Tropical Cyclone Size Research Articles

On the Size Dependence in the Recent Time-Dependent Theory of Tropical Cyclone Intensification

Abstract Previous observational studies have shown that the intensification rate (IR) of a tropical cyclone (TC) is often correlated with its real-time size. However, no any size parameter explicitly appears in the recent time-dependent theory of TC intensification, while the theory can still well capture the intensity evolution of simulated TCs. This study provides a detailed analysis to address how TC real-time size affects its intensification and why no size parameter explicitly appears in the theory based on the results from axisymmetric numerical simulations. The results show that the overall correlation between the TC IR and real-time size as reported in previous observational studies, in terms of both the radius of maximum wind (RMW) and the radius of 17 m s−1 wind (R17), is largely related to the correlation between the IR and intensity because the size and intensity are highly interrelated. As a result, the correlation between the TC IR and size for a given intensity is rather weak. Diagnostic analysis shows that the TC real-time size (RMW and R17) has two opposing effects on intensification. A larger TC size tends to result in a higher steady-state intensity but reduce the conversion efficiency of thermodynamic energy to inner-core kinetic energy or the degree of moist neutrality of the eyewall ascent for a given intensity. The former is favorable, while the latter is unfavorable for intensification. The two effects are implicitly included in the theory and largely offset, resulting in the weak dependence of the IR on TC size for a given intensity.

TC‐GEN: Data‐Driven Tropical Cyclone Downscaling Using Machine Learning‐Based High‐Resolution Weather Model

AbstractSynthetic downscaling of tropical cyclones (TCs) is critically important to estimate the long‐term hazard of rare high‐impact storm events. Existing downscaling approaches rely on statistical or statistical‐deterministic models that are capable of generating large samples of synthetic storms with characteristics similar to observed storms. However, these models do not capture the complex two‐way interactions between a storm and its environment. In addition, these approaches either necessitate a separate TC size model to simulate storm size or involve post‐processing to capture the asymmetries in the simulated surface wind. In this study, we present an innovative data‐driven approach for TC synthetic downscaling. Using a machine learning‐based high‐resolution global weather model (ML‐GWM), our approach can simulate the full life cycle of a storm with asymmetric surface wind that accounts for the two‐way interactions between the storm and its environment. This approach consists of multiple components: a data‐driven model for generating synthetic TC seeds, a blending method that seamlessly integrates storm seeds into the surrounding while maintaining the seed structure, and a model based on a recurrent neural network to correct for biases in storm intensity. Compared to observations and synthetic storms simulated using existing statistical‐deterministic and statistical downscaling approaches, our method shows the ability to effectively capture many aspects of TC statistics, including track density, landfall frequency, landfall intensity, and outermost wind extent. Leveraging the computational efficiency of ML‐GWM, our approach shows substantial potential for TC regional hazard and risk assessment.

A Markov model approach for statistical tropical cyclone wind radii baseline forecasts

Abstract A skill baseline for five-day, 34-, 50-, and 64-knot (1 kt = 0.514 m s−1) tropical cyclone (TC) wind radii forecasts is described. The Markov Model CLiper (MMCL) generates a sequence of 12-h forecasts out to a forecast length limited only by the length of the forecast track and intensity. The model employs a climatology of TC size based on infrared satellite imagery, a Markov chain, and a basin-specific drift. MMCL uses the initial wind radii and initial forecast track and intensity as input. Unlike the previously developed wind radii climatology and persistence model (DRCL) that reverts to a climatological size and shape after approximately 48 h, MMCL retains more of its initial size and asymmetry and is likely more palatable for use in operational forecasting. MMCL runs operationally in the western North Pacific basin, the North Indian Ocean, and the Southern Hemisphere for the Joint Typhoon Warning Center (JTWC) in Pearl Harbor, Hawaii. This work also describes the development of Atlantic and eastern North Pacific versions of MMCL. MMCL’s formulation allows unlimited extension of forecast lead time without reverting to a generic climatological size and shape. Independent forecast comparisons between MMCL and DRCL for the 2020–2022 seasons demonstrates that MMCL’s mean absolute errors are generally smaller and biases are closer to zero in North Atlantic, and eastern North Pacific basins, and in the Southern Hemisphere. This validation includes a few example forecasts and demonstrates that MMCL can be used both as a baseline for assessing wind radii forecast skill and operational use.

