Hurricane Track Research Articles

Tropical cyclone forecast using NCMRWF Global (12 km) and regional (4 km) models

Countries adjoining the North Indian Ocean (NIO) region are among the world's worst affected areas by tropical cyclones (TCs). An increase in frequency and intensity of TCs affecting this basin is noticed in recent years. Timely and accurate prediction of a TC can lead to a decrease in damages to life and property caused by the cyclone. In recent times, the forecasts of TC tracks and intensity have improved due to advancements in the resolution, data assimilation techniques and physics of Numerical Weather Prediction (NWP) models and improvements in the model's initial condition. In this study, we have analysed the forecasts of Super Cyclone (SuCS) 'Amphan' that occurred over the Bay of Bengal during 16-21 May, 2020 obtained from the Global and Regional version of the NCMRWF Unified Model, i.e., NCUM-G and NCUM-R, respectively. The analysis of the track error shows that the initial position and 24-hour forecast errors in both the models are very close. However, the 48 to 72-hour forecast errors are much lower in the NCUM-R compared to NCUM-G. The percentage decrease in the track errors from global to regional model is about 12% in the 48-hour forecasts and 17% in the 72-hour forecasts. The landfall position error is less than 50 km in the predictions after 18th May, 2020 in NCUM-G. However, NCUM-R shows a lower landfall location error as compared to NCUM-G. Comparing the intensity forecasts from NCUM-G and NCUM-R shows that the 48 hrs forecast of minimum sea level pressure (SLP) based on initial conditions of 16th May, 2020 are very close to the observed intensity. The rapid intensification of Amphan is also well predicted by both the models, although with a delay. The cyclone structure shows that the 850 hPa vorticity core is much stronger in NCUM-R forecasts than NCUM-G. The warm-core system in the NCUM-R forecasts is also more organized than in NCUM-G. On the other hand, the maximum wind radius is greater in NCUM-G forecasts than NCUM-R.

Comparative performance of HWRF model coupled with POM and HYCOM for tropical cyclones over North Indian Ocean

The demonstration of air-sea interaction in the high-resolution numerical weather prediction (NWP) model is the necessary condition to predict the extreme event like tropical cyclone. It is already established that the coupled mesoscale models are capable of forecasting the track and intensity of tropical cyclones with reasonable skill over North Indian Ocean (NIO) basin and over other ocean basins as well. The Hurricane Weather Research and Forecasting (HWRF) model is consequently operational by India Meteorological Department (IMD) for the forecasting tropical cyclones like many other National Meteorological and Hydrological Services. Two different coupled versions of HWRF modeling systems with feature based Princeton Ocean Model (POM) and eddy-resolving Hybrid Coordinate Ocean Model (HYCOM) are employed for the real-time forecasting of tropical cyclones over NIO. In this study, the comparison of forecast skills between two coupled variants in predicting track and intensity for all eight cyclones during 2019 has been carried out. The results show that the track prediction of all cyclones by HWRF model coupled with HYCOM is comparatively better than that with POM. But the intensity prediction skills of both coupled models do not portray any obvious superiority for all forecast hours. The absolute intensity errors of HWRF model with POM coupling are large compared to HYCOM during initial phase up to 60 hours of forecast except for Vayu cyclone. But, the errors in intensity exhibited by POM are a bit smaller compared to HYCOM for longer lead period. However, the standard deviation is very large for both models and numbers of forecasts verified are also less. In case of two cyclones of 2020, the ability of coupled variant with HYCOM is superior to POM in forecasting the tracks of cyclones. Considering the skills of both the models for intensity prediction, it is clear that the use of coupled HCOM model for short-lived weaker storms is advantageous but the performance of the model is not consistently maintained with longer lead time for intense and long-lived cyclone. A brief assessment of surface fluxes and Sea Surface Temperature reveals that HYCOM portray more realistic ocean condition in turn surface fluxes compared to POM and therefore the coupled variant of HWRF with HYCOM is superior to the variant with POM while forecasting cyclones over deep Ocean.

