Tropical Cyclone Intensity Change Research Articles

Thermodynamic pathways of vertical wind shear impacting tropical cyclone intensity change: Energetics and Lagrangian analysis

Abstract This study analyzes the variations in the thermodynamic cycle and energy of a tropical cyclone (TC) under the influence of vertical wind shear (VWS), exploring the possible thermodynamic pathways through which VWS affects TC intensity. The maximum energy harnessed by the TC diminishes alongside a decrease in storm intensity in the presence of VWS. In the sheared TC, the ascending branch of the thermodynamic cycles of TC shifts toward lower entropy, which is related to the reduction of entropy in the eyewall and/or the increase of entropy and enhanced upward motion outside the eyewall. Moreover, the descending leg to shift toward higher entropy due to the increase in entropy and weakening of downward motion in both the ambient environment and upper troposphere. These changes in the ascending and descending branches could reduce the work done by the heat engine cycle, with the former playing a primary role in the presence of VWS. Given that the ascending branch is influenced by the eyewall and the rainbands outside the eyewall under VWS, the thermodynamic pathways could be categorized into inner ventilation and outer ventilation based on the location of their roles. The pathways associated with inner ventilation primarily reduce the entropy in the eyewall. In addition to the conventional low- and mid-level ventilation, the inner ventilation also encompasses new pathways entering the mid-level eyewall after descending from the upper level and ascending from the boundary layer. Conversely, the pathways of outer ventilation are related to the increase the entropy outside the eyewall. These include the ascent of high-entropy air to the middle and upper troposphere related to the inner and outer rainbands, the outward advection of high-entropy air from the eyewall in the mid- and upper-levels, and air warming by the descending draft from the upper to the mid-level troposphere. These insights contribute to a nuanced understanding of the sophisticated interactions within TCs and their response to VWS.

Improvement in Forecasting Short-Term Tropical Cyclone Intensity Change and Their Rapid Intensification Using Deep Learning

Abstract Forecasting the intensity of a tropical cyclone (TC) remains challenging, particularly when it undergoes rapid changes in intensity. This study aims to develop a convolutional neural network (CNN) for 24-h forecasts of the TC intensity changes and their rapid intensifications over the western Pacific. The CNN model, the DeepTC, is trained using a unique loss function, an amplitude focal loss, to better capture large intensity changes, such as those during rapid intensification (RI) events. We showed that the DeepTC outperforms operational forecasts, with a lower mean absolute error (8.9%–10.2%) and a higher coefficient of determination (31.7%–35%). In addition, the DeepTC exhibits a substantially better skill at capturing RI events than operational forecasts. To understand the superior performance of the DeepTC in RI forecasts, we conduct an occlusion sensitivity analysis to quantify the relative importance of each predictor. Results revealed that scalar quantities such as latitude, previous intensity change, initial intensity, and vertical wind shear play critical roles in successful RI prediction. Additionally, the DeepTC utilizes the three-dimensional distribution of relative humidity to distinguish RI cases from non-RI cases, with higher dry–moist moisture gradients in the mid-to-low troposphere and steeper radial moisture gradients in the upper troposphere showed during RI events. These relationships between the identified key variables and intensity change were successfully simulated by the DeepTC, implying that the relationship is physically reasonable. Our study demonstrates that the DeepTC can be a powerful tool for improving RI understanding and enhancing the reliability of TC intensity forecasts.

Trends in the ocean mixed‐layer depth in the western North Pacific tropical cyclone basin

AbstractThe upper ocean mixed‐layer depth (MLD) has been recognized as a crucial factor in understanding the impact of climate change on tropical cyclone (TC) intensity, while the specific MLD variations in the western North Pacific (WNP) basin have received comparatively little attention. Using two ocean reanalysis datasets and the output of 28 CMIP6 models, the climatological mean MLD and its trend in the TC active region are examined. Compared to the ORAS4 and EN4 datasets, the spatial pattern of the climatological mean MLD can be well simulated in the multi‐model ensemble (MME). The mean MLD decreases with increasing latitude and maintains the maximum in the equatorial region. The spatial patterns of MLD long‐term (1958–2017) and short‐term (1979–2017) trends are deepening in the TC active region, with the relatively large positive trends occurring to the southeastern WNP primarily between 5°N and 15°N. However, the simulated trend in the MME is dominant with shoaling MLD, and the shoaling of the MLD is also projected under the SSP5‐8.5 scenario. It is suggested that the bias on the simulated shoaling of the MLD in CMIP6 models may result from the overestimated enhancement of the upper‐ocean stratification. Thus, there is a discrepancy between the observed and simulated MLD trends in the WNP basin, which may have implications for TC intensity change under global warming scenarios.