Sensitivity of Philippine historically damaging tropical cyclone events to surface and atmospheric temperature forcings

As the climate warms, sea surface temperature (SST) is projected to increase, along with atmospheric variables which may have an impact on tropical cyclone (TC) properties. Climate models have well-known errors in simulating current climate SSTs that will likely affect future TC projections. Therefore, a better understanding of the impact of SST changes will help us identify the largest uncertainty in projecting TC changes. This study employs three different and independent methodologies to investigate the impact of sea surface and atmospheric temperature changes and tropical cyclone (TC) characteristics, focusing on three historically damaging TCs in the Philippines: Typhoons Haiyan, Bopha, and Mangkhut. These methodologies include initially simulations with uniform SST anomalies between −4 to +4°C, then experiments using delta from CMIP6 CESM2 for SST and atmospheric temperature in the far future, and, finally, simulations imposing Radiative-Convective Equilibrium (RCE) conditions. The experiments reveal significant insights into TC dynamics under varying environmental conditions. Changes in SSTs resulted in changes in TC track, intensity, and rainfall. In the positive SST simulations, TCs tended to move northwards and resulted in substantial increases in maximum wind speeds reaching a difference of up to 10, 13, 23 ms−1 for Typhoons Haiyan, Bopha, and Mangkhut, respectively. Analysis of the accumulated rainfall also showed that increased SST results in increased rainfall. Inclusion of atmospheric warming offsets the intensification due to SST change. Moreover, warmer SSTs resulted in slower-moving TCs and increased TC size. Further analyses incorporating atmospheric temperature adjustments derived from CESM2 and RCE simulations offer better insights on TC response. Under near-RCE conditions, TCs exhibit reduced sensitivity to SST changes, with smaller intensity and size modifications simulated when stable relative humidity is imposed. The smaller changes in TC intensity and size observed in these experiments suggest that maintaining atmospheric stability through pre-storm atmospheric adjustments dampens the response of TCs to SST warming.

Numerical simulation of size contraction of Typhoon Cempaka (2021)

In 2021, Cempaka, a tiny tropical cyclone, made landfall in China. As the TC intensified prior to landfall, the tropical cyclone size measured with precipitation decreased significantly. A numerical simulation was conducted to examine the possible processes modulating the storm size. Azimuthally mean potential vorticity (PV) was found to decrease mainly in the middle to upper troposphere between 50- and 80-km radii. The PV budget results indicate that the advection and generation of mean PV associated with asymmetric processes, rather than the symmetric processes, primarily contributed to the decrease in mean PV. These asymmetric processes leading to a negative PV tendency were likely associated with inactive outer rainbands. In contrast, the tangential winds simultaneously expanded radially outward, possibly related to inner-core diabatic heating. The findings here emphasize the importance of outer rainband activity in tropical cyclone size change.摘要2021年台风“查帕卡”增强过程中风场向外扩张, 但卫星云图显示降水范围减小. 高分辨率数值模拟结果显示, “查帕卡”降水范围减小对应着50-80km半径处对流层中层位涡的减小. 位涡诊断结果表明, 非对称过程是导致上述位涡减小的关键物理过程, 而非对称过程主要同外雨带活动减弱有关. 上述结果强调了外雨带活动对热带气旋尺度变化的潜在重要影响.

