Maximum Sustained Surface Wind Speed Research Articles

The impact of spume droplets induced by the bag-breakup mechanism on tropical cyclone modeling

Spume, large-radius seawater droplets that are ejected from the ocean into the atmosphere, can exchange moisture and heat fluxes with the surrounding air. Under severe weather conditions, spume can substantially mediate air-sea fluxes through thermal effects and thus needs to be physically parameterized. While the impact made by spume on air-sea interactions has been considered in bulk turbulent air-sea algorithms, various hypotheses in current models have resulted in uncertainties remaining regarding the effect of spume on air-sea coupling. In this study, we extended a classic bulk turbulent air-sea algorithm with a “bag-breakup” physical scheme of spume generation parameterizations to include spume effects in a complicated numerical model. To investigate the impact of spume on air-sea coupling, we conducted numerical experiments in a simulation of Tropical Cyclone Narelle. We observed a significant improvement in the ability to model minimum central pressure and maximum sustained surface wind speed when including the bag-breakup spume scheme. In particular, the impact of the bag breakup–generated spume is observed in the intensity, structure, and size of the tropical cyclone system through the modulation of local wind speed (U10), wave height (Hs), and sea surface temperature.

Revisiting the Definition of Rapid Intensification of Tropical Cyclones by Clustering the Initial Intensity and Inner‐Core Size

AbstractRapid intensification (RI) of tropical cyclones (TCs) provides a great challenge in operational forecasting and contributes significantly to the development of major TCs. RI is commonly defined as an increase in the maximum sustained surface wind speed of at least a certain threshold within 24 hr. The most widely used threshold is 30 kt (15.4 m/s), which was determined statistically. Here we propose a new definition for RI by objectively clustering TCs using the intensification rate, initial intensity, and radius of the maximum wind speed (RMW). A group of 770 samples is separated at a threshold of 45 kt (23.2 m/s). The threshold is 40 kt (20.6 m/s) for the western North Atlantic, where TC size measurements are more reliable. Monte Carlo experiments demonstrate that the proposed threshold is robust even considering the uncertainty in RMW of as high as 30 km. We show that, when a TC undergoes RI, its maximum wind speed is approximately 60 ± 15 kt (30.9 ± 7.7 m/s) and the RMW is 45 ± 20 km. The new threshold outperforms the conventional threshold of 30 kt/24 hr in describing the bimodal distribution of lifetime maximum intensity and explaining the annual count of Category 5 TCs. This new definition provides a more physically based threshold and describes a more reliable representation of extreme events. Although more comparisons are needed for operational application, it is likely to be desirable for case‐based process studies and could provide a more valuable metric for TC intensification classification and research.

Investigation of Machine Learning Using Satellite-Based Advanced Dvorak Technique Analysis Parameters to Estimate Tropical Cyclone Intensity

AbstractSeveral simple and computationally inexpensive machine learning models are explored that can use advanced Dvorak technique (ADT)-retrieved features of tropical cyclones (TCs) from satellite imagery to provide improved maximum sustained surface wind speed (MSW) estimates. ADT (version 9.0) TC analysis parameters and operational TC forecast center best track datasets from 2005 to 2016 are used to train and validate the various models over all TC basins globally and select the best among them. Two independent test sets of TC cases from 2017 to 2018 are used to evaluate the intensity estimates produced by the final selected model called the “artificial intelligence (AI)” enhanced advanced Dvorak technique (AiDT). The 2017–18 MSW results demonstrate a global RMSE of 7.7 and 8.2 kt (1 kt ≈ 0.51 m s−1), respectively. Basin-specific MSW RMSEs of 8.4, 6.8, 7.3, 8.0, and 7.5 kt were obtained with the 2017 dataset in the North Atlantic, east/central Pacific, northwest Pacific, South Pacific/south Indian, and north Indian Ocean basins, respectively, with MSW RMSE values of 8.9, 6.7, 7.1, 10.4, and 7.7 obtained with the 2018 dataset. These represent a 30% and 23% improvement over the corresponding ADT RMSE for the 2017–18 datasets, respectively, with the AiDT error reduction significant to 99% in both sets. The AiDT model represents a notable improvement over the ADT performance and also compares favorably to more computationally expensive and complex machine learning models that interrogate satellite images directly while still preserving the operational familiarity of the ADT.

