Tropical Cyclone Rapid Intensification Research Articles

Rapid intensification of tropical cyclones in the Gulf of Mexico is more likely during marine heatwaves

Tropical cyclones can rapidly intensify under favorable oceanic and atmospheric conditions. This phenomenon is complex and difficult to predict, making it a serious challenge for coastal communities. A key contributing factor to the intensification process is the presence of prolonged high sea surface temperatures, also known as marine heatwaves. However, the extent to which marine heatwaves contribute to the potential of rapid intensification events is not yet fully explored. Here, we conduct a probabilistic analysis to assess how the likelihood of rapid intensification changes during marine heatwaves in the Gulf of Mexico and northwestern Caribbean Sea. Approximately 70% of hurricanes that formed between 1950 and 2022 were influenced by marine heatwaves. Notably, rapid intensification is, on average, 50% more likely during marine heatwaves. As marine heatwaves are on the increase due to climate change, our findings indicate that more frequent rapid intensification events can be expected in the warming climate.

Diversity of Tropical Cyclones Rapid Intensification

AbstractThe study investigates the rapid intensification (RI) of tropical cyclones (TCs) in the Northwestern Pacific. We found that rapid changes in the maximum wind speed (Vmax) and the minimum central pressure (Pmin) are not always concurrent. RI cases can be categorized into three types: (a) RIv, only Vmax strengthens rapidly; (b) RIp, only Pmin decreases rapidly; (c) RIpv, rapid changes in Vmax and Pmin occur concurrently. At the onset of RI, RIv‐type TCs exhibit the weakest intensity and the smallest size, with deep convection concentrated in the inner‐core region; RIp‐type TCs are characterized by the strongest cyclone intensity and the largest outer‐core size, with strong convection covering the inner‐ and outer‐core regions; RIpv‐type TCs show moderate intensity, size, and convection distribution. For RIpv, significant strengthening of wind profile is concentrated in the inner‐core region, while for RIp it is more prominent in the outer‐core.

On the Duration of Tropical Cyclone Rapid Intensification

AbstractRapid intensification (RI) of tropical cyclones (TCs) not only plays a crucial role in the development of major TCs but also poses great challenges to operational forecasting. Previous studies predominantly focused on RI over 24‐hr periods, overlooking the potential for extended durations with intermittent moments. Here, we investigate the actual duration of RI and show that, when considering the interruption and intermittent moments, the longest RI can persist for up to 90 hr. Declines and resurgences in maximum potential intensity (MPI) and tropical cyclone heat potential (TCHP) are associated with the interruption of RI process. Given that the error in TC track prediction is much lower compared with that in intensity prediction, such a local minimum in MPI and TCHP could be better forecasted and potentially assist the prediction of RI, ultimately reducing TC‐related hazards.

The Effects of Upper-Ocean Sea Temperatures and Salinity on the Intensity Change of Tropical Cyclones over the Western North Pacific and the South China Sea: An Observational Study

With increasing air and sea temperatures, the thermodynamic environments over the oceans are becoming more favourable for the development of intense tropical cyclones (TCs) with rapid intensification (RI). The South China coastal region consists of highly densely populated cities, especially over the Pearl River Delta (PRD) region. Intense TCs maintaining their strength or the RI of TCs close to the coastal region can present substantial forecasting challenges and have significant potential impacts on the coastal population. This study investigates the effect of sea-surface and sub-surface temperatures and salinity on the intensification of five TCs, namely Super Typhoon Hato in 2017, Super Typhoon Mangkhut in 2018, and Typhoon Talim, Super Typhoon Saola, and Severe Typhoon Koinu in 2023, which have significantly affected the South China coastal region and triggered high TC warning signals in Hong Kong in the past few years. This analysis utilised the Hong Kong Observatory’s TC best-track and intensity data, along with sea temperature and salinity profiles generated using the China Ocean ReAnalysis version 2 (CORA2) product from the National Marine Data and Information Service of China. It was found that high sea-surface temperatures (SST) of 30 °C or above for a depth of about 20 m, low sea-surface salinity (SSS) levels of 33.8 psu or below for a depth of at least 20 m, and strong salinity stratification of at least 0.6 psu per 100 m depth might offer useful hints for predicting the RI of TCs over the western North Pacific and the South China Sea (SCS) in operational forecasting, while noting other contributing environmental factors and synoptic flow patterns conducive to RI. This study represents the first documentation of sub-surface salinity’s impact on some intense TCs traversing the SCS during 2017–2023 based on an observational study. Our aim is to supplement operational techniques for forecasting RI with some quantitative guidance based on upper-level ocean observations of temperatures and salinity, on top of well-known but more rapidly changing dynamical factors like low-level convergence, weak vertical wind shear, and upper-level divergent outflow, as forecasted with numerical weather prediction models. This study will also encourage further research to refine the analysis of quantitative contributions from different RI factors and the identification of essential features for developing AI models as one way to improve the forecasting of TC RI before the TC makes landfall near the PRD, with due consideration given to the effect of freshwater river discharge from the Pearl River.