Construction of a tropical cyclone size dataset using reanalysis data

AbstractThis paper details the creation of a tropical cyclone (TC) size dataset using the NCEP/NCAR Reanalysis I dataset for landfalling TCs along the United States coastline from 1948 to 2022. The radius of the outermost closed isobar (ROCI) is used as the size parameter. The dataset comprises landfall ROCI for 220 TCs. Storms are split into three zones (Texas–Alabama, Florida and Georgia–Maine) to determine if TC size varies geographically. Results showed a significant difference in landfall size, with Florida storms larger on average than the Texas–Alabama storms. Additionally, TC size increased with increasing intensity from tropical storm to Category 3, and storms tended to be larger later in the hurricane season, but there was no significant trend in landfall size over the 75‐year period. ROCI exhibited statistically significant positive correlations with longitude and wind speed and a negative correlation with the outermost closed isobar's pressure. The dataset's creation is an example of how reanalysis datasets can be used to develop a TC size climatology.

A flexible tropical cyclone vortex initialization technique for GFDL SHiELD

Tropical cyclone (TC) intensity forecasting poses challenges due to complex dynamical processes and data inadequacies during model initialization. This paper describes efforts to improve TC intensity prediction in the Geophysical Fluid Dynamics Laboratory (GFDL) System for High-resolution prediction on Earth-to-Local Domains (SHiELD) model by implementing a Vortex Initialization (VI) technique. The GFDL SHiELD model, relying on the Global Forecast System (GFS) analysis for initialization, faces deficiencies in initial TC structure and intensity. The VI method involves adjusting the TC vortex inherited from the GFS analysis and merging it back into the environment at the observed location, enhancing the analyzed representation of storm structure. We made modifications to the VI package implemented in the operational Hurricane Analysis and Forecast System, including handling initial condition data, reducing input domain size, and improving storm intensity enhancement. Experiments using the T-SHiELD configuration demonstrate that using VI significantly improves the representation of initial TC intensity and size, enhancing TC predictions, particularly in storm intensity and outer wind forecasts within the first 48 h.

On the Size Discrepancies between Datasets from China Meteorological Administration and Joint Typhoon Warning Center for the Northwestern Pacific Tropical Cyclones

This study analyzes the Northwestern Pacific tropical cyclone (TC) size difference between the China Meteorological Administration (CMA) dataset and the Joint Typhoon Warning Center (JTWC) dataset. The TC size is defined by the near-surface 34-knot wind radius (R34). Although there is a high correlation (correlation coefficient of 0.71) between CMA and JTWC R34 values, significant discrepancies are still found between them. The JTWC tends to report larger R34 values than the CMA for large-sized TCs, while the trend is reversed for compact TCs. Despite spatial distribution discrepancies, both datasets exhibit significant similarity (spatial correlation coefficient of 0.61), particularly in latitudinal distribution; higher R34 values are observed near 25° N. An investigation of key parameters affecting R34 estimations shows that the discrepancies in R34 values between the two agencies’ estimates of TC size are primarily influenced by the size itself and latitude. There is a high correlation between R34 difference and R34 values, with a high correlation of up to 0.58 with the JTWC’s R34 values. There is also a significant correlation between R34 difference and latitude, with a correlation coefficient of 0.26 in both the CMA and JTWC datasets. Case studies of Typhoons “Danas” and “Maysak” confirm distinct characteristics in R34 estimations during different development stages, with the JTWC capturing TC intensification better, while the CMA underestimates TC size during rapid growth phases. During the weakening stage of the TC, both agencies accurately estimate the R34 values. These findings contribute valuable insights into the discrepancies and characteristics of R34 datasets, informing the selection and utilization of data for typhoon research and forecasting.

Why does tropical cyclone intensify faster under weaker parameterized horizontal diffusion in three-dimensional numerical simulations?