The Role of the Peak Radius of Maximum Wind Contraction Rate Preceding the Peak Intensification Rate on Tropical Cyclone Lifetime Maximum Intensity

AbstractThe prevalence of the maximum radius of maximum wind (RMW) contraction rate for the tropical cyclone (TC) before the maximum TC intensification rate has been observed in several previous studies. However, it remains unclear whether and how the maximum RMW contraction rate preceding the maximum TC intensification rate affects the TC lifetime maximum intensity (LMI). In this study, tropical cyclones are grouped into three types depending on whether the peak RMW contraction rate precedes, occurs simultaneously with, or lags the time of the peak intensification rate in the North Atlantic and western North Pacific. Results indicate that when the maximum RMW contraction rate occurs before the maximum intensification rate, TCs are more likely to gain a greater LMI. TCs with the time of maximum RMW contraction rate preceding the time of the maximum intensification rate are more likely to achieve a small RMW and intermediate intensity after the maximum RMW contraction rate period, which is conducive to higher intensification rates. An intermediate initial intensity and higher maximum intensification rate both lead to a greater LMI. Environmental factors are also examined but are found to have little impact on the LMI for the three types of TCs. This work suggests that TCs with a higher LMI are often characterized by a notable maximum rate of RMW contraction before the peak intensification rate and helps to a better understanding of TC structure and intensity changes.

Short-Term Intensity Prediction of Tropical Cyclones Based on Multi-Source Data Fusion with Adaptive Weight Learning

Tropical cyclones (TCs) can cause significant economic damage and loss of life in coastal areas. Therefore, TC prediction has become a crucial topic in current research. In recent years, TC track prediction has progressed considerably, and intensity prediction remains a challenge due to the complex mechanism of TC structure. In this study, we propose a model for short-term intensity prediction based on adaptive weight learning (AWL-Net) for the evolution of the TC’s structure as well as intensity changes, exploring the multidimensional fusion of features including TC morphology, structure, and scale. Furthermore, in addition to using satellite imageries, we construct a dataset that can more comprehensively explore the degree of TC cloud organization and structure evolution. Considering the information difference between multi-source data, a multi-branch structure is constructed and adaptive weight learning (AWL) is designed. In addition, according to the three-dimensional dynamic features of TC, 3D Convolutional Gated Recurrent (3D ConvGRU) is used to achieve feature enhancement, and then 3D Convolutional Neural Network (CNN) is used to capture and learn TC temporal and spatial features. Experiments on a sample of northwest Pacific TCs and official agency TC intensity prediction records are used to validate the effectiveness of our proposed model, and the results show that our model is able to focus well on the spatial and temporal features associated with TC intensity changes, with a root mean square error (RMSE) of 10.62 kt for the TC 24 h intensity forecast.

A Climatology of Intensity Change and Translation Speed of Landfalling North American Tropical Cyclones between 1971 and 2020

Abstract In this study, we investigated the translation speed and intensity change characteristics for landfalling North American tropical cyclones (TCs) from 1971 to 2020. We calculated three variables—intensity change, mean translation speed, and translation speed change—prior to each TC landfall and investigated the climatology of these variables for seven coastal segments. We found that lower-latitude segments generally had greater positive intensity changes prior to landfall, and higher-latitude segments had greater translation speeds. Longitude primarily influenced translation speed changes, with landfalling TCs along the Atlantic coast of the United States notably accelerating prior to landfall. Temporal trends in each of these variables were inconsistent geographically, but most segments showed an increase in positive intensity changes over time, demonstrating the increasing likelihood of intensifying TCs before landfall in recent years. We defined extreme intensification and extreme weakening as the 90th and 10th percentile of all landfalling TC intensity changes, respectively. We found that extreme intensification and weakening have been increasing and decreasing in frequency, respectively. Results from this study can be used in a variety of future applications, including in operational forecasting and model production, and provide a baseline for climate attribution studies investigating extreme intensity change events. Significance Statement Landfalling tropical cyclones pose significant hazards to life and property in coastal regions across North America. This study develops a comprehensive climatology of intensity change and translation speed of tropical cyclones during the final 36 h prior to landfall. We found that incidences of extreme intensification and weakening of landfalling North American tropical cyclones have increased and decreased, respectively, since 1971. We also identify lower-latitude coastal segments tend to average faster translation speeds that higher-latitude segments, while segments on the east coast of the United States tended to average a greater acceleration. These results show the importance of looking at tropical cyclone characteristics regionally and provide a useful baseline to assess how tropical cyclone risk is changing in a warming climate.