The Impact of High-Density Airborne Observations and Atmospheric Motion Vector Observation Assimilation on the Prediction of Rapid Intensification of Hurricane Matthew (2016)

Tropical cyclone rapid intensification (RI) prediction still remains a big international challenge in numerical weather prediction. Hurricane Matthew (2016) underwent extreme and non-classic RI, intensifying from a Category 1 storm to a Category 5 hurricane within 24 h under a strong vertical shear environment. However, most models failed to capture this RI, and limited or no inner core, and outflow observations were assimilated in the NWS operational HWRF Model before the onset of RI for Matthew (2016). The goals of the study are to (1) explore the best way to assimilate the High-Density Observations (HDOB, including FL and SFMR) and AMV data; (2) study the impact of assimilating these observations on the analysis of both the inner-core and outflow structures; and (3) examine the impact of assimilating these data on the prediction of RI for Matthew. The main results are as follows: (1) With proper pre-processing of the HDOB observations and by using a 4DEnVar method, the inner-core structure analysis was improved. And the RI prediction is more consistent with the best track without spin-down for the first 24 h. Assimilating CIMMS AMV observations on top of the HDOB observations further improves both the track and intensity forecasts. Specifically, both the magnitude and timing of the peak intensity are further improved. (2) Diagnostics are conducted to understand how the assimilation of these different types of observations impacts RI prediction. Without assimilating HODB and AMV data, baseline experimentover-predict the intensification rate during the first 18 h, but under-predict RI after 18 h. However, the assimilation of FL and SFMR and CIMMS AMV correctly weakens the upper-level outflow and improves the shear-relative structure of the inner-core vortex, such as reducing the low-level moisture in the downshear left quadrant. The deep convection on the downshear side is weaker than baseline for the first 18 h but keeps enhancing, later moving cyclonically to the USL quadrant, and then causes more subsidence warming, maximizing in the USL quadrant and the maximum wind increases faster. Moreover, the rapid intensification rate is much more consistent with the best track and the forecast skill of RI is improved. Therefore, 4DEnVar assimilation with proper pre-processing of the high-density observations can indeed correct the shear-relative moisture and structural distributions of both the inner core and environment for TCs imbedded in the stronger shear, which is important for shear-TC RI prediction.

Increasing Flood Hazard Posed by Tropical Cyclone Rapid Intensification in a Changing Climate

AbstractTropical cyclones (TCs) that undergo rapid intensification (RI) before landfall are notoriously difficult to predict and have caused tremendous damage to coastal regions in the United States. Using downscaled synthetic TCs and physics‐based models for storm tide and rain, we investigate the hazards posed by TCs that rapidly intensify before landfall under both historical and future mid‐emissions climate scenarios. In the downscaled synthetic data, the percentage of TCs experiencing RI is estimated to rise across a significant portion of the North Atlantic basin. Notably, future climate warming causes large increases in the probability of RI within 24 hr of landfall. Also, our analysis shows that RI events induce notably higher rainfall hazard levels than non‐RI events with equivalent TC intensities. As a result, RI events dominate increases in 100‐year rainfall and storm tide levels under climate change for most of the US coastline.