The mechanisms by which the parameterized horizontal diffusion impacts the tropical cyclone (TC) intensification are investigated via a series of numerical simulations of Supertyphoon Lekima (2019) with different horizontal mixing lengths (Lh). Consistent with previous studies, the TC intensifies faster and attains a higher peak intensity as Lh decreases, which is attributed to a more compact wind structure and the stronger eyewall convective heating. The azimuthal-mean tangential wind and vertical momentum budgets are conducted to explain the differences in TC size and convective strength between the simulations. It is found that when horizontal diffusion weakens, the eddy mixing of tangential momentum becomes larger inside the radius of maximum wind, therefore promoting size contraction. The stronger eddy mixing is attributed to the more active eyewall mesovortices whose growth is suppressed when horizontal diffusion strengthens. The vertical momentum budget results indicate that the air parcels in the simulation with smaller Lh experience stronger upward buoyancy force in the lower-to-mid troposphere (3–7 km) than those in the larger-Lh simulation, leading to more vigorous eyewall convection. It is further revealed that the reduced horizontal diffusion helps maintain the strength of virtual potential temperature perturbations and therefore the buoyancy in the eyewall updrafts. The findings from this study imply that the horizontal diffusion plays an important role in regulating the mesoscale and convective-scale processes in the inner-core region and improvements in its parameterization are urged for improving TC intensity forecast.

Operational scheme for predicting tropical cyclone wind radius based on a statistical–dynamical approach and track pattern clustering

AbstractAn operational scheme for predicting the symmetric R30 and R50 of tropical cyclones (TCs) in the western North Pacific was developed using a statistical regression method and track pattern clustering (four clusters). The statistical–dynamical model employs multiple linear regressions of two to eight variables at each cluster and forecast lead time. The dependent variable for prediction was the change in the 5‐kt wind radius (R5)—a proxy of TC size—relative to the initial time. The performance of the model was compared for the training (2008–2016) and testing (2017–2018) periods. The effect of clustering on TC size prediction was evaluated by comparing the performance of the non‐clustering and clustering models. The clustering model improved the prediction of TC size by 3%–24% at all lead times during the training period, especially with a significant improvement of up to 43% in Cluster 2. In Cluster 2, because most TCs tend to develop strongly and continue to increase in size, it greatly reduced the variability in TC size through clustering, allowed for smarter predictor selection, and ultimately improved TC size prediction. In the real‐time R30 and R50 predictions for the 2017 and 2018 TCs, the error of the clustered model was 18%–19% less than that of the non‐clustered model. The analysis results revealed that the real‐time prediction errors of the current model increase when the TC tracks are difficult to classify into specific clusters, the predicted environments and TC tracks are inaccurate, and the size and intensity of a TC rapidly increase.

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.

Changes of tropical cyclone size in three oceanic basins of the northern hemisphere from 2001 to 2021

Changes of tropical cyclone size in three oceanic basins of the northern hemisphere from 2001 to 2021

Spatiotemporal Variability of Tropical Cyclone Genesis Density in the Northwest Pacific

Abstract The small sample size of tropical cyclone (TC) genesis in the observations prevents us from fully characterizing its spatiotemporal variations. Here we take advantage of a large ensemble of 60-km-resolution atmospheric simulations to address this issue over the northwest Pacific (NWP) during 1951–2010. The variations in annual TC genesis density are explored separately on interannual and decadal time scales. The interannual variability is dominated by two leading modes. One is characterized by a dipole pattern, and its temporal evolution is closely linked to the developing ENSO. The other mode features high loadings in the central part of the basin, with out-of-phase changes near the equator and date line, and tends to occur during ENSO decay years. On decadal time scales, TC genesis density variability is primarily controlled by one mode, which exhibits an east–west dipole pattern with strong signals confined to south of 20°N and is tied to the interdecadal Pacific oscillation–like sea surface temperature anomalies. Further, we investigate the seasonal evolution of the ENSO effect on TC genesis density. The results highlight the distinct impacts of the two types of ENSO (i.e., eastern Pacific vs central Pacific) on TC genesis density in the NWP during a specific season and show the strong seasonal dependency of the TC genesis response to ENSO. Although the results from the observations are not as prominent as those from the simulations because of the small sample size, the high consistency between them demonstrates the fidelity of the model in reproducing TC statistics and variability in the observations.