On occurrence of rapid intensification and rainfall changes in tropical cyclones over the North Indian Ocean

AbstractThe present study examines the climatological characteristics and possible triggers of rapid intensification (RI) of tropical cyclones (TCs) during 1990–2019 over the North Indian Ocean (NIO). RI is defined as an increase in maximum sustained surface wind speed of 30 knots (15.4 m⋅s−1) or more in a 24 hr duration. In the NIO basin, the threshold of 24 hr intensity change represents the 93rd percentile using a 3‐min sustained wind, while, it is 95th percentile in the Atlantic basin, where 1‐min sustained wind is used. A total of 46 TCs (~38% of the total TCs) have exhibited RI at an average rate of 1–2 TCs year−1 over the NIO. A significant increase of RI‐TCs is seen from the year 2000 onwards over the region. The maximum RI‐TCs occurrence is found in the post‐monsoon season. The majority of TCs (~48%) undergo the RI phase within a 12–24 hr time during the depression stage. About ~35% (26%) of the TCs are retained in the RI phase for the duration of at least 24 hr (36 hr). Most of the RI‐TCs move northwestward (38%) and westward (31%) direction 6 hr before the RI onset with slow/normal translation speeds. During the RI phase, ~72% of TCs travels a distance of ~150–450 km. The TC inner‐core region receives heavy rainfall, and a ~ 3 mm⋅hr−1 increment is noticed 12 hr before the RI to the RI onset. Most of the RI‐TCs made landfall over eastern states of India showing the vulnerability of the regions. Composite analysis demonstrated that higher precipitable water (~55 mm), surface flux (500 W⋅m−2), cyclone heat potential (50–60 kJ⋅cm−2) in the moderate shear (6–8 m⋅s−1) environment favours the RI process. This study highlights the significance of RI‐TCs and triggering conditions for the RI onset over the NIO region.

Prediction of Landfall Intensity, Location, and Time of a Tropical Cyclone

The prediction of the intensity, location and time of the landfall of a tropical cyclone well advance in time and with high accuracy can reduce human and material loss immensely. In this article, we develop a Long Short-Term memory based Recurrent Neural network model to predict intensity (in terms of maximum sustained surface wind speed), location (latitude and longitude), and time (in hours after the observation period) of the landfall of a tropical cyclone which originates in the North Indian ocean. The model takes as input the best track data of cyclone consisting of its location, pressure, sea surface temperature, and intensity for certain hours (from 12 to 36 hours) anytime during the course of the cyclone as a time series and then provide predictions with high accuracy. For example, using 24 hours data of a cyclone anytime during its course, the model provides state-of-the-art results by predicting landfall intensity, time, latitude, and longitude with a mean absolute error of 4.24 knots, 4.5 hours, 0.24 degree, and 0.37 degree respectively, which resulted in a distance error of 51.7 kilometers from the landfall location. We further check the efficacy of the model on three recent devastating cyclones Bulbul, Fani, and Gaja, and achieved better results than the test dataset.

Prediction of tropical cyclone trajectories over the Northern Indian Ocean using COSMO

