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Deep Learning for Typhoon Intensity Classification Using Satellite Cloud ImagesAbstract
A tropical cyclone, also known as a typhoon, is one of the most destructive weather phenomena. Its intense cyclonic eddy circulations often cause serious damage to coastal areas. Accurate classification or prediction for typhoon intensity is crucial to disaster warning and mitigation management. But typhoon intensity-related feature extraction is a challenging task as it requires significant preprocessing and human intervention for analysis, and its recognition rate is poor due to various physical factors such as tropical disturbance. In this study, we built a Typhoon-CNNs framework, an automatic classifier for typhoon intensity based on a convolutional neural network (CNN). The Typhoon-CNNs framework utilized a cyclical convolution strategy supplemented with dropout zero-set, which extracted sensitive features of existing spiral cloud bands (SCBs) more effectively and reduces the overfitting phenomenon. To further optimize the performance of Typhoon-CNNs, we also proposed the improved activation function (T-ReLU) and the loss function (CE-FMCE). The improved Typhoon-CNNs was trained and validated using more than 10 000 multiple sensor satellite cloud images from the National Institute of Informatics. The classification accuracy reached to 88.74%. Compared with other deep learning methods, the accuracy of our improved Typhoon-CNNs was 7.43% higher than ResNet50, 10.27% higher than InceptionV3, and 14.71% higher than VGG16. Finally, by visualizing hierarchic feature maps derived from Typhoon-CNNs, we can easily identify the sensitive characteristics such as typhoon eyes, dense-shadowing cloud areas, and SCBs, which facilitate classifying and forecasting typhoon intensity.
Abstract
A tropical cyclone, also known as a typhoon, is one of the most destructive weather phenomena. Its intense cyclonic eddy circulations often cause serious damage to coastal areas. Accurate classification or prediction for typhoon intensity is crucial to disaster warning and mitigation management. But typhoon intensity-related feature extraction is a challenging task as it requires significant preprocessing and human intervention for analysis, and its recognition rate is poor due to various physical factors such as tropical disturbance. In this study, we built a Typhoon-CNNs framework, an automatic classifier for typhoon intensity based on a convolutional neural network (CNN). The Typhoon-CNNs framework utilized a cyclical convolution strategy supplemented with dropout zero-set, which extracted sensitive features of existing spiral cloud bands (SCBs) more effectively and reduces the overfitting phenomenon. To further optimize the performance of Typhoon-CNNs, we also proposed the improved activation function (T-ReLU) and the loss function (CE-FMCE). The improved Typhoon-CNNs was trained and validated using more than 10 000 multiple sensor satellite cloud images from the National Institute of Informatics. The classification accuracy reached to 88.74%. Compared with other deep learning methods, the accuracy of our improved Typhoon-CNNs was 7.43% higher than ResNet50, 10.27% higher than InceptionV3, and 14.71% higher than VGG16. Finally, by visualizing hierarchic feature maps derived from Typhoon-CNNs, we can easily identify the sensitive characteristics such as typhoon eyes, dense-shadowing cloud areas, and SCBs, which facilitate classifying and forecasting typhoon intensity.
Abstract
It is widely known that strong vertical wind shear (exceeding 10 m s−1) often weakens tropical cyclones (TCs). However, in some circumstances, a TC is able to resist this strong shear and even restrengthen. To better understand this phenomenon, a series of idealized simulations are conducted, followed by a statistical investigation of 40 years of Northern Hemisphere TCs. In the idealized simulations, a TC is embedded within a time-varying point-downscaling framework, which is used to gradually increase the environmental vertical wind shear to 14 m s−1 and then hold it constant. This controlled framework also allows for the separation of the TC-induced flow from the prescribed environmental flow. The TC-induced outflow is found to withstand the strong upper-tropospheric environmental flow, and this is manifested in the TC-induced shear difference (TCSD) vector. The TCSD vector, together with the environmental shear vector, defines an azimuthal range within which most of the asymmetric convection is located. The statistical analysis confirms the findings from the idealized simulations, and the results are not strongly sensitive to the TC intensity or basin. Moreover, compared with total shear, the inclusion of TCSD information creates a slightly better correlation with TC intensity change. Overall, the TCSD vector serves as a diagnostic to explain the ability of a TC to resist strong environmental shear through its outflow, and it could potentially be used as a parameter to predict future intensity change.