Environmental ingredients that lead to tornado outbreak and tornado failure: A comparison between two similar recurving tropical cyclones

AbstractRecurving tropical cyclones (TCs) can sometimes produce tornado outbreaks, while some TCs with similar tracks and intensities may produce none of tornado, which makes it challenging to assess tornado risk within recurving TCs. This study investigates two recurving TCs, Typhoon Yagi (2018) and Typhoon In‐Fa (2021), that made landfall in eastern China. Despite the similar recurving tracks and intensities, Yagi produced 11 tornadoes while In‐Fa produced none. Results show that both TCs were characterized by similar large‐scale conditions that were dynamically favourable for tornadoes during the recurvature process. The non‐tornadic In‐Fa even featured a higher shear and helicity environment in its northeast sector than did the tornado‐productive Yagi. The greatest difference between Yagi and In‐Fa is the thermodynamic instability owing to the different lower–middle‐tropospheric lapse rates that are attributable to the differences in air trajectories at low levels. In‐Fa featured marginal instability due to the cooler air at low levels because almost all of the air parcels came from the Pacific Ocean while most air parcels for Yagi came from the warm land. The cooler low‐level air tends to create higher relative humidity in In‐Fa's interior and thus leads to widespread precipitation which in turn also contributes to the low‐level cooling. The different air trajectories are demonstrated related to the TC's translation speed, size and synoptic characteristics days before TC's landfall. Numerical simulations suggest that the upward motions within the widespread precipitation regions of In‐Fa are overall weaker than those of Yagi due to the limited instability in the former. These findings suggest that even though two TCs were characterized by similar tracks, intensities and large‐scale forcings, their different low‐level air pathways may have significant influence on priming the mesoscale environment for supercell or tornado formation.

The Characteristics of Tropical Cyclones and Associated High Winds in Shanghai from 2005 to 2020

The characteristics of 21 tropical cyclones (TCs) and associated high winds in Shanghai from 2005 to 2020 are analyzed based on the TC best track data, surface observations, and ERA5 reanalysis dataset. Results indicate that 95% of the TCs causing high winds in Shanghai were generated during July to September, and located to the south of Shanghai for ~80% of the impact time. Divided into four categories according to tracks, the TCs show significant discrepancies in genesis month and location, intensity, size, and duration. The findings of high winds in Shanghai highlight the combination effect of TC intensity, track, and the distance between TCs and Shanghai normalized by TC size (NDSH-TC). The maximum 10‐min sustained (Vmax) and extreme 3‐s wind speeds (Vex) in Shanghai only increase slightly with TC intensity, due to the fact that the stronger TCs are usually farther away from Shanghai. The Vmax and Vex differ in response to different TC tracks and reach the maximum value under TCs moving northward across the land west of Shanghai and the sea east of Shanghai, respectively. The urban and coastal wind speeds are the smallest and the largest respectively, indicating that the largest underlying surface roughness is associated with the most rapid urban construction. The wind speeds under the influence of TCs in four quadrants of Shanghai also present different variations with the NDSH-TC. Additionally, the wind direction in Shanghai is regularly related to the relative position between the TC center and Shanghai. A case study combined with TC In‐Fa (2106) verified the validity of the conclusions, which can provide a reference for TC forecasters to make more accurate wind field predictions.

Integrating Climatological‐Hydrodynamic Modeling and Paleohurricane Records to Assess Storm Surge Risk

AbstractSediment cores from blue holes have emerged as a promising tool for extending the record of long‐term tropical cyclone (TC) activity. However, interpreting this archive is challenging because storm surge depends on many parameters including TC intensity, track, and size. In this study, we use climatological‐hydrodynamic modeling to interpret paleohurricane sediment records between 1851 and 2016 and assess the storm surge risk for Long Island in The Bahamas. As the historical TC data from 1988 to 2016 is too limited to estimate the surge risk for this area, we use historical event attribution in paleorecords paired with synthetic storm modeling to estimate TC parameters that are often lacking in earlier historical records (i.e., the radius of maximum wind for storms before 1988). We then reconstruct storm surges at the sediment site for a longer time period of 1851–2016 (the extent of hurricane Best Track records). The reconstructed surges are used to verify and bias‐correct the climatological‐hydrodynamic modeling results. The analysis reveals a significant risk for Long Island in The Bahamas, with an estimated 500‐year stormtide of around 1.63 ± 0.26 m, slightly exceeding the largest recorded level at site between 1988 and 2015. Finally, we apply the bias‐corrected climatological‐hydrodynamic modeling to quantify the surge risk under two carbon emission scenarios. Due to sea level rise and TC climatology change, the 500‐year stormtide would become 2.69 ± 0.50 and 3.29 ± 0.82 m for SSP2‐4.5 and SSP5‐8.5, respectively by the end of the 21st century.