The present study investigates the performance of a regional numerical weather prediction model; namely, the Consortium for Small-scale Modelling (COSMO) in the prediction of the tropical cyclone (TC) trajectories for varying intensities of the storm. A total of 8 TCs formed over the Northern Indian Ocean from 2017 to 2019 are chosen for the evaluation of the COSMO model. The central pressure ( $$P_\mathrm{Central}$$ ), pressure drop ( $$\varDelta P$$ ), and maximum sustained surface wind speed (MSW) simulated by the COSMO model are validated against the concurrent observations from India Meteorological Department. The forecasted mean track errors are 95 km for a lead time of 24 h, whereas it was about 140 km for a lead time of 48 h. The mean initial positional error in identification of the storm was about 50 km. The intensity of a storm is underestimated in terms of $$\varDelta P$$ and MSW, especially for a lead time of 0–24 h, whereas the model shows a consistent overestimation for a lead time of more than 24 h. During the initial stage of a storm, when its intensity is categorized as a Deep Depression, we notice a maximum amount of uncertainty in the prediction of cyclone track. The COSMO model yields improved predictability of the tracks for storms categorized as Very Severe Cyclonic Storms. As the intensity of a storm increases from a Deep Depression to a Very Severe Cyclonic Storm, the track errors associated with model simulations tend to decrease. Results of the present study illustrate the predictability of TCs from COSMO in terms of the trajectory and intensity of the storm.

Understanding the characteristics of rapid intensity changes of Tropical Cyclones over North Indian Ocean

North Indian Ocean (NIO), which comprises of Bay of Bengal (BoB) and Arabian Sea (AS) basins, is one of the highly potential regions for Tropical Cyclones (TCs) in the world. Significant improvements have been achieved in the prediction of the movement of TCs, since the last decade. However, the prediction of sudden intensity changes becomes a challenging task for the research and operational meteorologists. Hence, the present study focuses on finding the climatological characteristics of such intensity changes over NIO regions. Rapid Intensification (RI) is defined as the 24-h maximum sustained surface wind speed rate equal to 30 knots (15.4 ms−1). The results suggest that the TCs formed over the NIO basin are both seasonal and basin sensitive. Since 2000, a significant trend is observed in RI TCs over the basin. At least one among three cyclones getting intensified is of RI category. More number of RI cases have been identified in the BoB basin than the AS. The post-monsoon season holds more RI and rapid decay cases, with 63% and 90% contribution. Most of the TCs are attaining RI onset in their initial stage. Further, India is receiving more landfalling RI TCs, followed by Bangladesh and Oman. The east coast of India, Tamil Nadu, and Andhra Pradesh are the most vulnerable states to these RI TCs. The cyclogenesis locations associated with RI cases hold higher moisture, and sea surface temperature as compared to the Non-RI cases.

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.

Impact of very severe cyclonic storm Phailin on shoreline change along South Odisha Coast

The present study makes a qualitative and quantitative assessment of inundation limit, the structural damage and shoreline change due to very severe cyclonic storm Phailin and also estimates the rate of recovery along different parts of south Odisha coast. At the time of landfall of Phailin along Odisha and adjoining Andhra Pradesh coast on October 12, 2013, maximum sustained surface wind speed reached up to 200–210 kmph gusting to 220 kmph with an estimated pressure drop 66 mbar at the center. Significant wave heights reached up to more than 7 m with a mean period range between 4 and 12 s from southeast direction during the event. Strong gale wind and high wave followed by massive rainfall brought irreparable damage to the coastal structures and shoreline along south Odisha. Real-time kinematics global positioning system and differential global positioning system Arcpad were used to monitor the shoreline change before and after Phailin and the inundation limit. The study indicates maximum inundation at southernmost part of Odisha coast (Ramayapatnam) followed by inside the Gopalpur Port area and minimum near Gopalpur port north. Shoreline change from pre- (September 2013) to post-storm period (October 2013) is landward all along south Odisha coast with maximum (48.7 m) near Rushikulya turtle nesting beach and minimum (12.6 m) near south of Gopalpur tourist beach. The recovery of the beach and dune areas assessed during October 2014 (after 1 year from the post-storm observation) is uneven. Percentage of recovery is maximum at south side of Gopalpur port, while recovery is minimum on north of the port.