Effects of Background Synoptic Environment in Controlling South China Sea Tropical Cyclone Intensity and Size Changes in Pseudo–Global Warming Experiments

Abstract Assessing how global warming affects tropical cyclones (TC) is immensely important for climate change adaptation and hazard mitigation. However, projected intensity and size change can vary greatly among different individual storms, even under the same forcing in pseudo–global warming (PGW) experiments. Here we hypothesize that these variations are related to the historical environment in which each TC was embedded. Twenty-five TCs in the South China Sea (SCS) region were simulated using the Weather Research and Forecasting (WRF) Model. Their changes in the near (2036–65) and far future (2075–99) following the representative concentration pathways 8.5 (RCP8.5) and 4.5 (RCP4.5) under phase 5 of the Coupled Model Intercomparison Project (CMIP5) were investigated by the PGW technique. The mean changes in TC intensity and gale-force wind radius (R17) in the SCS were +6.4% and +1.5% for a 2°C warming, respectively. Multiple linear regression and stepwise regression analysis revealed that storm intensity variations were positively correlated with historical sea surface temperature and negatively with outer (i.e., outside the TC’s R17) atmospheric instability, while the R17 variations correlated positively with outer midtropospheric relative humidity (RH) and surface outer wind speed (OWS). Ertel potential vorticity (EPV) diagnostics further showed a moister SCS background can cause stronger diabatic heating and EPV production at the spiral rainbands under PGW, which increases R17. Additionally, stronger background absolute angular momentum (AAM) promoted stronger AAM influx, leading to larger R17. Implications were drawn to explain the uncertainties in projected TC intensity and size due to natural variability. Significance Statement Changes in the intensity and size of individual tropical cyclones in future climates are highly variable. This study aims to understand the environmental factors influencing these variations. We selected 25 storms that entered the South China Sea region and modeled them in both present and future climates. The mean intensity and size were projected to increase by 6.4% and 1.5% for a 2°C warming, respectively. We found that tropical cyclones embedded in historically warmer oceans and more stable atmospheres intensified more than the others, while historically wetter and windier environments promoted larger size growth. Our results suggest that certain tropical cyclones can exhibit a greater increase in intensity and size in a warmer climate under favorable environmental conditions.

Influence of Spatial Heterogeneity in Sea Surface Temperature on Tropical Cyclone Intensity Over the Western North Pacific

AbstractThis study explores the impact of sea surface temperature (SST) spatial heterogeneity on tropical cyclone (TC) intensity through a combination of observations and simulations, aiming to provide a reference for further improving TC intensity forecasting skills. Two distinct patterns of SST spatial heterogeneity are identified based on a statistical analysis of observational data, when the SST at the TC center is above and below 29.3°C, respectively. One is a warm‐core pattern (WCP) with a warm peak SST at the TC center decreasing centrifugally which favors TC development, and the other one is a poleward‐decreasing pattern (PDP) with a warm SST at the south decreasing poleward which suppresses TC development. The numerical simulations confirm the opposite influence of the WCP and the PDP on TC intensity. The WCP intensifies TC intensity by strengthening TC secondary circulation, increasing the conversion from ocean heat energy to TC kinetic energy, and compacting TC structure. In contrast, the PDP weakens TC intensity by inducing opposing responses of these processes. The magnitude of TC intensity change caused by SST spatial heterogeneity is comparable to those caused by a 1°C change in SST at the TC center. These findings offer valuable insights into the role of SST spatial heterogeneity in TC development and provide a new perspective to improve TC intensity forecasting by incorporating SST spatial heterogeneity into statistical‐dynamic models.