Abstract
It is widely known that strong vertical wind shear (exceeding 10 m s−1) often weakens tropical cyclones (TCs). However, in some circumstances, a TC is able to resist this strong shear and even restrengthen. To better understand this phenomenon, a series of idealized simulations are conducted, followed by a statistical investigation of 40 years of Northern Hemisphere TCs. In the idealized simulations, a TC is embedded within a time-varying point-downscaling framework, which is used to gradually increase the environmental vertical wind shear to 14 m s−1 and then hold it constant. This controlled framework also allows for the separation of the TC-induced flow from the prescribed environmental flow. The TC-induced outflow is found to withstand the strong upper-tropospheric environmental flow, and this is manifested in the TC-induced shear difference (TCSD) vector. The TCSD vector, together with the environmental shear vector, defines an azimuthal range within which most of the asymmetric convection is located. The statistical analysis confirms the findings from the idealized simulations, and the results are not strongly sensitive to the TC intensity or basin. Moreover, compared with total shear, the inclusion of TCSD information creates a slightly better correlation with TC intensity change. Overall, the TCSD vector serves as a diagnostic to explain the ability of a TC to resist strong environmental shear through its outflow, and it could potentially be used as a parameter to predict future intensity change.
Abstract
Although numerous studies have demonstrated that increasing model spatial resolution in free forecasts can potentially improve tropical cyclone (TC) intensity forecasts, studies on the impact of model resolution during data assimilation (DA) on TC prediction are lacking. In this study, using the ensemble-variational DA system for the Hurricane Weather Research and Forecasting (HWRF) Model, we investigated the individual impact of increasing the model resolution of first guess (FG) and background ensemble (BE) forecasts during DA on initial analyses and subsequent forecasts of Hurricane Patricia (2015). The impacts were compared between horizontal and vertical resolutions and also between the tropical storm (TS) and hurricane assimilation during Patricia. The results show that increasing the horizontal or vertical resolution in FG has a larger impact than increasing the resolution in BE on improving the analyzed TC intensity and structure for the hurricane stage. The result is reversed for the TS stage. These results are attributed to the effectiveness of increasing the FG resolution in intensifying the background vortex for the hurricane stage relative to the TS stage. Increasing the BE resolution contributes to improving the analyzed intensity through the better-resolved background correlation structure for both the hurricane and TS stages. Increasing horizontal resolution has an overall larger effect than increasing vertical resolution in improving the analysis at the hurricane stage and their effects are close for the analysis at the TS stage. Additionally, the more accurately analyzed primary circulation, secondary circulation, and warm-core structures via the increased resolution in DA lead to improved TC intensity forecasts.
Abstract
Although numerous studies have demonstrated that increasing model spatial resolution in free forecasts can potentially improve tropical cyclone (TC) intensity forecasts, studies on the impact of model resolution during data assimilation (DA) on TC prediction are lacking. In this study, using the ensemble-variational DA system for the Hurricane Weather Research and Forecasting (HWRF) Model, we investigated the individual impact of increasing the model resolution of first guess (FG) and background ensemble (BE) forecasts during DA on initial analyses and subsequent forecasts of Hurricane Patricia (2015). The impacts were compared between horizontal and vertical resolutions and also between the tropical storm (TS) and hurricane assimilation during Patricia. The results show that increasing the horizontal or vertical resolution in FG has a larger impact than increasing the resolution in BE on improving the analyzed TC intensity and structure for the hurricane stage. The result is reversed for the TS stage. These results are attributed to the effectiveness of increasing the FG resolution in intensifying the background vortex for the hurricane stage relative to the TS stage. Increasing the BE resolution contributes to improving the analyzed intensity through the better-resolved background correlation structure for both the hurricane and TS stages. Increasing horizontal resolution has an overall larger effect than increasing vertical resolution in improving the analysis at the hurricane stage and their effects are close for the analysis at the TS stage. Additionally, the more accurately analyzed primary circulation, secondary circulation, and warm-core structures via the increased resolution in DA lead to improved TC intensity forecasts.