Evaluation of Parametric Tropical Cyclone Surface Winds over the Eastern Australian Region

Abstract Hazard studies based on thousands of synthetic tropical cyclone (TC) events require a validated model representation of the surface wind field. Here, we assess three different TC parametric vortex models with input from four along-track parameter studies of the TC size and shape, based on statistical formulation of the relationships to observed TC intensity, geographic location, and forward transition speed. The 12 model combinations are compared to in situ 10-min observed surface mean wind speeds for 10 TCs that made landfall over Queensland, Australia, which occurred over the period 2006–17. Empirical wind reduction factors to reduce gradient winds to the surface are recalculated for the more recent TCs at both offshore (ocean, small islands, reefs, and moorings) and onshore (land) locations. To improve the wind comparisons over ocean and land, a secondary reduction factor was developed based on an inland decay function. Pearson correlations for the unadjusted modeled peak wind speed from 118 instances of a TC passing a weather station sit between a range of 0.57 and 0.65 for the 12 model combinations. Using the secondary reduction factor based on the inland decay function increases the range of correlation to 0.74–0.81. Based on the assessment of the instances of peak surface wind speed correlations, bias, and root-mean-square error, along with the correlation 48 h around the peak, the top-ranked performing model combination for the region was an along-track parameter study with a double-vortex model, both previously tested for the South Pacific basin. Significance Statement When assessing tropical cyclone hazards, users are presented with several simplified parametric models to describe the surface wind field of tropical cyclones. These parametric models are used routinely for risk assessment of cyclonic winds, as well as for input to surge and wave models used in coastal hazard assessments. Differences between the models include the formulation of the parametric cyclone model, the way winds above the boundary layer are specified at the surface and along-track parameters that describe the cyclones’ size and shape. Of the 12 model combinations investigated in this study, the top-ranked performing model combination for the region was an along-track parameter equation with a double-vortex model, which were both tested previously for the South Pacific basin. Analysis is performed to show unadjusted modeled winds overestimate observed 10-min surface winds over the ocean by around 13% (median) and over land by around 73.9% (median), which is resolved in this study with a secondary empirical wind reduction factor. These findings will support future modeling of tropical cyclone winds for multiple applications, including regional risk assessment and coastal hazard studies.

Assessing Storm Surge Multiscenarios Based on Ensemble Tropical Cyclone Forecasting

AbstractEnsemble forecasting is a promising tool to aid in making informed decisions against risks of coastal storm surges. Although tropical cyclone (TC) ensemble forecasts are commonly used in operational numerical weather prediction systems, their potential for disaster prediction has not been maximized. Here we present a novel, efficient, and practical method to utilize a large ensemble forecast of 1,000 members to analyze storm surge scenarios toward effective decision making such as evacuation planning and issuing surge warnings. We perform the simulation of TC Hagibis (2019) using the Japan Meteorological Agency's (JMA) nonhydrostatic model. The simulated atmospheric predictions were utilized as inputs for a statistical surge model named the Storm Surge Hazard Potential Index to estimate peak surge heights along the central coast of Japan. We show that Pareto‐optimized solutions from an ensemble storm surge forecast can describe potential worst (maximum) and optimum (minimum) storm surge scenarios. These solutions exemplify a diversity of trade‐off surge outcomes across diverse coastal locations, reflecting variations in coastal geometry, including bathymetry. For example, some of the Pareto‐optimized solutions that illustrate worst surge scenarios for inner bay locations are not necessarily accountable for bringing severe surge cases in open coasts. We further emphasize that an in‐depth evaluation of Pareto‐optimal solutions can shed light on how meteorological variables such as track, intensity, and size of TCs influence the worst and optimum surge scenarios, which is not clearly quantified in current multiscenario assessment methods such as those used by JMA/National Hurricane Center in the United States.