Assimilation of satellite and other data for the forecasting of tropical cyclones over NIO

Tropical Cyclones that occur over the North Indian Ocean (NIO) are warm cored, highly devastating and short-lived compared to other oceanic basins. Numerical prediction of these cyclones is mainly an initial value problem. Accuracy of the initial position largely depends on forecasting model, density & quality of observations and data assimilation scheme. State of the art numerical modeling systems are able to predict the genesis of most cyclonic systems reasonably well, but often, they fail to maintain the correct position and intensity. Satellite observations play an important role in cyclone prediction as they also cover data sparse open ocean. Often intensity and structural characteristics of these systems need to be inferred from the remotely sensed data. Satellite observations are increasingly being used to better initialize the tropical cyclones' location and intensity in the NWP systems. This study highlights India's efforts in the reception and assimilation of satellite and various other observations from different sources. The impacts of these observations are studied by carrying out Observing System Experiment (OSE) and through Forecast Sensitivity Observation Impact (FSOI) studies. Some of the recent OSEs on Tropical Cyclones over the NIO and a list of more beneficial observations through FSOI are presented in this paper.

Evolution of tropical cyclone forecasts of dynamical - Statistical Cyclone Prediction System (CPS) over the North Indian Ocean during the decade (2010-2019)

Tropical cyclone (TC) forecasting over the North Indian Ocean (NIO) has been improved significantly during past decade (2010-2019). The improvement is largely attributed to the numerical weather prediction (NWP) models and dynamical-statistical Cyclone Prediction System (CPS). The CPS has five components namely, (i) Genesis Potential Parameter (GPP), (ii) Multi-Model Ensemble (MME) technique for track prediction, (iii) Statistical Cyclone Intensity Prediction (SCIP), (iv) Rapid intensification (RI) and (v) Decay model for intensity forecast after landfall. Performance of CPS and NWP models is assessed to review the improvement during the decade. Forecast analysis revealed low false alarm ratio (0.13), high probability of detection (0.95) and high critical success index (0.84) for GPP. Mean track error of MME was ranged from 67 km at 24h to 246 km at 120 h and about 30% less than NWP models. The MME error has also reduced by 52% to 55% for 24 h to 48 h and 41% to 24% for 72 h to 96 h forecast during 2010-2019. Mean landfall point error was ranged from 31 km at 24 h to 127 km at 120 h and landfall time error was ranged from 2.2 h at 24 h to 8.1 h at 120 h. Mean intensity error of SCIP model was ranged from 8.3 kt at 24 h to 12.6 kt at 120 h. Probabilistic rapid intensification index (RII) achieved a good Brier score (BS) 0.079 for RI forecasting. Mean decaying intensity error (after landfall) was ranged from 6.6 kt at 6 h to 3.3 kt at 24 h. There has been improvement of forecasts for NWP models also but MME outperformed all models. Results demonstrate the role of CPS for improvement of cyclone forecast over the NIO in past decade.

Global Ensemble Forecast System (GEFS T1534) evaluation for tropical cyclone prediction over the North Indian Ocean

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.

Impact of CYGNSS-Derived Winds on Tropical Cyclone Forecasts in a Global and Regional Model

AbstractAn observing system experiment was conducted to assess the impact of wind products derived from the Cyclone Global Navigation Satellite System (CYGNSS) on tropical cyclone track, maximum 10-m wind speed Vmax, and minimum sea level pressure forecasts. The experiment used a global data assimilation and forecast system, and the impact of both CYGNSS-derived scalar and vector wind retrievals was investigated. The CYGNSS-derived vector wind products were generated by optimally combining the scalar winds and a gridded a priori vector field. Additional tests investigated the impact of CYGNSS data on a regional model through the impact of lateral boundary and initial conditions from the global model during the developmental phase of Hurricane Michael (2018). In the global model, statistically significant track forecast improvements of 20–40 km were found in the first 60 h. The Vmax forecasts showed some significant degradations of ~2 kt at a few lead times, especially in the first 24 h. At most lead times, impacts were not statistically significant. Degradations in Vmax for Hurricane Michael in the global model were largely attributable to a failure of the CYGNSS-derived scalar wind test to produce rapid intensification in the forecast initialized at 0000 UTC 7 October. The storm in this test was notably less organized and symmetrical than in the control and CYGNSS-derived vector wind test. The regional model used initial and lateral boundary conditions from the global control and CYGNSS scalar wind tests. The regional forecasts showed large improvements in track, Vmax, and minimum sea level pressure.