Abstract
The interactions between the outflow of a tropical cyclone (TC) and its background flow are explored using a hierarchy of models of varying complexity. Previous studies have established that, for a select class of TCs that undergo rapid intensification in moderate values of vertical wind shear, the upper-level outflow of the TC can block and reroute the environmental winds, thus reducing the shear and permitting the TC to align and subsequently to intensify. We identify in satellite imagery and reanalysis datasets the presence of tilt nutations and evidence of upwind blocking by the divergent wind field, which are critical components of atypical rapid intensification. We then demonstrate how an analytical expression and a shallow water model can be used to explain some of the structure of upper-level outflow. The analytical expression shows that the dynamic high inside the outflow front is a superposition of two pressure anomalies caused by the outflow’s deceleration by the environment and by the environment’s deceleration by the outflow. The shallow water model illustrates that the blocking is almost entirely dependent upon the divergent component of the wind. Then, using a divergent kinetic energy budget analysis, we demonstrate that, in a full-physics TC, upper-level divergent flow generation occurs in two phases: pressure driven and then momentum driven. The change happens when the tilt precession reaches left of shear. When this change occurs, the outflow blocking extends upshear. We discuss these results with regard to prior severe weather studies.
Abstract
The interactions between the outflow of a tropical cyclone (TC) and its background flow are explored using a hierarchy of models of varying complexity. Previous studies have established that, for a select class of TCs that undergo rapid intensification in moderate values of vertical wind shear, the upper-level outflow of the TC can block and reroute the environmental winds, thus reducing the shear and permitting the TC to align and subsequently to intensify. We identify in satellite imagery and reanalysis datasets the presence of tilt nutations and evidence of upwind blocking by the divergent wind field, which are critical components of atypical rapid intensification. We then demonstrate how an analytical expression and a shallow water model can be used to explain some of the structure of upper-level outflow. The analytical expression shows that the dynamic high inside the outflow front is a superposition of two pressure anomalies caused by the outflow’s deceleration by the environment and by the environment’s deceleration by the outflow. The shallow water model illustrates that the blocking is almost entirely dependent upon the divergent component of the wind. Then, using a divergent kinetic energy budget analysis, we demonstrate that, in a full-physics TC, upper-level divergent flow generation occurs in two phases: pressure driven and then momentum driven. The change happens when the tilt precession reaches left of shear. When this change occurs, the outflow blocking extends upshear. We discuss these results with regard to prior severe weather studies.
Abstract
Hurricane Patricia (2015) was a record-breaking tropical cyclone that was difficult to forecast in real time by both operational numerical weather prediction models and operational forecasters. The current study examines the potential for improving intensity prediction for extreme cases like Hurricane Patricia. We find that Patricia’s intensity predictability is potentially limited by both initial conditions, related to the data assimilation, and model errors. First, convection-permitting assimilation of airborne Doppler radar radial velocity observations with an ensemble Kalman filter (EnKF) demonstrates notable intensity forecast improvements over assimilation of conventional observations alone. Second, decreasing the model horizontal grid spacing to 1 km and reducing the surface drag coefficient at high wind speed in the parameterization of the sea surface–atmosphere exchanges is also shown to notably improve intensity forecasts. The practical predictability of Patricia, its peak intensity, rapid intensification, and the underlying dynamics are further investigated through a high-resolution 60-member ensemble initialized with realistic initial condition uncertainties represented by the EnKF posterior analysis perturbations. Most of the ensemble members are able to predict the peak intensity of Patricia, but with greater uncertainty in the timing and rate of intensification; some members fail to reach the ultimate peak intensity before making landfall. Ensemble sensitivity analysis shows that initial differences in the region beyond the radius of maximum wind contributes the most to the differences between ensemble members in Patricia’s intensification. Ensemble members with stronger initial primary and secondary circulations beyond the radius of maximum wind intensify earlier, are able to maintain the intensification process for longer, and thus reach a greater and earlier peak intensity.