Alignment of Track Oscillations during Tropical Cyclone Rapid Intensification

Alignment of Track Oscillations during Tropical Cyclone Rapid Intensification

Predicting rapid intensification of tropical cyclones in the western North Pacific: a machine learning and net energy gain rate approach

In this study, a machine learning (ML)-based Tropical Cyclones (TCs) Rapid Intensification (RI) prediction model has been developed by using the Net Energy Gain Rate Index (NGR). This index realistically captures the energy exchanges between the ocean and the atmosphere during the intensification of TCs. It does so by incorporating the thermal conditions of the upper ocean and using an accurate parameterization for sea surface roughness. To evaluate the effectiveness of NGR in enhancing prediction accuracy, five distinct ML algorithms were utilized: Decision Tree, Logistic Regression, Support Vector Machine, K-Nearest Neighbors, and Feed-forward Neural Network. Two sets of experiments were performed for each algorithm. The first set used only traditional predictors, while the second set incorporated NGR. The outcomes revealed that models trained with the inclusion of NGR exhibited superior performance compared to those that only used traditional predictors. Additionally, an ensemble model was developed by utilizing a hard-voting method, combining the predictions of all five individual algorithms. This ensemble approach showed a noteworthy improvement of approximately 10% in the skill score of RI prediction when NGR was included. The findings of this study emphasize the potential of NGR in refining TC intensity prediction and underline the effectiveness of ensemble ML models in RI event detection.

An assessment of model capability on rapid intensification prediction of tropical cyclones in the South China Sea

The absence of robust quantitative evaluation methods has led to insufficient knowledge of models capability on the rapid intensification (RI) prediction of tropical cyclones (TCs). In this study, we propose a method and define some indicators aiming to evaluate model capability on predicting RI in a more accurate manner. An assessment of model predictive capability on RI of TCs based on 10 years of operational forecasts has been conducted using different RI criteria. The Tropical Regional Atmosphere Model for the South China Sea of China Meteorological Administration (CMA-TRAMS) and the European Centre for Medium-Range Weather Forecasts (ECMWF) high resolution (HRES) operational forecasts were used. Analysis results revealed that the criterion of 6-hour sea level pressure (SLP) change is more appropriate to be used in RI operational forecast. The maximum lead time (MLT) of CMA-TRAMS and HRES was 72 and 78 h, and the maximum deviation of RI occurrence time of CMA-TRAMS and HRES was 48 h delay and 24 h ahead, respectively. Overall results suggest that the model predictive capability of RI is currently limited, and both models have inadequate capability in providing sufficient heat and energy to support RI in the long run. A tendency of CMA-TRAMS to have a lag in RI occurrence time was also demonstrated due to an air-sea interaction lag resulting from the fixed skin sea surface temperature used. Results of the present study provide insights and could be the basis for future efforts on improving parametrization schemes for properly describing RI process of TCs.

Predicting Tropical Cyclone Rapid Intensification in Western North Pacific Basin using a TDA-RI Model from Digital Typhoon Dataset

Predicting Tropical Cyclone Rapid Intensification in Western North Pacific Basin using a TDA-RI Model from Digital Typhoon Dataset

Predicting Short-Term Intensity Change in Tropical Cyclones Using a Convolutional Neural Network

