Hurricane Center Research Articles

Stratospheric Gravity Waves as a Proxy for Hurricane Intensification: A Case Study of Weather Research and Forecast Simulation for Hurricane Joaquin

AbstractWe conducted simulations with a 4‐km resolution for Hurricane Joaquin in 2015 using the weather research and forecast (WRF) model. The model data are used to study stratospheric gravity waves (GWs) generated by the hurricane and how they correlate with hurricane intensity. The simulation results show spiral GWs propagating upward and anticlockwise away from the hurricane center. GWs with vertical wavelengths up to 14 km are generated. We find that GW activity is more frequent and intense during hurricane intensification than during weakening, particularly for the most intense GW activity. There are significant correlations between the change of stratospheric GW intensity and hurricane intensity. Therefore, the emergence of intensive stratospheric GW activity may be considered a useful proxy for identifying hurricane intensification.

The Interaction between African Easterly Waves and Different Types of Deep Convection and Its Influence on Atlantic Tropical Cyclones

This study revisited the association of African easterly waves (AEWs) to Atlantic tropical cyclone (TC) development using weather states (WSs) from the International Satellite Cloud Climatology Project, National Hurricane Center best track hurricane data (HURDAT2), and reanalysis products. The WS data are used as a proxy for two different types of deep convection. This study covers July–October 1984–2009. Statistical analysis based on HURDAT2 and objectively tracked AEWs has shown that a small fraction (~20%) of the AEWs that propagate from Africa serve as TC precursors. About 80% of the AEWs from the continent were non-developing. As in the past work, our study showed an important difference between developing and non-developing AEWs. Composites based on developing AEWs revealed well-organized large scale deep convection (one type, composed of mesoscale systems and thick anvil clouds) is tightly coupled to the AEW trough, while scattered, less well-organized deep convection (second type, isolated cumulonimbus and cumulus congestus clouds) dominated a large area downstream of the developing AEW trough. Developing AEWs propagate westwards while strengthening. In contrast, non-developing AEWs showed that the peak well-organized deep convection is located either behind (to the east of) or far ahead (to the west) of the AEW trough (peaks values are not in close proximity). Moreover, well-organized deep convections associated with non-developing AEWs were weaker than those associated with developing AEWs. The results indicated that convective activity ahead of the non-developing AEWs is weak. Positive relative humidity (RH) anomalies dominate the area around AEWs and downstream over the main TC development region. In contrast, negative RH dominated the main TC development region ahead of non-developing AEWs, suggesting an unfavorable environment downstream of the AEWs. The results also showed that developing AEWs maintained stronger features in the lower and middle troposphere, while non-developing AEWs exhibited weaker structures, in agreement with past work.

Question answering method for infrastructure damage information retrieval from textual data using bidirectional encoder representations from transformers

Manual searching for infrastructure damage information from large amounts of textual data requires considerable time and effort. A fast and accurate collection of damage information from such data is necessary for effective infrastructure planning. In this study, a question answering method was proposed to provide users with infrastructure damage information from textual data automatically. The proposed method relies on a natural language model called bidirectional encoder representations from transformers for information retrieval. From the 143 reports collected from the National Hurricane Center, 533 question-answer pairs were formulated. The proposed model was trained with 435 pairs and tested with the remainder. The model was also tested with 43 question-answer pairs created using earthquake-related textual data and achieved F1-scores of 90.5% and 83.6% for the hurricane and earthquake datasets, respectively.

Probabilistic Storm Surge Estimation for Landfalling Hurricanes: Advancements in Computational Efficiency Using Quasi-Monte Carlo Techniques

During landfalling tropical storms, predictions of the expected storm surge are critical for guiding evacuation and emergency response/preparedness decisions, both at regional and national levels. Forecast errors related to storm track, intensity, and size impact these predictions and, thus, should be explicitly accounted for. The Probabilistic tropical storm Surge (P-Surge) model is the established approach from the National Weather Service (NWS) to achieve this objective. Historical forecast errors are utilized to specify probability distribution functions for different storm features, quantifying, ultimately, the uncertainty in the National Hurricane Center advisories. Surge statistics are estimated by using the predictions across a storm ensemble generated by sampling features from the aforementioned probability distribution functions. P-Surge relies, currently, on a full factorial sampling scheme to create this storm ensemble, combining representative values for each of the storm features. This work investigates an alternative formulation that can be viewed as a seamless extension to the current NHC framework, adopting a quasi-Monte Carlo (QMC) sampling implementation with ultimate goal to reduce the computational burden and provide surge predictions with the same degree of statistical reliability, while using a smaller number of sample storms. The definition of forecast errors adopted here directly follows published NWS practices, while different uncertainty levels are considered in the examined case studies, in order to offer a comprehensive validation. This validation, considering different historical storms, clearly demonstrates the advantages QMC can offer.