Correction to: The co-variability of SST and vertical wind shear on the variability of tropical cyclone intensity change in the Northern Hemisphere

Correction to: The co-variability of SST and vertical wind shear on the variability of tropical cyclone intensity change in the Northern Hemisphere

Typhoon-Induced Extreme Sea Surface Temperature Drops in the Western North Pacific and the Impact of Extra Cooling Due to Precipitation

Sea surface temperature (SST) responses have been perceived as crucial to consequential tropical cyclone (TC) intensity development. In addition to regular cooling responses, a few TCs could cause extreme SST drops (ESSTDs) (e.g., SST drops more than 6 °C) during their passage. Given the extreme temperature differences and the consequentially marked air–sea flux modulations, ESSTDs are intuitively supposed to play a serious role in modifying TC intensities. Nevertheless, the relationship between ESSTDs and consequential storm intensity changes remains unclear. In this study, satellite-observed microwave SST drops and the International Best Track Archive for Climate Stewardship TC data from 2001 to 2021 were used to elucidate the relationship between ESSTDs and the consequential TC intensity changes in the Western North Pacific typhoon season (July–October). Subsequently, the distributed characteristics of ESSTDs were systematically examined based on statistical analyses. Among them, Typhoon Kilo (2015) triggered an unexpected ESSTD behind its passage, according to existing theories. Numerical experiments based on the Regional Ocean Modeling System were carried out to explore the possible mechanisms that resulted in the ESSTD due to Kilo. The results indicate that heavy rainfall leads to additional SST cooling through the enhanced sensible heat flux leaving the surface layer in addition to the cooling from momentum-driven vertical mixing. This process enhanced the sensible heat flux leaving the sea surface since the temperature of the raindrops could be much colder than the SST in the tropical ocean, specifically under heavy rainfall and relatively less momentum entering the upper ocean during Kilo.

An Observational Analysis of the Relationship between Tropical Cyclone Vortex Tilt, Precipitation Structure, and Intensity Change

Abstract This study uses a recently developed airborne Doppler radar database to explore how vortex misalignment is related to tropical cyclone (TC) precipitation structure and intensity change. It is found that for relatively weak TCs, defined here as storms with a peak 10-m wind of 65 kt (1 kt = 0.51 m s−1) or less, the magnitude of vortex tilt is closely linked to the rate of subsequent TC intensity change, especially over the next 12–36 h. In strong TCs, defined as storms with a peak 10-m wind greater than 65 kt, vortex tilt magnitude is only weakly correlated with TC intensity change. Based on these findings, this study focuses on how vortex tilt is related to TC precipitation structure and intensity change in weak TCs. To illustrate how the TC precipitation structure is related to the magnitude of vortex misalignment, weak TCs are divided into two groups: small-tilt and large-tilt TCs. In large-tilt TCs, storms display a relatively large radius of maximum wind, the precipitation structure is asymmetric, and convection occurs more frequently near the midtropospheric TC center than the lower-tropospheric TC center. Alternatively, small-tilt TCs exhibit a greater areal coverage of precipitation inward of a relatively small radius of maximum wind. Greater rates of TC intensification, including rapid intensification, are shown to occur preferentially for TCs with greater vertical alignment and storms in relatively favorable environments. Significance Statement Accurately predicting tropical cyclone (TC) intensity change is challenging. This is particularly true for storms that undergo rapid intensity changes. Recent numerical modeling studies have suggested that vortex vertical alignment commonly precedes the onset of rapid intensification; however, this consensus is not unanimous. Until now, there has not been a systematic observational analysis of the relationship between vortex misalignment and TC intensity change. This study addresses this gap using a recently developed airborne radar database. We show that the degree of vortex misalignment is a useful predictor for TC intensity change, but primarily for weak storms. In these cases, more aligned TCs exhibit precipitation patterns that favor greater intensification rates. Future work should explore the causes of changes in vortex alignment.

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.

Buffering Effect of Atmosphere–Ocean Coupling on Intensity Changes of Tropical Cyclones Under a Changing Climate

AbstractThe effect of atmosphere–ocean coupling on intensity changes of tropical cyclones (TCs) under global warming was examined using a regional high‐resolution three‐dimensional atmosphere–ocean coupled model. A storyline event attribution approach was applied to four historical intense TCs in the western North Pacific. The results indicate that atmosphere–ocean coupling buffers TC intensification as global warming progresses. This buffering effect increased as storms traveled northward. Moreover, the effect intensified as warming progressed, because reductions in sea surface temperature induced by the storm increased as the storm strengthened in future warmer climates. The magnitude of the buffering effect depended on the storm's size and translation speed; a large, slow‐moving storm had significant resilience against global warming, whereas a compact, fast‐moving storm was sensitive to global warming. A high‐resolution atmosphere–ocean coupled model is important for more reliable future projections of TC intensity under the changing climate.