Abstract This study details a two-method, machine learning approach to predict current and short-term intensity change in global tropical cyclones (TCs), “D-MINT” and “D-PRINT.” D-MINT and D-PRINT use infrared imagery and environmental scalar predictors, while D-MINT also employs microwave imagery. Results show that current TC intensity estimates from D-MINT and D-PRINT are more skillful than three established intensity estimation methods routinely used by operational forecasters for North Atlantic and eastern and western North Pacific TCs. Short-term intensity predictions are validated against five operational deterministic guidances at 6-, 12-, 18-, and 24-h lead times. D-MINT and D-PRINT are less skillful than NHC and consensus TC intensity predictions in North Atlantic and eastern North Pacific TCs, but are more skillful than the other guidances for at least half of the lead times. In western North Pacific, north Indian Ocean, and Southern Hemisphere TCs, D-MINT is more skillful than the JTWC and other individual TC intensity forecasts for over half of the lead times. When probabilistically predicting TC rapid intensification (RI), D-MINT is more skillful in North Atlantic and western North Pacific TCs than three operationally used RI guidances, but less skillful for yes–no RI forecasts. In addition, this work demonstrates the importance of microwave imagery, as D-MINT is more skillful than D-PRINT. Since D-MINT and D-PRINT are convolutional neural network models interrogating two-dimensional structures within TC satellite imagery, this study also demonstrates that those features can yield better short-term predictions than existing scalar statistics of satellite imagery in operational models. Finally, a diagnostics tool is revealed to aid the attribution of the D-MINT/D-PRINT intensity predictions. Significance Statement This study develops a method to predict current and short-term forecasts of tropical cyclone (TC) intensity using artificial intelligence. The resultant models use a convolutional neural network (CNN) that can identify two-dimensional features in satellite imagery that are indicative of TC intensity and future intensity change. The performance results indicate that in several TC basins, the CNN approach is generally more skillful than alternative satellite-based estimates of TC intensity as well as operational short-term forecasts of deterministic intensity change and of similar skill to probabilistic rapid intensification forecasts.

Modulation of tropical cyclone rapid intensification by mesoscale asymmetries

AbstractComputer model simulations are one of the most important tools in current use for understanding tropical cyclone (TC) formation and rapid intensification (RI). These include “idealized” simulations in which a TC‐like vortex is placed in a hypothetical environment with predefined sea surface temperature and vertical profiles of temperature, humidity, and wind that are either constant or slowly varying across a large domain. The vast majority of such simulations begin with a perfectly circular vortex as the precursor to a TC. However, most real TCs form or intensify while interacting with asymmetric wind fields either within or external to the vortex circulation. This study introduces a method to initialize idealized TC simulations with asymmetries, and investigates the extent to which such asymmetries might delay RI in favorable environments. It is shown that mesoscale asymmetries can delay RI and reduce the fastest rates of intensification, and that these effects are statistically significantly increased when relatively low values of vertical shear of the horizontal wind are present. In some cases the asymmetries tilt the vortex directly through advection. In other cases, the wind asymmetries increase the disorganization of the convection or increase the size of the inner‐core wind field, and thus make the weaker TC more susceptible to environmental wind shear. The results suggest that mesoscale asymmetries of the wind field could be useful predictors for delay of RI in otherwise favorable environments.

Predictability of Tropical Cyclone Rapid Intensification based on Statistical Approach

Predictability of Tropical Cyclone Rapid Intensification based on Statistical Approach

Marathon versus Sprint: Two Modes of Tropical Cyclone Rapid Intensification in a Global Convection-Permitting Simulation

Abstract Tropical cyclones that intensify abruptly experience “rapid intensification.” Rapid intensification remains a formidable forecast challenge, in part because the underlying science has not been settled. One way to reconcile the debates and inconsistencies in the literature is to presume that different forms (or modes) of rapid intensification exist. The present study provides evidence in support of this hypothesis by documenting two modes of rapid intensification in a global convection-permitting simulation and the HURDAT2 database. The “marathon mode” is characterized by a moderately paced and long-lived intensification period, whereas the “sprint mode” is characterized by explosive and short-lived intensification bursts. Differences between the modes were also found in initial vortex structure (well defined versus poorly defined), nature of intensification (symmetric versus asymmetric), and environmental conditions (weak shear versus strong shear). Collectively, these differences indicate that the two modes involve distinct intensification mechanisms. Recognizing the existence of multiple intensification modes may help to better understand and predict rapid intensification by, for example, explaining the lack of consensus in the literature, or by raising awareness that rapid intensification in strongly sheared cyclones is not just an exception to a rule, but a typical process. Significance Statement Hurricanes are serious threats to society—in particular those that suddenly and quickly intensify before striking land. Forecasting these “rapid intensification” events is a challenge, in part because we do not fully understand the science behind rapid intensification. This study furthers our understanding of hurricane rapid intensification by documenting that rapid intensification comes in different types. Specifically, we show that one type of rapid intensification happens under conditions that meteorologists have thought would lessen the chances of intensification. Awareness of such a type of rapid intensification could lead to better predictions of hurricane intensity because forecasters are more cognizant of this type of event and the conditions in which they occur.