Upper-Ocean Temperature Variability in the Gulf of Mexico with Implications for Hurricane Intensity

AbstractStrong winds in tropical cyclones (TCs) mix the ocean, causing cooler water from below the thermocline to be drawn upward, reducing sea surface temperature (SST). This decreases the air–sea temperature difference, limits available heat energy, and impacts TC intensity. Part of TC forecast accuracy therefore depends upon the ability to predict sea surface cooling; however, it is not well understood how underlying ocean conditions contribute to this cooling. Here, ~4400 Argo profiles in the Gulf of Mexico were used in a principal component analysis to identify the modes of variability in upper-ocean temperature, and a 1D mixed layer model was used to determine how the modes respond to surface forcing. It was found that the first two modes explain 75% of the variance in the data, with high mode-1 scores being broadly characterized as having warm SST and deep mixed layer and mode-2 scores being characterized as having high SST and a shallow mixed layer. Both modes have distinct seasonal and spatial variability. When subjected to the same model forcing, mode-1- and mode-2-characteristic waters with equal tropical cyclone heat potential (TCHP) respond very differently. Mode-2 SST cools faster than mode 1, with the difference being most pronounced at lower wind speeds and when comparing early-season storms with late-season storms. The results show that using TCHP as a marker for SST response during TC forcing is insufficient because it does not fully capture subsurface ocean thermal structure. This result underscores the need for continual subsurface monitoring so as to accurately initialize the upper ocean in coupled TC models.

Subsidence Warming in the Tropical Cyclogenesis of Cindy (2017): CPEX Observations and Coupled Modeling

AbstractThe formation of tropical cyclones (TCs) in unfavorable large-scale environments remains a challenge for TC forecasting. Tropical Storm (TS) Cindy (2017) formed at 1800 UTC 20 June 2017 in the Gulf of Mexico despite strong vertical wind shear, low midtropospheric relative humidity, and poorly organized convection. A key to TC genesis is the initial development of a warm core within an emergent cyclonic vortex, a process that occurs on small spatial scales and is often difficult to observe. TS Cindy was observed during the Convective Processes Experiment (CPEX) field campaign in 2017 by the NASA DC-8 aircraft, equipped with a Doppler wind lidar, precipitation radar, and GPS dropsondes. This study combines CPEX observations and a cloud-resolving, fully coupled atmosphere–wave–ocean numerical simulation to investigate the formation of TS Cindy. Prior to TC genesis, a shallow cyclonic circulation was embedded in a deep layer of west-southwesterly flow associated with an upper-level trough. Within the disturbance, a warm and dry anomaly was observed by dropsondes near the center of the cyclonic circulation, with a maximum at about the 2.5-km level. In the coupled model simulation, the temperature perturbation reached 5°C along with a dewpoint temperature depression of 8°C. Backward trajectory analysis shows that subsidence is primarily associated with a thermally indirect circulation along the western flank of the storm. Air parcels descend more than 1000 m toward the lower troposphere while warming up by 9°–12°C. The subsidence-induced virtual temperature perturbation in the 1.5–3.5-km layer accounts for 50% of the sea level pressure depression. Subsidence warming, therefore, played a key role in the genesis of TS Cindy.

Impact of Langmuir Turbulence, Wave Breaking, and Stokes Drift on Upper Ocean Dynamics Under Hurricane Conditions

AbstractOcean surface gravity waves can modulate the upper ocean circulation and vertical mixing through air‐sea interactions, wave breaking, Coriolis‐Stokes forcing, Langmuir turbulence and the grid resolved vortex force. However, the roles of these wave induced processes remain uncertain under hurricane conditions. This study examines the impact of these surface wave associated dynamics on the turbulent kinetic energy (TKE), thermal structure and currents in the upper ocean under idealized hurricane forcing using a coupled wave‐circulation model. Eight experiments were conducted with hurricanes moving at three translational speeds. The simulations include two fully coupled experiments and five additional coupled experiments in which wave effects are selectively disabled. All coupled experiments are compared with a baseline circulation simulation. Consistent with previous studies, our results indicate that Langmuir turbulence has the biggest impact on upper ocean currents, temperature and TKE during hurricanes. However, all considered wave effects contribute to vertical mixing and the two‐way fully coupled experiment differs the most from the baseline experiment. The combined wave effects reduce the surface currents by over 0.8 m/s in the front two quadrants of the slow hurricane and enhance the cold wake and near‐surface cooling by as much as 0.6°C, predominantly to the right of the storm track. The wave contributions to the cooling are inversely related to the hurricane translational speed, although their contributions to currents and TKE are correlated with hurricane translational speed. SST differences can alter the latent heat flux driving hurricanes, suggesting that wave dynamics should be included in hurricane forecast models.