The co-variability of SST and vertical wind shear on the variability of tropical cyclone intensity change in the Northern Hemisphere

The co-variability of SST and vertical wind shear on the variability of tropical cyclone intensity change in the Northern Hemisphere

Quantifying the Environmental Effects on Tropical Cyclone Intensity Change Using a Simple Dynamically Based Dynamical System Model

Abstract Accurate prediction of tropical cyclone (TC) intensity is quite challenging due to multiple competing processes among the TC internal dynamics and the environment. Most previous studies have evaluated the environmental effects on TC intensity change from both internal dynamics and external influence. This study quantifies the environmental effects on TC intensity change using a simple dynamically based dynamical system (DBDS) model recently developed. In this simple model, the environmental effects are uniquely represented by a ventilation parameter B, which can be expressed as multiplicative of individual ventilation parameters of the corresponding environmental effects. Their individual ventilation parameters imply their relative importance to the bulk environmental ventilation effect and thus to the TC intensity change. Six environmental factors known to affect TC intensity change are evaluated in the DBDS model using machine learning approaches with the best track data for TCs over the North Atlantic, central, eastern, and western North Pacific and the Statistical Hurricane Intensity Prediction Scheme (SHIPS) dataset during 1982–2021. Results show that the deep-layer vertical wind shear (VWS) is the dominant ventilation factor to reduce the intrinsic TC intensification rate or to drive the TC weakening, with its ventilation parameter ranging between 0.5 and 0.8 when environmental VWS between 200 and 850 hPa is larger than 8 m s−1. Other environmental factors are generally secondary, with their respective ventilation parameters over 0.8. An interesting result is the strong dependence of the environmental effects on the stage of TC development.

On the Distribution of Convective and Stratiform Precipitation in Tropical Cyclones from Airborne Doppler Radar and Its Relationship to Intensity Change and Environmental Wind Shear Direction

Abstract Airborne Doppler radar reflectivity data collected in hurricanes on the NOAA P-3 aircraft between 1997 and 2021 were parsed into different modes of precipitation: stratiform precipitation, shallow convection, moderate convection, and deep convection. Stratiform precipitation was the most frequent precipitation mode with 82.6% of all observed precipitation while deep convection was the most infrequent at 1.3%. When stratified by 12-h intensity change, intensifying TCs had a greater areal coverage of total convection in the eyewall compared to weakening and steady-state TCs. The largest difference in the azimuthal distributions in the precipitation modes was in deep convection, which was mostly confined to the downshear-left quadrant in weakening and steady-state hurricanes and more symmetrically distributed in intensifying hurricanes. For all intensity change categories, the most symmetrically distributed precipitation mode was stratiform rain. To build upon the results of a recent thermodynamic study, the precipitation data were recategorized for hurricanes experiencing deep-layer wind shear with either a northerly component or southerly component. Like intensifying storms, hurricanes that experienced northerly component shear had a more symmetric distribution of deep convection than southerly component shear storms, which had a distribution of deep convection that resembled weakening storms. The greatest difference in the precipitation distributions between the shear direction groups were in major hurricanes experiencing moderate (4.5–11 m s−1) wind shear values. Consistent with previous airborne radar studies, the results suggest that considering the distribution of deep convection and the thermodynamic distributions associated with differing environmental wind shear direction could aid TC intensity forecasts. Significance Statement This research investigates how the distribution of different types of precipitation are related to tropical cyclone (TC) intensity change. Even though deep convection—the tallest clouds—is the least frequent type of precipitation, it has the strongest relationship to intensity change with uniform distributions around the eyewall associated with intensification. Less significant relationships were noticed for shallower clouds and stratiform (lighter) rain. The study also analyzed how change in direction of the large-scale winds with height (wind shear) influences intensity change. When wind shear is northerly, there is a more symmetric distribution of deep convection compared to when wind shear is southerly. These relationships illustrate how wind shear direction influences TC convective structure and, in turn, TC intensity change.