Dependence of Tropical Cyclone Intensification Rate on Sea Surface Temperature, Storm Intensity, and Size in the Western North Pacific

Abstract This study extends the statistical analysis on the dependence of tropical cyclone (TC) intensification rate (IR) on sea surface temperature (SST), storm initial intensity (maximum sustained surface wind speed Vmax), and storm size, in terms of the radius of maximum wind (RMW), the radius of 34-kt (AR34; 1 kt = 0.51 m s−1) wind, and the outer-core wind skirt parameter DR34 (= AR34 − RMW), for North Atlantic TCs to western North Pacific (WNP) TCs during 1982–2015. Results show that the relationship between the TC maximum potential intensification rate (MPIR) and SST also exists in the WNP. TC IR depends strongly on TC intensity and structure, consistent with the findings for North Atlantic TCs. TC IR is positively (negatively) correlated with storm intensity when Vmax is below (above) 70 kt and negatively correlated with the RMW. Rapid intensification (RI) occurs only in a relatively narrow range of parameter space in storm intensity and both inner- and outer-core sizes, with the highest IR appearing for Vmax = 70 kt, RMW ≦ 40 km, AR34 = 150 km, and DR34 = 100 km. The highest frequency of occurrence of intensifying TCs occurs for Vmax ~ 40–60 kt, RMW ~ 20–60 km, AR34 = 200 km, and DR34 = 120 km. Overall, these values are very similar to those for TCs in the North Atlantic. These results suggest the need for the realistic initialization of TC structure in numerical models and the inclusion of size parameters in statistical TC intensity prediction schemes.

Weak Tropical Cyclones Dominate the Poleward Migration of the Annual Mean Location of Lifetime Maximum Intensity of Northwest Pacific Tropical Cyclones since 1980

The poleward migration of the annual mean location of tropical cyclone (TC) lifetime maximum intensity (LMI) has been identified in the major TC basins of the globe over the past 30 years, which is particularly robust over the western North Pacific (WNP). This study has revealed that this poleward migration consists mainly of weak TCs (with maximum sustained surface wind speed less than 33 m s−1) over the WNP. Results show that the location of LMI of weak TCs has migrated about 1° latitude poleward per decade since 1980, while such a trend is considerably smaller for intense TCs. This is found to be linked to a significant decreasing trend of TC genesis in the southern WNP and a significant increasing trend in the northwestern WNP over the past 30 years. It is shown that the greater sea surface temperature (SST) warming at higher latitudes associated with global warming and its associated changes in the large-scale circulation favor more TCs to form in the northern WNP and fewer but stronger TCs to form in the southern WNP.

Synoptic Flow Patterns and Large-Scale Characteristics Associated with Rapidly Intensifying Tropical Cyclones in the South China Sea

Abstract The typical synoptic flow patterns and environmental factors that favor the rapid intensification (RI) of tropical cyclones (TCs) in the South China Sea (SCS) have been identified based on all TCs formed in the SCS between 1981 and 2011. The quantity RI is defined as the 24-h increase in maximum sustained surface wind speed by 15 m s−1 as in previous studies, which is close to the 95th percentile of 24-h intensity change of all SCS samples excluding those after landfall. There are 4.9% (2.3%) of tropical depressions (tropical storms) that experienced RI. No typhoons satisfied the RI threshold. Six low-level synoptic flow patterns favoring RI have been identified based on 18 RI cases. In the monsoon season very few TCs experience RI due to large vertical wind shear (VWS). Most RI cases occurred in the postmonsoon season when the midlatitude troughs often penetrated into the SCS whereas the southwesterly monsoon flow is still strong in the southern SCS. Compared with those of non-RI cases, the mean initial conditions of RI cases include weak VWS and relatively strong forcing from midlatitude troughs. Several criteria of significant environmental factors for RI are statistically identified based on all TC samples. It is found that 16 non-RI TCs fitted in the RI flow patterns but only two of them satisfy all the criteria, suggesting that a combination of the synoptic flow pattern and the environmental factors can be used to predict RI in the SCS. In addition, two RI cases involving TC–trough interaction are analyzed.