Abstract
Hurricane Patricia (2015) was a record-breaking tropical cyclone that was difficult to forecast in real time by both operational numerical weather prediction models and operational forecasters. The current study examines the potential for improving intensity prediction for extreme cases like Hurricane Patricia. We find that Patricia’s intensity predictability is potentially limited by both initial conditions, related to the data assimilation, and model errors. First, convection-permitting assimilation of airborne Doppler radar radial velocity observations with an ensemble Kalman filter (EnKF) demonstrates notable intensity forecast improvements over assimilation of conventional observations alone. Second, decreasing the model horizontal grid spacing to 1 km and reducing the surface drag coefficient at high wind speed in the parameterization of the sea surface–atmosphere exchanges is also shown to notably improve intensity forecasts. The practical predictability of Patricia, its peak intensity, rapid intensification, and the underlying dynamics are further investigated through a high-resolution 60-member ensemble initialized with realistic initial condition uncertainties represented by the EnKF posterior analysis perturbations. Most of the ensemble members are able to predict the peak intensity of Patricia, but with greater uncertainty in the timing and rate of intensification; some members fail to reach the ultimate peak intensity before making landfall. Ensemble sensitivity analysis shows that initial differences in the region beyond the radius of maximum wind contributes the most to the differences between ensemble members in Patricia’s intensification. Ensemble members with stronger initial primary and secondary circulations beyond the radius of maximum wind intensify earlier, are able to maintain the intensification process for longer, and thus reach a greater and earlier peak intensity.
Abstract
The dropsondes released during the Tropical Cyclone Intensity (TCI) field campaign provide high-resolution kinematic and thermodynamic measurements of tropical cyclones within the upper-level outflow and inner core. This study investigates the impact of these upper-level TCI dropsondes on analyses and prediction of Hurricane Patricia (2015) during its rapid intensification (RI) phase using an ensemble–variational data assimilation system. In the baseline experiment (BASE), both kinematic and thermodynamic observations of TCI dropsondes at all levels except the upper levels are assimilated. The upper-level wind and thermodynamic observations are assimilated in additional experiments to investigate their respective impacts. Compared to BASE, assimilating TCI upper-level wind observations improves the accuracy of outflow analyses verified against independent atmospheric motion vector (AMV) observations. It also strengthens the tangential and radial wind near the upper-level eyewall. The inertial stability within the upper-level eyewall is enhanced, and the maximum outflow is more aligned toward the inner core. Additionally, the analyses including the upper-level thermodynamic observations produce a warmer and drier core at high levels. Assimilating both upper-level kinematic and thermodynamic observations also improves the RI forecast. Compared to BASE, assimilating the upper-level wind induces more upright and inward-located eyewall convection, resulting in more latent heat release closer to the warm core. This process leads to stronger inner-core warming. Additionally, the initial warmer upper-level inner core produced by assimilating TCI thermodynamic observations also intensifies the convection and latent heat release within the eyewall, thus further contributing to the improved intensity forecasts.
Abstract
The dropsondes released during the Tropical Cyclone Intensity (TCI) field campaign provide high-resolution kinematic and thermodynamic measurements of tropical cyclones within the upper-level outflow and inner core. This study investigates the impact of these upper-level TCI dropsondes on analyses and prediction of Hurricane Patricia (2015) during its rapid intensification (RI) phase using an ensemble–variational data assimilation system. In the baseline experiment (BASE), both kinematic and thermodynamic observations of TCI dropsondes at all levels except the upper levels are assimilated. The upper-level wind and thermodynamic observations are assimilated in additional experiments to investigate their respective impacts. Compared to BASE, assimilating TCI upper-level wind observations improves the accuracy of outflow analyses verified against independent atmospheric motion vector (AMV) observations. It also strengthens the tangential and radial wind near the upper-level eyewall. The inertial stability within the upper-level eyewall is enhanced, and the maximum outflow is more aligned toward the inner core. Additionally, the analyses including the upper-level thermodynamic observations produce a warmer and drier core at high levels. Assimilating both upper-level kinematic and thermodynamic observations also improves the RI forecast. Compared to BASE, assimilating the upper-level wind induces more upright and inward-located eyewall convection, resulting in more latent heat release closer to the warm core. This process leads to stronger inner-core warming. Additionally, the initial warmer upper-level inner core produced by assimilating TCI thermodynamic observations also intensifies the convection and latent heat release within the eyewall, thus further contributing to the improved intensity forecasts.
Abstract
Interactions between the upper-level outflow of a sheared, rapidly intensifying tropical cyclone (TC) and the background environmental flow in an idealized model are presented. The most important finding is that the divergent outflow from convection localized by the tilt of the vortex serves to divert the background environmental flow around the TC, thus reducing the local vertical wind shear. We show that this effect can be understood from basic theoretical arguments related to Bernoulli flow around an obstacle. In the simulation discussed, the environmental flow diversion by the outflow is limited to 2 km below the tropopause in the 12–14-km (250–150 hPa) layer. Synthetic water vapor satellite imagery confirms the presence of upshear arcs in the cloud field, matching satellite observations. These arcs, which exist in the same layer as the outflow, are caused by slow-moving wave features and serve as visual markers of the outflow–environment interface. The blocking effect where the outflow and the environmental winds meet creates a dynamic high pressure whose pressure gradient extends nearly 1000 km upwind, thus causing the environmental winds to slow down, to converge, and to sink. We discuss these results with respect to the first part of this three-part study, and apply them to another atypical rapid intensification hurricane: Matthew (2016).