Recent global increase in multiple rapid intensification of tropical cyclones

The rapid intensification (RI ≥ 30 knots/24-h) of tropical cyclones (TCs) makes TC forecasting difficult, resulting in severe damage to property and life. Forecasting can get even worse if TCs have experienced RI more than once (referred to as "multiple-RI TCs") in their lifetime. On a global scale, the relation between long-term trends of multiple-RI TC frequency and maximum potential intensity (MPI) changes is investigated in this study. During 1981–2020, the frequency of multiple-RI TCs significantly increased at a rate of 1.2 TCs/decade, which was primarily due to the upper phases of TC becoming conducive to RI as MPI increased. Our analysis shows that the frequency of multiple-RI TCs has increased by 82.43% in 2000–2020 compared to that in 1981–2000, whereas the frequency of single RI TCs has increased by only 1.63%. The rise in MPI elevates the initial intensity at which a TC undergoes maximum intensification rate, making post-Tropical Storm stages of TCs conducive to RI. As a result, TCs can undergo RI multiple times even following a weakening before the intensity approaches MPI.

Revisiting the Relationship Between Tropical Cyclone Rapid Intensification and the Distribution of Inner‐Core Precipitation

AbstractThe relationship between tropical cyclone (TC) rapid intensification (RI) and the distribution of inner‐core precipitation preceding RI is reexamined using satellite‐based precipitation measurements and best‐track data. TCs experiencing RI (RITCs) are classified into weakly, moderately and highly asymmetric categories according to the 25th and 75th percentiles of an objective asymmetry index. Composite results indicate that weakly‐to‐moderately asymmetric RITCs experience precipitation axisymmetrization before RI, resulting from increased coverage of both light‐to‐moderate (0.5–10 mm hr−1) and intense rainfall (>10 mm hr−1) upshear. Contrarily, the rainfall asymmetry strengthens significantly before RI in highly asymmetric RITCs due to an outbreak of intense convection downshear left, demonstrating climatologically that RI is not necessarily preceded by rainfall axisymmetrization. It is hypothesized that the different rainfall evolution characteristics may be explained by the balanced intensification theory, and the azimuthal‐mean precipitation rate normalized by TC intensity could be a predictor for RI regardless of the rainfall asymmetry.

Recent increases in tropical cyclone rapid intensification events in global offshore regions

Rapid intensification (RI) is an essential process in the development of strong tropical cyclones and a major challenge in prediction. RI in offshore regions is more threatening to coastal populations and economies. Although much effort has been devoted to studying basin-wide temporal-spatial fluctuations, variations of global RI events in offshore regions remain uncertain. Here, we show that compared with open oceans, where the annual RI counts do not show significant changes, offshore areas within 400 km of the coastline have experienced a significant increase in RI events, with the count tripling from 1980 to 2020. Furthermore, thermodynamic environments present more favorable conditions for this trend, and climate models show that global ocean warming has enhanced such changes. This work yields an important finding that an increasing threat of RI in coastal regions has occurred in the preceding decades, which may continue under a future warming climate.

Onset of Tropical Cyclone Rapid Intensification: Evaluating the Response to Length Scales of Sea Surface Temperature Anomalies

