost ~13.3% of its pre-Ivan land area, lost ~27% sediment volume, and had a shoreline change rate of −33 m/yr. Between 2005 (post-Katrina) and 2016, Horn Island recouped ~6.6% of its pre-Ivan land area and ~4.3% sediment volume, whereas Petit Bois Island recovered ~4% of its pre-Ivan land area and ~22.9% sediment volume. The overall averaged shoreline change rates between 2004 and 2016 were −2 m/yr for Horn Island and −3 m/yr for Petit Bois Island. These changes reflect that Horn Island is no longer stable, as its sediment supply cannot keep pace with the current rate of sediment loss, and that because Petit Bois Island's narrow central shoreline is retreating at a rate of 10 m/yr, the island is at risk of breaching during the next storm. Highlighting complex island response, the relationship between area and shoreline changes to that of volume changes was inconsistent.

IMPACT OF HURRICANE MARIA IN THE INCIDENCE OF ACUTE CORONARY SYNDROMES: A SINGLECENTER OBSERVATIONAL STUDY

Hurricane Maria made landfall in Yabucoa, Puerto Rico at approximately 6:15 AM of September 20, 2017 as a category 4 hurricane with maximum sustained winds of 155 mph. Damages were estimated at $90 billion, placing Maria as the third costliest cyclone in the United States reported since 1900.

Influence of Enso to Variability of Rain Chain at Tropical Cyclon in Southern East Nusa Tenggara

The Region of East Nusa Tenggara province which is geographically situated around Equator is an area immediately adjacent to place where the growth of tropical cyclones. This research to determine the variability of tropical cyclones that have happened, how the influence of ENSO on the variability of tropical cyclones, and how the impact of tropical cyclones on rainfall in East Nusa Tenggara. In this study, the rainfall data used is represented by 8 stations observations in the region of NTT, namely Kupang, Rote, Sabu, Waingapu, Ruteng, Maumere, Larantuka, and Alor, with period of 19 years ie the assessment year 1996-2014. The data was obtained from Era Interim ECMWF. Tropical cyclone data was obtained from JTWC-Japan and TCWC-Australia, covering a maximum sustained wind data, position, and lifetime cyclone. Rainfall data obtained from Climatological Station of Lasiana-Kupang. Results of the analysis showed that during the study period the total incidence of tropical cyclones in the south of East Nusa Tenggara many as 113 cases, with an average of 6 events cyclones per year and were distributed mainly in the winter between November and April. The results also show that ENSO influence on tropical cyclone variability indirectly to the parameter of tropical cyclones.

Estimating the True Maximum Sustained Wind Speed of a Tropical Cyclone from Spatially Averaged Observations

AbstractThe maximum sustained wind speed Vm of a tropical cyclone (TC) observed by a sensor varies with its spatial resolution. If unaccounted for, the difference between the “true” and observed Vm results in an error in estimation of Vm. The magnitude of the error is found to depend on the radius of maximum wind speed Rm and Vm itself. Quantitative relationships are established between Vm estimation errors and the TC characteristics. A correction algorithm is constructed as a scale factor to estimate the true Vm from coarsely resolved wind speed measurements observed by satellites. Without the correction, estimates of Vm made directly from the observations have root-mean-square differences of 1.77, 3.41, and 6.11 m s−1 given observations with a spatial resolution of 25, 40, and 70 km, respectively. When the proposed scale factors are applied to the observations, the errors are reduced to 0.69, 1.23, and 2.12 m s−1. A demonstration of the application of the correction algorithm throughout the life cycle of Hurricane Sergio in 2018 is also presented. It illustrates the value of having the scale factor depend on Rm and Vm, as opposed to using a fixed value, independent of TC characteristics.