The Influence of Tropical Cyclone Size on Its Intensification

Abstract This study investigates tropical cyclones of the past two decades (1990–2010) and the connection, if any, between their size and their ability to subsequently undergo rapid intensification (RI). Three different parameters are chosen to define the size of a tropical cyclone: radius of maximum wind (RMW), the average 34-knot (kt; 1 kt = 0.51 m s−1) radius (AR34), and the radius of the outermost closed isobar (ROCI). The data for this study, coming from the North Atlantic hurricane database second generation (HURDAT2), as well as the extended best-track dataset, are organized into 24-h intervals of either RI or slow intensification/constant intensity periods (non-RI periods). Each interval includes the intensity (maximum sustained surface wind speed), RMW, AR34, and ROCI at the beginning of the period and the change of intensity during the subsequent 24-h period. Results indicate that the ability to undergo RI shows significant sensitivity to initial size. Comparisons between RI and non-RI cyclones confirm that tropical cyclones that undergo RI are more likely to be smaller initially than those that do not. Analyses show that the RMW and AR34 have the strongest negative correlation with the change of intensity. Scatterplots imply there is a general maximum size threshold for RMW and AR34, above which RI is extremely rare. In contrast, the overall size of the tropical cyclones, as measured by ROCI, appears to have little to no relationship with subsequent intensification. The results of this work suggest that intensity forecasts and RI predictions in particular may be aided by the use of the initial size as measured by RMW and AR34.

Intensity forecast of tropical cyclones over North Indian Ocean using multilayer perceptron model: skill and performance verification

The coastal regions of India are profoundly affected by tropical cyclones during both pre- and post-monsoon seasons with enormous loss of life and property leading to natural disasters. The endeavour of the present study is to forecast the intensity of the tropical cyclones that prevail over Arabian Sea and Bay of Bengal of North Indian Ocean (NIO). A multilayer perceptron (MLP) model is developed for the purpose and compared the forecast through MLP model with other neural network and statistical models to assess the forecast skill and performances of MLP model. The central pressure, maximum sustained surface wind speed, pressure drop, total ozone column and sea surface temperature are taken to form the input matrix of the models. The target output is the intensity of the tropical cyclones as per the T—number. The result of the study reveals that the forecast error with MLP model is minimum (4.70 %) whereas the forecast error with radial basis function network (RBFN) is observed to be 14.62 %. The prediction with statistical multiple linear regression and ordinary linear regression are observed to be 9.15 and 9.8 %, respectively. The models provide the forecast beyond 72 h taking care of the change in intensity at every 3-h interval. The performance of MLP model is tested for severe and very severe cyclonic storms like Mala (2006), Sidr (2007), Nargis (2008), Aila (2009), Laila (2010) and Phet (2010). The forecast errors with MLP model for the said cyclones are also observed to be considerably less. Thus, MLP model in forecasting the intensity of tropical cyclones over NIOs may thus be considered to be an alternative of the conventional operational forecast models.

A western North Pacific tropical cyclone intensity prediction scheme

A western North Pacific tropical cyclone (TC) intensity prediction scheme (WIPS) is developed based on TC samples from 1996 to 2002 using the stepwise regression technique, with the western North Pacific divided into three sub-regions: the region near the coast of East China (ECR), the South China Sea region (SCR), and the far oceanic region (FOR). Only the TCs with maximum sustained surface wind speed greater than 17.2 m s−1 are used in the scheme. Potential predictors include the climatology and persistence factors, synoptic environmental conditions, potential intensity of a TC and proximity of a TC to land. Variances explained by the selected predictors suggest that the potential intensity of a TC and the proximity of a TC to land are significant in almost all the forecast equations. Other important predictors include vertical wind shear in ECR, 500-hPa geopotential height anomaly at the TC center, zonal component of TC translation speed in SCR, intensity change of TC 12 or 24 h prior to initial time, and the longitude of TC center in FOR.