Pulmonary nontuberculous mycobacterial disease in Florida and association with large-scale natural disasters

BackgroundLittle is known about the impact of the ecosystem disruption and its contribution on the non-tuberculosis mycobacteria (NTM) diseases (cases) rate in Florida (FL), a state with a high prevalence of NTM in the United States. We aimed to evaluate the epidemiological distribution of NTM in FL and identify its association with extreme weather events.MethodsWe used OneFlorida Clinical Research Consortium dataset and extracted data on NTM cases using ICD codes 9- CM 031.0 and ICD-10 A31 during 2012–2018. The number of hurricanes during the study period which affected FL were extracted data from the National Hurricane Center (NHC) and the National Oceanic and Atmospheric Administration (NOAA).ResultsPrevalence of NTM gradually increased during the study period. The rate was 2012: 14.3/100,000, 2015; 20.1/100,000 and 2018; 22.6/100,00 except in 2014 where there was an 8% decrease. The incidences were 2012; 6.5/100,00, 2015; 4.9/100,000 and in 2015; 5.4/100,000. Geographical analysis demonstrated a gradual expansion of the NTM cases in Alachua, and Marion Counties throughout the study period. Notably, the 2018 heat map showed higher prevalence of NTM in the northwestern, panhandle region of FL which had been absent in the heat maps for years 2012–2018. High number of the hurricanes was associated with the higher number of the new cases of NTM infection for years 2012, 2016–2018, while the lower number of the hurricanes was associated with the lower number of the new cases of NTM infection for years 2014–2015.ConclusionThe current study found the prevalence rates of NTM disease in FL rose from 2012 to 2018. A higher prevalence was seen following the hurricanes.

Communicating Probability Information in Hurricane Forecasts: Assessing Statements that Forecasters Use on Social Media and Implications for Public Assessments of Reliability

Forecasters are responsible for predicting the weather and communicating risk with stakeholders and members of the public. This study investigates the statements that forecasters use to communicate probability information in hurricane forecasts and the impact these statements may have on how members of the public evaluate forecast reliability. We use messages on Twitter to descriptively analyze probability statements in forecasts leading up to Hurricanes Harvey, Irma, Maria, and Florence from forecasters in three different groups: the National Hurricane Center, local Weather Forecast Offices, and in the television broadcast community. We then use data from a representative survey of United States adults to assess how members of the public wish to receive probability information and the impact of information format on assessments of forecast reliability. Results from the descriptive analysis indicate forecasters overwhelmingly use words and phrases in place of numbers to communicate probability information. In addition, the words and phrases forecasters use are generally vague in nature -- they seldom include rank adjectives (e.g., “low” or “high”) to qualify blanket expressions of uncertainty (e.g., “there is a chance of flooding”). Results from the survey show members of the public generally prefer both words/phrases and numbers when receiving forecast information. They also show information format affects public judgments of forecast reliability; on average, people believe forecasts are more reliable when they include numeric probability information.

Comparison of ARCHER MPERC to NHC Analysis

Eyewall replacement cycles (ERCs) are events that occur in intense tropical cyclones (TCs) and are difficult to predict. An ERC event involves a secondary outer eyewall that surrounds the inner eyewall. The outer eyewall slowly moves towards the eye and weakens the inner eyewall, eventually replacing the inner eyewall. During this process, wind speeds lower and the structure of a TC becomes disorganized, further weakening the storm. TCs often restrengthen after an ERC. Little is known about the process and as such, poses an obstacle to forecasters. The Automated Rotational Center Hurricane Eye Retrieval (ARCHER) Microwave-based Probability of Eyewall Replacement Cycle (MPERC) is an algorithm that uses 89-95 GHz passive microwave imagery and intensity estimates from the National Hurricane Center (NHC), Central Pacific Hurricane Center (CPHC), or the Joint Typhoon Warning Center (JTWC) to predict the possibility of an ERC. The effectiveness and ability of ARCHER MPERC was analyzed and compared to the NHC’s official reports on all Atlantic Basin tropical cyclones from 2017 to 2019. MPERC ultimately predicted seventeen ERCs in nine tropical cyclones. Of those, seven were valid ERCs. The algorithm works well, predicting approximately 41% of the total number of predictions correctly. However, MPERC did not predict five ERCs that were cited by the NHC. It was further found that it was true that MPERC produces incorrect results in sheared and dry environments.