Integrating Evacuation and Storm Surge Modeling Considering Potential Hurricane Tracks: The Case of Hurricane Irma in Southeast Florida

Hurricane Irma, in 2017, made an unusual landfall in South Florida and the unpredictability of the hurricane’s path challenged the evacuation process seriously and left many evacuees clueless. It was likely to hit Southeast Florida but suddenly shifted its path to the west coast of the peninsula, where the evacuation process had to change immediately without any time for individual decision-making. As such, this study aimed to develop a methodology to integrate evacuation and storm surge modeling with a case study analysis of Irma hitting Southeast Florida. For this purpose, a coupled storm surge and wave finite element model (ADCIRC+SWAN) was used to determine the inundation zones and roadways with higher inundation risk in Broward, Miami-Dade, and Palm Beach counties in Southeast Florida. This was fed into the evacuation modeling to estimate the regional clearance times and shelter availability in the selected counties. Findings show that it takes approximately three days to safely evacuate the populations in the study area. Modeling such integrated simulations before the hurricane hit the state could provide the information people in hurricane-prone areas need to decide to evacuate or not before the mandatory evacuation order is given.

Predicting the Gulf of Mexico Hurricane Season With 500‐hPa Temperature

AbstractWhile North Atlantic tropical cyclone (TC) forecasts are numerous, the number of Gulf of Mexico (GOM) storms is normally left ambiguous. Motivated by the 2020 GOM hurricane season, this study introduces two methods of anticipating upcoming GOM TC frequency on June 1 of each year: (a) statistically transforming Climate Forecast System (CFS)‐predicted June–November GOM 500‐hPa temperature (T500); and (b) applying a 0.284 multiplier, the climatological proportion of GOM versus North Atlantic TCs, to a representative, widely circulated North Atlantic‐wide forecast. Both methods perform skillfully, especially given their simplicity. Though warm T500 normally inhibits deep convection and is not generally considered among statistical TC forecasts, it is comparatively well represented by modeling systems. Additionally, T500 is well correlated with traditional, yet less accurately resolved, TC environmental parameters. While unfavorable to individual TCs, T500 generally (though indirectly) indicates several difficult‐to‐model physical aspects of more favorable GOM TC environments.

Unified ensemble mean forecasting of tropical cyclones based on the feature-oriented mean method

AbstractOperational and research applications generally use the consensus approach for forecasting the track and intensity of tropical cyclones (TCs) due to the spatial displacement of the TC location and structure in ensemble member forecasts. This approach simply averages the location and intensity information for TCs in individual ensemble members, which is distinct from the traditional pointwise arithmetic mean (AM) method for ensemble forecast fields. The consensus approach, despite having improved skills relative to the AM in predicting the TC intensity, cannot provide forecasts of the TC spatial structure. We introduced a unified TC ensemble mean forecast based on the feature-oriented mean (FM) method to overcome the inconsistency between the AM and consensus forecasts. FM spatially aligns the TC-related features in each ensemble field to their geographical mean positions before the amplitude of their features is averaged.We select 219 TC forecast samples during the summer of 2017 for an overall evaluation of the FM performance. The results show that the TC track consensus forecasts can differ from AM track forecasts by hundreds of kilometers at long lead times. AM also gives a systematic and statistically significant underestimation of the TC intensity compared with the consensus forecast. By contrast, FM has a very similar TC track and intensity forecast skill to the consensus approach. FM can also provide the corresponding ensemble mean forecasts of the TC spatial structure that are significantly more accurate than AM for the low- and upper-level circulation in TCs. The FM method has the potential to serve as a valuable unified ensemble mean approach for the TC prediction.

Understanding the Effects of Wind Intensity, Forward Speed, Pressure and Track on Generation and Propagation of Hurricane Irma Surges around Florida

In this study, it is demonstrated that hurricane wind intensity, forward speed, pressure, and track play an important role on the generation and propagation of coastal storm surges. Hurricane Irma, which heavily impacted the entire Florida peninsula in 2017, is used to study the storm surge sensitivity to varying storm characteristics. Results show that the west coast experiences a negative surge due to offshore wind of the approaching storm, but the positive surge returns after the hurricane eye passes over a location and wind became onshore. In the west coast peak, surges are intensified by an increase in onshore wind intensity and forward speed. In the Florida Keys, peak surges are intensified by an increase in wind intensity, a decrease in forward speed and a decrease in pressure. In southeast and east Florida, peak surges are intensified by decrease in pressure, although overall surges are less significant as the water can slide along the coastline. In the recessed coastline of Georgia-Carolinas, maximum surge is elevated by an increase in onshore wind intensity. Shifting the track westward increases peak surges on the west coast, while shifting the track eastward increases peak surge on the east coast. The results demonstrate a new understanding about the sensitivity of surge to varying parametric conditions and the importance of considering changes in the coastline orientation in storm surge predictions.