Comparison of phytoplankton blooms triggered by two typhoons with different intensities and translation speeds in the South China Sea

Two phytoplankton blooms in the South China Sea (SCS), triggered by 2 typhoons with different intensities and translation speeds, were compared using remotely sensed chlorophyll a (chl a), sea surface temperature (SST), vector wind field, and best-track typhoon data. Typhoon Ling-Ling in 2001 was strong, with a maximum sustained surface wind speed of 59 m s(-1), and fast-moving with a mean translation speed of 4.52 m s(-1). Typhoon Kai-Tak in 2005 was weak with a maximum sustained surface wind speed of 46 m s(-1), and slow-moving with a mean translation speed of 2.87 m s(-1). The weak, slow-moving typhoon Kai-Tak induced phytoplankton blooms with higher chl a concentrations, while the strong, fast-moving typhoon Ling-Ling induced blooms over a larger area. On average, about 7 typhoons per year affect the SCS, among which 41% are strong (> 50 m s(-1)) and 59% are weak, while 64% are fast-moving (>4.4 m s(-1)) and 36% are slow-moving. We conservatively estimate that typhoon periods may account for 3.5% of the annual primary production in the oligotrophic SCS.

Tropical Cyclone Destructive Potential by Integrated Kinetic Energy

Tropical cyclone damage potential, as currently defined by the Saffir-Simpson scale and the maximum sustained surface wind speed in the storm, fails to consider the area impact of winds likely to force surge and waves or cause particular levels of damage. Integrated kinetic energy represents a framework that captures the physical process of ocean surface stress forcing waves and surge while also taking into account structural wind loading and the spatial coverage of the wind. Integrated kinetic energy was computed from gridded, objectively analyzed surface wind fields of 23 hurricanes representing large and small storms. A wind destructive potential rating was constructed by weighting wind speed threshold contributions to the integrated kinetic energy, based on observed damage in Hurricanes Andrew, Hugo, and Opal. A combined storm surge and wave destructive potential rating was assigned according to the integrated kinetic energy contributed by winds greater than tropical storm force. The ratings are based on the familiar 1–5 range, with continuous fits to allow for storms as weak as 0.1 or as strong as 5.99.

Large-Scale Characteristics of Rapidly Intensifying Tropical Cyclones in the North Atlantic Basin

The National Hurricane Center (NHC) and Statistical Hurricane Intensity Prediction Scheme (SHIPS) databases are employed to examine the large-scale characteristics of rapidly intensifying Atlantic basin tropical cyclones. In this study, rapid intensification (RI) is defined as approximately the 95th percentile of over-water 24-h intensity changes of Atlantic basin tropical cyclones that developed from 1989 to 2000. This equates to a maximum sustained surface wind speed increase of 15.4 m s−1 (30 kt) over a 24-h period. It is shown that 31% of all tropical cyclones, 60% of all hurricanes, 83% of all major hurricanes, and all category 4 and 5 hurricanes underwent RI at least once during their lifetimes. The mean initial (t = 0 h) conditions of cases that undergo RI are compared to those of the non-RI cases. These comparisons show that the RI cases form farther south and west and have a more westward component of motion than the non-RI cases. In addition, the RI cases are typically intensifying at a faster rate during the previous 12 h than the non-RI cases. The statistical analysis also shows that the RI cases are further from their maximum potential intensity and form in regions with warmer SSTs and higher lower-tropospheric relative humidity than the non-RI cases. The RI cases are also embedded in regions where the upper-level flow is more easterly and the vertical shear and upper-level forcing from troughs or cold lows is weaker than is observed for the non-RI cases. Finally, the RI cases tend to move with the flow within a higher layer of the atmosphere than the non-RI cases. A simple technique for estimating the probability of RI is described. Estimates of the probability of RI are determined using the predictors for which statistically significant differences are found between the RI and non-RI cases. Estimates of the probability of RI are also determined by combining the five predictors that had the highest individual probabilities of RI. The probability of RI increases from 1% to 41% when the total number of thresholds satisfied increases from zero to five. This simple technique was used in real time for the first time during the 2001 Atlantic hurricane season as part of the Joint Hurricane Testbed (JHT).