Effect of Storm Size on Sea Surface Cooling and Tropical Cyclone Intensification in the Western North Pacific

Abstract The effect of tropical cyclone (TC) size on TC-induced sea surface temperature (SST) cooling and subsequent TC intensification is an intriguing issue without much exploration. Via compositing satellite-observed SST over the western North Pacific during 2004–19, this study systematically examined the effect of storm size on the magnitude, spatial extension, and temporal evolution of TC-induced SST anomalies (SSTA). Consequential influence on TC intensification is also explored. Among the various TC wind radii, SSTA are found to be most sensitive to the 34-kt wind radius (R34) (1 kt ≈ 0.51 m s−1). Generally, large TCs generate stronger and more widespread SSTA than small TCs (for category 1–2 TCs, R34: ∼270 vs 160 km; SSTA: −1.7° vs −0.9°C). Despite the same effect on prolonging residence time of TC winds, the effect of doubling R34 on SSTA is more profound than halving translation speed, due to more wind energy input into the upper ocean. Also differing from translation speed, storm size has a rather modest effect on the rightward shift and timing of maximum cooling. This study further demonstrates that storm size regulates TC intensification through an oceanic pathway: large TCs tend to induce stronger SST cooling and are exposed to the cooling for a longer time, both of which reduce the ocean’s enthalpy supply and thereby diminish TC intensification. For larger TCs experiencing stronger SST cooling, the probability of rapid intensification is half of smaller TCs. The presented results suggest that accurately specifying storm size should lead to improved cooling effect estimation and TC intensity prediction. Significance Statement Storm size has long been speculated to play a crucial role in modulating the TC self-induced sea surface temperature (SST) cooling and thus potentially influence TC intensification through ocean negative feedback. Nevertheless, systematic analysis is lacking. Here we show that larger TCs tend to generate stronger SST cooling and have longer exposure to the cooling effect, both of which enhance the strength of the negative feedback. Consequently, larger TCs undergo weaker intensification and are less likely to experience rapid intensification than smaller TCs. These results demonstrate that storm size can influence TC intensification not only from the atmospheric pathway, but also via the oceanic pathway. Accurate characterization of this oceanic pathway in coupled models is important to accurately forecast TC intensity.

Regime Shifts in the Damage Caused by Tropical Cyclones in the Guangdong–Hong Kong–Macao Greater Bay Area of China

Tropical cyclones (TCs) pose a significant threat in terms of wind-induced damage and storm risk to the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) of China. In this research, annual power dissipation index (PDI) and storm surge and wave destructive potential (SDP) index from June to November were used to estimate the damage caused by the TC events in the buffer zone of the GBA. The regime shifts in 1993 and 2013 were identified through the Bayesian changepoint detection in six TC datasets. The TC-induced damage during 1994–2012 (the low-damage period) was weaker than that in 1977–1993 and 2013–2020 (the high-damage periods). The intensity and size of stronger TCs are the dominant factors responsible for the interdecadal changes in the TC damage. This may be explained by the influences of sea surface temperature (SST), surface latent heat flux and mid-level relative humidity. During high-damage periods, TCs can extract more energy from the ocean, leading to increased TC intensity and larger size. Conversely, during low-damage periods, TCs undergo a decrease in energy intake, resulting in reduced TC power and smaller size. The variations in the SST are relative to the Luzon Strait transport. In addition, the reduction in TC translation speed is unfavorable for the development of TCs in low-damage periods. Further research suggested that mid-level steering flow affects the TC movement velocity. The results offer valuable insights into the variations in the TC-induced damage in the GBA.