End-user Preference for and Understanding of Hurricane Forecast Graphs

Hurricane forecast graphics have the challenging task of communicating information about spatio-temporal uncertainty. This study assesses the impact of graph literacy and graph format on user preference and understanding. In a laboratory setting, we compared user responses to official National Hurricane Center advisory maps and alternative visualizations. Results indicate that prior experience with a visualization drives preference and that graph literacy, visualization format, and tropical cyclone characteristics, in combination, influence interpretations of hurricane forecast track. The findings from this study are expected to inform redesign efforts of hurricane risk communication products.

Effects of the Assimilation of Relative Humidity Reproduced From T‐PARCII and Himawari‐8 Satellite Imagery Using Dynamical Initialization and Ocean‐Coupled Model: A Case Study of Typhoon Lan (2017)

AbstractEffects of the assimilation of relative humidity (RH) reproduced from dropsonde data from the Tropical cyclones‐Pacific Asian Research Campaign for Improvement of Intensity estimations/forecasts (T‐PARCII) campaign and Himawari‐8 satellite data on the simulation of Typhoon Lan (2017) were investigated herein using a weather research and forecasting model. The lateral boundary and initial conditions were obtained from Global Forecast System (GFS) forecast data. Four experiments varying from the initial vortex, assimilation of the reproduced RH (ARH), and ocean model (OCEAN, three‐dimensional Price‐Weller‐Pinkel upper‐ocean circulation model) activation were conducted for 42 h to evaluate tropical cyclone (TC) forecast performance: the GFS experiment and dynamical initialization (DI) experiments such as DI, DI‐OCEAN, and DI‐ARH‐OCEAN, respectively. All track forecast errors were less than 100 km until landfall, that is, up to 36 h. TC intensity forecasts such as the minimum sea‐level pressure and maximum surface wind speed were slightly improved in DI‐related experiments compared to the GFS experiment. The DI‐ARH‐OCEAN experiment, in particular, demonstrated improvements in both TC forecasts and convective areas. The ocean‐coupled experiments yielded significant sea surface temperature cooling in the rear‐right quadrant of TC, forming a stable boundary layer that could suppress the convective activity, particularly in the lower troposphere. These findings support that compared to the original DI method, ARH could improve initial conditions, resulting in more accurate TC forecasts. Furthermore, it may argue the necessity and urgency of regular aircraft surveillance of TCs in the western North Pacific area.

Combining Monte Carlo and Ensemble Probabilities in Tropical Cyclone Forecasts near Landfall

Combining Monte Carlo and Ensemble Probabilities in Tropical Cyclone Forecasts near Landfall

Evaluation of Global Wave Probabilities Consistent with Official Forecasts

AbstractThe U.S. Navy is keenly interested in analyses and predictions of waves at sea due to their effects on important tasks such as shipping, base preparedness and disaster relief. U.S. Tropical Cyclone (TC) Forecast Centers routinely disseminate wind probabilities consistent with official TC forecasts worldwide, but do not do the same for wave forecasts. These probabilities are especially important at longer leads where TC forecast accuracy diminishes. This work describes global wave probabilities consistent with both the official TC forecasts and their wind probabilities. Real-time runs for 84 TCs between May 2018 and March 2019, with probabilities generated for 12-ft and 18-ft significant wave heights are used to calculate verification statistics. This results in 347, 319, 261, 214, 155, and 112 verification cases at lead times of 1, 2, 3, 4, and 5 days where each verification case consists of a 20x20 degree latitude longitude grid around the verifying TC position. When compared with wave probabilities generated solely by a global numerical weather prediction model, the wind probability-based algorithm demonstrates improved consistency with official forecasts and provides additional benefits. Those benefits include an improved capability to discriminate between 12-ft and 18-ft significant wave events and non-events. The verification statistics also shows that the wind probability-based algorithm has a consistent high bias. How these biases can be reduced in future efforts is also discussed.