Climatology of Passive Microwave Brightness Temperatures in Tropical Cyclones and their Relations to Storm Intensities as Seen by FY-3B/MWRI

A new database, the tropical cyclones passive microwave brightness temperature (TCsBT) database including 6273 overpasses of 503 tropical cyclones (TC) was established from 6-year (2011–2016) Fengyun-3B (FY-3B) Microwave Radiation Imager (MWRI) Level-1 brightness temperature (TB) data and TC best-track data. An algorithm to estimate the TC intensity is developed using MWRI TB’s from the database. The relationship between microwave TB and the maximum sustained surface wind (Vmax) of TCs is derived from the TCsBT database. A high correlation coefficient between MWRI channel TB and Vmax is found at the radial distance 50–100 km near the TC inner core. Brightness temperatures at 10.65, 18.70, 23.8, and 36.5 GHz increase but 89 GHz TB’s and polarization corrected TB at 36.5 GHz (PCT36.50) and PCT89 decrease with increasing TC intensity. The TCsBT database is further separated into the 5063 dependent samples (2010–2015) for the development of the TC intensity estimation algorithm and 1210 independent samples (2016) for algorithm verification. The stepwise regression method is used to select the optimal combination of storm intensity estimation variables from 12 candidate variables and four parameters (10.65h, 23.80v, 89.00v and PCT36.50) were selected for multiple regression models development. Among the four predictors, PCT36.50 contributes the most in estimating TC intensity. In addition, the errors are lower for estimating 6-h and 12-h future Vmax than estimating the current Vmax.

The Effect of the Warm Water and Its Weak Negative Feedback on the Rapid Intensification of Typhoon Hato (2017)

Typhoon Hato (2017) went through a rapid intensification (RI) process before making landfall in Zhuhai, Guangdong Province, as the observational data shows. Within 24 hours, its minimum sea level pressure deepened by 35hPa and its maximum sustained wind speed increased by 20m s^-1. According to satellite observations, Hato encountered a large area of warm water and two warm core rings before the RI process, and the average sea surface temperature cooling (SSTC) induced by Hato was only around 0.73℃. Air-sea coupled simulations were implemented to investigate the specific impact of the warm water on its RI process. The results showed that the warm water played an important role by facilitating the RI process by around 20%. Sea surface temperature budget analysis showed that the SSTC induced by mixing mechanism was not obvious due to the warm water. Besides, the cold advection hardly caused any SSTC, either. Therefore, the SSTC induced by Hato was much weaker compared with that in general cases. The negative feedback between ocean and Hato was restrained and abundant heat and moisture were sufficiently supplied to Hato. The warm water helped heat flux increase by around 20%, too. Therefore, the warm water influenced the structure and the intensity of Hato. Although there might be other factors that also participated in the RI process, this study focused on air-sea interaction in tropical cyclone forecast and discussed the impact of warm water on the intensity and structure of a tropical cyclone.

An overview of the impact of Hurricane Harvey on chemical and process facilities in Texas

Category 4 Hurricane Harvey with maximum sustained winds of approximately 212 km/h and more than 1000 mm of rainfall reached the densely populated Houston area, Texas, resulting in unprecedented flooding and damage to the onshore industrial facilities. In order to analyze the impact of flood, rainfall, and wind on onshore chemical and process facilities during Harvey, an investigation of the damage suffered by onshore chemical and process facilities is conducted using government incident databases. Based on the locations of the damaged equipment, the corresponding maximum flood depth, accumulated rainfall, and wind speed were identified through online GIS interactive maps and the software ArcGIS. In line with the previous studies in the field of natural-technological accidents (Natechs), atmospheric storage tanks were identified as the most vulnerable type of process equipment. Flooding and unprecedented rainfall were identified as the main causes of Natechs during hurricane Harvey. Further, compared to the common failure modes observed during previous hurricanes, the roof sinking of tanks with floating roofs due to excessive accumulated rainfall was identified as a serious failure mode during hurricane Harvey. In order to reduce the risk of roof sinking in future hurricanes, the design standards of floating-roof storage tanks need to be updated to enable withstanding excessive rainfalls in the future.

Team Rubicon Medical Response to Hurricane Dorian in the Bahamas.

On September 1, 2019, Hurricane Dorian made landfall as a category 5 hurricane on Great Abaco Island, Bahamas. Hurricane Dorian matched the "Labor Day" hurricane of 1935 as the strongest recorded Atlantic hurricane to make landfall with maximum sustained winds of 185 miles/h.1 At the request of the Government of the Bahamas, Team Rubicon activated a World Health Organization Type 1 Mobile Emergency Medical Team and responded to Great Abaco Island. The team provided medical care and reconnaissance of medical clinics on the island and surrounding cays….