Abstract
Interactions between the upper-level outflow of a sheared, rapidly intensifying tropical cyclone (TC) and the background environmental flow in an idealized model are presented. The most important finding is that the divergent outflow from convection localized by the tilt of the vortex serves to divert the background environmental flow around the TC, thus reducing the local vertical wind shear. We show that this effect can be understood from basic theoretical arguments related to Bernoulli flow around an obstacle. In the simulation discussed, the environmental flow diversion by the outflow is limited to 2 km below the tropopause in the 12–14-km (250–150 hPa) layer. Synthetic water vapor satellite imagery confirms the presence of upshear arcs in the cloud field, matching satellite observations. These arcs, which exist in the same layer as the outflow, are caused by slow-moving wave features and serve as visual markers of the outflow–environment interface. The blocking effect where the outflow and the environmental winds meet creates a dynamic high pressure whose pressure gradient extends nearly 1000 km upwind, thus causing the environmental winds to slow down, to converge, and to sink. We discuss these results with respect to the first part of this three-part study, and apply them to another atypical rapid intensification hurricane: Matthew (2016).
Abstract
Operational numerical models failed to predict the record-setting rapid intensification and rapid overwater weakening of Hurricane Patricia (2015) in the eastern North Pacific basin, resulting in large intensity forecast errors. In an effort to better understand the mesoscale processes contributing to Patricia’s rapid intensity changes, we analyze high-resolution aircraft observations collected on 22–23 October. Spline-based variational analyses are created from observations collected via in situ measurements, Doppler radar, and full-tropospheric dropsonde profiles as part of the Office of Naval Research Tropical Cyclone Intensity (TCI) experiment and the National Oceanic and Atmospheric Administration Intensity Forecasting Experiment (IFEX). We present the first full-tropospheric calculation of the dry, axisymmetric Ertel’s potential vorticity (PV) in a tropical cyclone without relying on balance assumptions. Detailed analyses reveal the formation of a “hollow tower” PV structure as Patricia rapidly approached its maximum intensity, and a subsequent breakdown of this structure during Patricia’s rapid overwater weakening phase. Transforming the axisymmetric PV analyses from radius–height to potential radius–isentropic coordinates reveals that Patricia’s rapid intensification was closely related to the distribution of diabatic heating and eddy mixing. During Patricia’s rapid overwater weakening phase, eddy mixing processes are hypothesized to be the primary factor rearranging the PV distribution near the eye–eyewall region, diluting the PV previously confined to the hollow tower while approximately conserving the absolute circulation.
Abstract
Operational numerical models failed to predict the record-setting rapid intensification and rapid overwater weakening of Hurricane Patricia (2015) in the eastern North Pacific basin, resulting in large intensity forecast errors. In an effort to better understand the mesoscale processes contributing to Patricia’s rapid intensity changes, we analyze high-resolution aircraft observations collected on 22–23 October. Spline-based variational analyses are created from observations collected via in situ measurements, Doppler radar, and full-tropospheric dropsonde profiles as part of the Office of Naval Research Tropical Cyclone Intensity (TCI) experiment and the National Oceanic and Atmospheric Administration Intensity Forecasting Experiment (IFEX). We present the first full-tropospheric calculation of the dry, axisymmetric Ertel’s potential vorticity (PV) in a tropical cyclone without relying on balance assumptions. Detailed analyses reveal the formation of a “hollow tower” PV structure as Patricia rapidly approached its maximum intensity, and a subsequent breakdown of this structure during Patricia’s rapid overwater weakening phase. Transforming the axisymmetric PV analyses from radius–height to potential radius–isentropic coordinates reveals that Patricia’s rapid intensification was closely related to the distribution of diabatic heating and eddy mixing. During Patricia’s rapid overwater weakening phase, eddy mixing processes are hypothesized to be the primary factor rearranging the PV distribution near the eye–eyewall region, diluting the PV previously confined to the hollow tower while approximately conserving the absolute circulation.