A Comparison of HWRF Six-Hourly 4DEnVar and Hourly 3DEnVar Assimilation of Inner Core Tail Dopper Radar Observations for the Prediction of Hurricane Edouard (2014)

Six-hourly three-dimensional ensemble variational (3DEnVar) (6H-3DEnVar) data assimilation (DA) assumes constant background error covariance (BEC) during a six-hour DA window and is, therefore, unable to account for temporal evolution of the BEC. This study evaluates the one-hourly 3DEnVar (1H-3DEnVar) and six-hourly 4DEnVar (6H-4DEnVar) DA methods for the analyses and forecasts of hurricanes with rapidly evolving BEC. Both methods account for evolving BEC in a hybrid EnVar DA system. In order to compare these methods, experiments are conducted by assimilating inner core Tail Doppler Radar (TDR) wind for Hurricane Edouard (2014) and by running the Hurricane Weather Research and Forecasting (HWRF) model. In most metrics, 1H-3DEnVar and 6H-4DEnVar analyses and forecasts verify better than 6H-3DEnVar. 6H-4DEnVar produces better thermodynamic analyses than 1H-3DEnVar. Radar reflectivity shows that 1H-3DEnVar produces better structure forecasts. For the first 24–48 h of the intensity forecast, 6H-4DEnVar forecast performs better than 1H-3DEnVar verified against the best track. Degraded 1H-3DEnVar forecasts are found to be associated with background storm center location error as a result of underdispersive ensemble storm center spread. Removing location error in the background improves intensity forecasts of 1H-3DEnVar.

Influence of water vapor distribution on the simulated track of Typhoon Hato (2017)

Predicting tropical cyclone (TCs) tracks is a primary concern in TC forecasting. Some TCs appear to move in a direction favorable for their development, beyond the influence of the steering flow. Thus, we hypothesize that TCs move toward regions with high water-vapor content in the lower atmosphere. In this study, four numerical experiments, including a control experiment and three sensitivity experiments, were performed using the Weather Research and Forecasting Model, to analyze the relationship between water vapor distribution and the track of Severe Typhoon Hato (2017). Observations validated the features reproduced in the control experiment. The sensitivity experiments were conducted to explore variations in the TC track under different water vapor environments. Results indicate that the horizontal distribution of water-vapor content exerted a greater impact on the TC track than the steering flow when both factors were significant. Further analysis revealed that the TC’s movement vector was between the direction of the steering flow and the direction toward the peak of vorticity increasing area. The peaks of vorticity increasing area were close to the peaks of water vapor increasing area, which also proved the effect of water vapor distribution on the TC track. These results are expected to improve TC track analysis and forecasting.

Ensemble generation for hurricane hazard assessment along the United States’ Atlantic coast

Atlantic tropical cyclones (TCs) affect the United States every year, and depending on their intensity, can cause coastal storm surge and inland inundation, leading to substantial damages in the east coast. Accurate TC risk assessment, however, is not easily quantified given the limited spatial and temporal historical records. This paper describes the development of a model framework for TC forecasts based on large ensemble generation. We illustrate the capability of the ensemble technique to improve TC forecasts by generating probabilistic forecasts of track and intensity. We show that the using this approach can generate tracks that are statistically similar to those of the underlying historical records, even TCs with long return periods. We began with historical compilations of the NOAA National Climatic Data Center (NCDC) tropical cyclone database. TCs reaching a hurricane strength and making landfall in or passing close to the United States were identified. The geographical area influenced by these hurricanes was discretized and the parameters of Markov chains and multivariate distributions were derived for each discretized area. Synthetic tracks were generated using repetitive random draws from the spatiotemporal distribution of historical genesis and storm motion, conditioned by Markov chains for each 6-hour displacement. Kolmogorov–Smirnov (K–S) tests revealed that the ensemble members and ’best track’ data are drawn from the same underlying population at the 95% significance level, with no test statistics exceeding the critical value of 0.13. The proposed algorithm is validated in both macro and micro scales. The results revealed that the general pattern of hurricane activities conforms well to the historical observations with high spatial correlations, over 90% for the Saffir–Simpson hurricane categories of 1 through 4 and near 80% for the category 5. The track generator also produces a history of potential wind and translational speeds for both of these scales.