Simulated Diurnal Pulses in Hurricane Dorian (2019)

Abstract Radially outward-propagating, diurnal pulses in tropical cyclones (TCs) are associated with TC intensity and structural changes. The pulses are observed to feature either cloud-top cooling or warming, so-called cooling pulses (CPs) or warming pulses (WPs), respectively, with CPs posing a greater risk for hazardous weather because they often assume characteristics of tropical squall lines. The current study evaluates the characteristics and origins of simulated CPs using various convection-permitting Weather Research and Forecasting (WRF) Model simulations of Hurricane Dorian (2019), which featured several CPs and WPs over the tropical Atlantic Ocean. CP evolution is tested against choice of microphysics parameterization, whereby the Thompson and Morrison schemes present distinct mechanisms for CP creation and propagation. Specifically, the Thompson CP is convectively coupled and propagates outward with a rainband within 100–300 km of the storm center. The Morrison CP is restricted to the cirrus canopy and propagates radially outward in the upper-level outflow layer, unassociated with any rainband, within 200–600 km of the storm center. The Thompson simulation better represents the observations of this particular event, but it is speculated that CPs in nature can resemble characteristics from either MP scheme. It is, therefore, necessary to evaluate pulses beyond just brightness temperature (e.g., reflectivity, rain rate), especially within simulations where full fields are available. Significance Statement Tropical cyclone size and structure are influenced by the time of day. Identifying and predicting such characteristics is critical for evaluating hazardous weather risk of storms close to land. While satellite observations are valuable for recognizing daily fluctuations of tropical cyclone clouds as seen from space, they do not reliably capture what occurs at the surface. To investigate the relationship between upper-level cloud oscillations and rainbands, this study analyzes simulations of a major hurricane along the coast of Florida. The results show that rainbands are not always tied to changes in cloud tops, suggesting multiple pathways toward the daily oscillation of upper-level tropical cyclone clouds.

A review of recent research progress on the effect of external influences on tropical cyclone intensity change

Over the past four years, significant research has advanced our understanding of how external factors influence tropical cyclone (TC) intensity changes. Research on air-sea interactions shows that increasing the moisture disequilibrium is a very effective way to increase surface heat fluxes and that ocean salinity-stratification plays a non-negligible part in TC intensity change. Vertical wind shear from the environment induces vortex misalignment, which controls the onset of significant TC intensification. Blocking due to upper-level outflow from TCs can reduce the magnitude of vertical wind shear, making for TC intensification. Enhanced TC-trough interactions are vital for rapid intensification in some TC cases because of strengthened warm air advection, but upper-level troughs are found to limit TC intensification in other cases due to dry midlevel air intrusions and increased shear. Aerosol effects on TCs can be divided into direct effects involving aerosol-radiation interactions and indirect effects involving aerosol-cloud interactions. The radiation absorption by the aerosols can change the temperature profile and affect outer rainbands through changes in stability and microphysics. Sea spray and sea salt aerosols are more important in the inner region, where the aerosols increase precipitation and latent heating, promoting more intensification. For landfalling TCs, the intensity decay is initially more sensitive to surface roughness than soil moisture, and the subsequent decay is mainly due to the rapid reduction in surface moisture fluxes. These new insights further sharpen our understanding of the mechanisms by which external factors influence TC intensity changes.

Editorial: Tropical cyclone intensity and structure changes: theories, observations, numerical modeling and forecasting

Editorial: Tropical cyclone intensity and structure changes: theories, observations, numerical modeling and forecasting

The Effect of Initial Vortex Asymmetric Structure on Tropical Cyclone Intensity Change in Response to an Imposed Environmental Vertical Wind Shear

AbstractPrevious studies have investigated how the environmental vertical wind shear (VWS) may trigger the asymmetric structure in an initially axisymmetric tropical cyclone (TC) vortex and how TC intensity changes in response. In this study, the possible effect of the initial vortex asymmetric structure on the TC intensity change in response to an imposed environmental VWS is investigated based on idealized full‐physics model simulations. Results show that the effect of the asymmetric structure in the initial TC vortex can either enhance or suppress the initial weakening of the TC in response to the imposed environmental VWS. When the initial asymmetric structure is in phase of the VWS‐induced asymmetric structure, the TC weakening will be enhanced and vice versa. Our finding calls for realistic representation of initial TC asymmetric structure in numerical weather prediction models and observations to better resolve the asymmetric structure in TCs.