Surface Pressure a More Skillful Predictor of Normalized Hurricane Damage than Maximum Sustained Wind

Surface Pressure a More Skillful Predictor of Normalized Hurricane Damage than Maximum Sustained Wind

Surface Pressure a More Skillful Predictor of Normalized Hurricane Damage than Maximum Sustained Wind

Surface Pressure a More Skillful Predictor of Normalized Hurricane Damage than Maximum Sustained Wind

Impacts of Ocean Warming, Sea Level Rise, and Coastline Management on Storm Surge in a Semienclosed Bay

AbstractRegional atmosphere and ocean models are used to investigate how sea level rise, ocean warming, and coastline management affect storm surge in the semienclosed Chesapeake Bay. Using Hurricane Isabel (2003) as a case study, the storm is placed under the climatic conditions projected for 2050 and 2100. Higher sea surface temperature increases the latent heat release from the ocean, resulting in large increases in storm intensity and maximum sustained wind speeds. The storm surge height, defined as the difference between storm tide and astronomical tide, is amplified in the future climate and amounts to ~30% of the relative sea level rise. Hardening shorelines further increases the storm surge height in the middle and upper parts of the Bay by up to 0.5 m. Sea level rise has a modest impact on the storm surge height: decreasing it by 0.1–0.2 m if the low‐lying areas are allowed to be flooded but increases it by 0.1–0.3 m if hypothetic walls are placed at the current coastline. Ocean warming is the main driver of storm surge amplification in the future climate. Energy budget analysis shows that allowing flooding over low‐lying areas leads to a significant energy loss and reduces the storm surge height in the estuary.

An assessment of the impact of oceanic initial conditions on the interaction of upper ocean with the tropical cyclones in the Arabian Sea

ABSTRACT The Regional Ocean Modelling System (ROMS) is utilised to estimate the cyclone-induced mixing and cooling associated with two tropical cyclones, namely Phet and Nilofar over the Arabian Sea (AS). Numerical experiments were carried out with realistic and idealistic oceanic conditions to assess the impact of oceanic initial condition and stratification on the interaction of upper ocean with the tropical cyclones in the AS. The model simulated profiles of temperature and baroclinic kinetic energy are analysed to examine the post-cyclonic cooling and vertical distribution of inertial kinetic energy which interacts with the upper-ocean stratification. Following the cyclone-induced upwelling, the inertial mixing acts to deepen the mixed layer depth (MLD) up to 50 and 100 m from its initial values of 15 and 22 m for the Phet and Nilofar, respectively. The analysis suggests that the combined effect of higher maximum sustained winds and slower translational speed of Nilofar cyclone leads to the excessive cooling (by 1.5°C) of the sea surface as compared to Phet. A decrement in the magnitude of cooling is observed when oceanic initial stratification was altered. The available baroclinic velocity shear at different depths found to modulate the magnitude of upper-ocean cooling in different model experiments.

Performance assessment of WRF model in simulating the very severe cyclonic storm “TITLI” in the Bay of Bengal: A case study

The present study has been conducted to assess the performance of the Weather Research and Forecasting – Advanced Research (WRF-ARW, hereinafter named as WRF) model in simulating the very severe cyclonic storm (VSCS) “Titli” which formed over the Bay of Bengal during 08–13 October, 2018. The simulated meteorological variables viz. 10-m wind vectors, surface latent heat flux, rainfall, track and intensity based on minimum sea level pressure (MSLP), maximum sustained surface wind (MSW) are assessed to evaluate the model performance. The results are compared with India Meteorological Department (IMD) observations along with verification analyses [FiNaL analysis (FNL)]. The model simulated 24- h accumulation of rainfall verification is carried out by using the Method for Object based Diagnostic Evaluation (MODE) tool of Model Evaluation Tool Version 8 (MET-V8). The various statistical skill scores obtained from the MODE clearly indicates that the overall performance of WRF model in predicting the rainfall is reasonably good, mainly at lower forecast length (36–60 hr). The intensity and spatial distribution of rainfall are well matched with the verified analysis as well as with the Tropical Rainfall Measuring Mission (TRMM) observations. The analysis of maximum latent heat flux in the region of strongest surface wind suggests that the circulation is maintained due to increased transport of heat and moisture from the ocean. The track error analysis shows more error during the model initialization, which gradually decreases with the advance of forecast time. Efficacy of the WRF model in simulating the tropical cyclone (TC) is very distinct from the present study.