Abstract
The newly developed expendable digital dropsonde (XDD) allows for high spatial and temporal resolution data collection in tropical cyclones (TCs). In 2015, a total of 725 XDDs were launched into Hurricanes Marty (27–28 September), Joaquin (2–5 October), and Patricia (20–23 October) as part of the Tropical Cyclone Intensity (TCI) experiment. These dropsondes were launched from a NASA WB-57 at altitudes above 18 km, capturing the full depth of the TCs to the tropopause. This study documents the vertical velocity distributions observed in TCI using the XDDs and examines the distributions altitudinally, radially, and azimuthally. The strongest mean or median XDD-derived vertical velocities observed during TCI occurred in the upper levels and within the cores of the three TCs. There was little azimuthal signal in the vertical velocity distribution, likely due to sampling asymmetries and noise in the data. Downdrafts were strongest in Joaquin, while updrafts were strongest in Patricia, especially within the eyewall on 23 October. Patricia also had an impressive low-level (<2 km) updraft that exceeded 10 m s−1 associated with a shallow, overturning, radial circulation in the secondary eyewall.
Abstract
The newly developed expendable digital dropsonde (XDD) allows for high spatial and temporal resolution data collection in tropical cyclones (TCs). In 2015, a total of 725 XDDs were launched into Hurricanes Marty (27–28 September), Joaquin (2–5 October), and Patricia (20–23 October) as part of the Tropical Cyclone Intensity (TCI) experiment. These dropsondes were launched from a NASA WB-57 at altitudes above 18 km, capturing the full depth of the TCs to the tropopause. This study documents the vertical velocity distributions observed in TCI using the XDDs and examines the distributions altitudinally, radially, and azimuthally. The strongest mean or median XDD-derived vertical velocities observed during TCI occurred in the upper levels and within the cores of the three TCs. There was little azimuthal signal in the vertical velocity distribution, likely due to sampling asymmetries and noise in the data. Downdrafts were strongest in Joaquin, while updrafts were strongest in Patricia, especially within the eyewall on 23 October. Patricia also had an impressive low-level (<2 km) updraft that exceeded 10 m s−1 associated with a shallow, overturning, radial circulation in the secondary eyewall.
Abstract
This study investigates the role of the asymmetric interaction between the tropical cyclone (TC) and the environmental flow in governing the TC inner-core asymmetric structure. Motivated by the limitations of bulk measures of vertical wind shear in representing the complete environmental flow, the TC outflow is used as a focus for the asymmetric interaction. By analyzing an idealized numerical simulation, it is demonstrated that parcels can go directly from the asymmetric rainband to the upper-level outflow. The relatively large vertical mass flux in the rainband region also suggests that the asymmetric rainband is an important source of the outflow. In a simulation that suppresses convection by reducing the water vapor within the rainband region, the upper-level outflow is weakened, further supporting the hypothesis that the rainband and outflow are directly connected. Finally, it is demonstrated that the asymmetric outflow and the outer rainband are coupled through the descending inflow below the outflow. Some of the main characteristics of the outflow–rainband relationship are also supported by a real-case numerical simulation of Hurricane Bill (2009). The relationship is potentially useful for understanding and predicting the evolution of the TC inner-core structure during the interaction with the large-scale environmental flow.
Abstract
This study investigates the role of the asymmetric interaction between the tropical cyclone (TC) and the environmental flow in governing the TC inner-core asymmetric structure. Motivated by the limitations of bulk measures of vertical wind shear in representing the complete environmental flow, the TC outflow is used as a focus for the asymmetric interaction. By analyzing an idealized numerical simulation, it is demonstrated that parcels can go directly from the asymmetric rainband to the upper-level outflow. The relatively large vertical mass flux in the rainband region also suggests that the asymmetric rainband is an important source of the outflow. In a simulation that suppresses convection by reducing the water vapor within the rainband region, the upper-level outflow is weakened, further supporting the hypothesis that the rainband and outflow are directly connected. Finally, it is demonstrated that the asymmetric outflow and the outer rainband are coupled through the descending inflow below the outflow. Some of the main characteristics of the outflow–rainband relationship are also supported by a real-case numerical simulation of Hurricane Bill (2009). The relationship is potentially useful for understanding and predicting the evolution of the TC inner-core structure during the interaction with the large-scale environmental flow.