Abstract Predicting the rapid intensification (>15.0 m s−1 increase in 10 m wind speed over 24 h or less) of tropical cyclones (TC) remains a challenge in the broader context of numerical weather prediction largely due to their multiscale dynamics. Ocean observations show that the size and magnitude of sea surface temperature (SST) anomalies associated with cold wakes and ocean eddies play important roles in TC dynamics. In this study, a combination of spectral and structure function analyses is utilized to generate realistic realizations of multiscale anomalies characteristic of the SST conditions in which Hurricane Irma (2017) underwent rapid intensification (RI). We investigate the impact of the length scale of these SST anomalies and the role of translation speed on the variance in RI onset timing. Length-scale-induced convective asymmetries, in addition to the mean magnitude of SST anomalies beneath the storm eye, are shown to modulate the variance in RI onset timing. The size of the associated SST length scales relative to the storm size is critical to the magnitude of variance in RI onset timing, as smaller length scales are shown to lack the spatial extent required to induce preferential convective asymmetries. Storm translation speed is also shown to influence the variance in RI onset timing for larger-length-scale ensembles by altering the exposure time of the eye to these SST anomalies. We find that an interplay between SST-induced convective asymmetries, the magnitude of SST anomalies underneath the eye/eyewall, and storm translation speed play crucial roles in modulating the variance in RI onset timing. Significance Statement The characteristics of sea surface temperature (SST) anomalies in the tropical cyclone near-environment are inherently multiscale in nature as a result of interactions between various dynamical processes in the ocean. Assuming a uniform SST beneath storms in numerical simulations limits the predictability of how air–sea interaction affects the physics of rapid intensification (RI). In this study, the influence of realistic multiscale SST anomalies on RI onset timing is investigated. Our results suggest that the length scale of SST anomalies (in addition to its magnitude) modulate the distribution of convection, creating asymmetries around the RMW that can influence the predictability of RI onset. This effect is further modulated by storm translation speed, with the most prominent impact seen in slow-moving storms.

Lightning-Based Tropical Cyclone Rapid Intensification Guidance

Abstract With several seasons of Geostationary Lightning Mapper (GLM) data, this work revisits incorporating lightning observations into operational tropical cyclone rapid intensification guidance. GLM provides freely available, real-time lightning data over the central and eastern North Pacific and North Atlantic Oceans. A long-term lightning dataset is needed to use GLM in a statistical–dynamical operational application to capture the relationship between lightning and the rare occurrence of rapid intensification. This work uses the World Wide Lightning Location Network (WWLLN) dataset from 2005 to 2017 to develop lightning-based predictors for rapid intensification guidance models. The models mimic the operational Statistical Hurricane Intensity Prediction Scheme Rapid Intensification Index and Rapid Intensification Prediction Aid frameworks. The frameworks are averaged to form a consensus as a means to isolate the impact of the lightning predictors. Two configurations for lightning predictors are assessed: a spatial configuration with 0–100-km inner core and 200–300-km rainband area for the preceding 6-h predictors and a temporal configuration with an inner core only for the preceding 0–1, 0–6, and 6–12 h. When tested on the 2018–21 seasons, the temporal configuration adds skill primarily to the 12–48-h forecasts when compared to the no-lightning version and rapid intensification operational consensus. When WWLLN is replaced with GLM, minor changes to the prediction are observed suggesting that this approach is suitable for operational applications and provides a new baseline for tropical cyclone lightning-based rapid intensification aids. Significance Statement The forecasting of rare, yet critical, tropical cyclone rapid intensification events continues to be challenging. The current operational tools to anticipate rapid intensity changes use a combination of numerical weather prediction–derived environmental conditions and satellite-based cloud top temperature variations of deep convection. Here, we use freely available Geostationary Lightning Mapper data, which provide independent information about convection, in similar intensity guidance frameworks using temporal and spatial aspects of lightning variability. Our results show an improvement in short-term (12–48 h) rapid intensification forecasts by using temporal lightning information, and our investigation highlights that users of Geostationary Lightning Mapper lightning information should be cognizant of the influence and impact of land on these observations.

Decadal changes in rapid intensification of western North Pacific tropical cyclones modulated by the North Pacific Gyre Oscillation

This study investigates the modulation of western North Pacific (WNP) tropical cyclone rapid intensification (RI) by the North Pacific Gyre Oscillation (NPGO) on decadal timescales. There is a significant inverse relationship between basinwide RI numbers during July–November and the simultaneous NPGO index from 1970 to 2021. During the positive NPGO phase, suppressed RI occurs over almost the entire WNP, with a distinctly different spatial distribution compared to the Pacific Decadal Oscillation-driven pattern of RI modulation. RI occurrence is significantly reduced over the eastern Philippine Sea (10°–25°N, 140°–155°E). This is primarily caused by enhanced negative low-level vorticity, which can be linked to the horizontal extent of the anomalous low-level anticyclone. By comparison, over the western Philippine Sea (10°–25°N, 125°–140°E), there are only weak RI occurrence changes due to offsetting influences of increased mid-level humidity and decreased low-level vorticity.