End-user Preference for and Understanding of Hurricane Forecast Graphs

Hurricane forecast graphics have the challenging task of communicating information about spatio-temporal uncertainty. This study assesses the impact of graph literacy and graph format on user preference and understanding. In a laboratory setting, we compared user responses to official National Hurricane Center advisory maps and alternative visualizations. Results indicate that prior experience with a visualization drives preference and that graph literacy, visualization format, and tropical cyclone characteristics, in combination, influence interpretations of hurricane forecast track. The findings from this study are expected to inform redesign efforts of hurricane risk communication products.

Portability of semantic and spatial\u2013temporal machine learning methods to analyse social media for near-real-time disaster monitoring

Up-to-date information about an emergency is crucial for effective disaster management. However, severe restrictions impede the creation of spatiotemporal information by current remote sensing-based monitoring systems, especially at the beginning of a disaster. Multiple publications have shown promising results in complementing monitoring systems through spatiotemporal information extracted from social media data. However, various monitoring system criteria, such as near-real-time capabilities or applicability for different disaster types and use cases, have not yet been addressed. This paper presents an improved version of a recently proposed methodology to identify disaster-impacted areas (hot spots and cold spots) by combining semantic and geospatial machine learning methods. The process of identifying impacted areas is automated using semi-supervised topic models for various kinds of natural disasters. We validated the portability of our approach through experiments with multiple natural disasters and disaster types with differing characteristics, whereby one use case served to prove the near-real-time capability of our approach. We demonstrated the validity of the produced information by comparing the results with official authority datasets provided by the United States Geological Survey and the National Hurricane Centre. The validation shows that our approach produces reliable results that match the official authority datasets. Furthermore, the analysis result values are shown and compared to the outputs of the remote sensing-based Copernicus Emergency Management Service. The information derived from different sources can thus be considered to reliably detect disaster-impacted areas that were not detected by the Copernicus Emergency Management Service, particularly in densely populated cities.

Tropical Cyclone Center Positioning Using Single Channel Microwave Satellite Observations of Brightness Temperature

Satellite observations of brightness temperature from the Advanced Technology Microwave Sounder (ATMS) and Microwave Humidity Sounder (MHS) humidity sounding channels can provide relatively high horizontal resolution information about cloud and atmospheric moisture in the troposphere, thus revealing the structures of tropical cyclones (TCs). There is usually a high brightness temperature in a TC eye region and low brightness temperature reflecting spiral rain bands. An azimuthal spectral analysis method is used as a center-fixing algorithm to determine the TC center objectively using the brightness temperature observations of the ATMS humidity-sounding channel 18 (183.31 ± 7.0 GHz) and MHS humidity-sounding channel 5 (190.31 GHz). The position in the brightness temperature field encompassing a TC that achieves the largest symmetric component is regarded as the TC center. Two Atlantic hurricanes in 2012, Hurricanes Sandy and Isaac, are first used to analyze the performance of the TC center-fixing technique. Compared with the National Hurricane Center best track, the root-mean-square differences of the center fixing results for Hurricanes Sandy and Isaac are less than 47.3 and 34.3 km, respectively. It is found that the uncertainty of the TC center-fixing algorithm and thus the difference from the best track increases when the brightness temperature distribution within a TC is significantly asymmetric. Then, the TC center-fixing technique is validated for all tropical storms and hurricanes over Northern Atlantic and Western Pacific in 2019. Compared with the best track data, the root-mean-square differences for tropical storms and hurricanes are 33.81 and 26.20 km, respectively. The demonstrated successful performance of the proposed TC center-fixing algorithm to use the single channel of microwave humidity sounders for TC positioning is important for vortex initialization in operational hurricane forecasts.

The lasting impacts of the Saffir-Simpson Hurricane Wind Scale on storm surge risk communication: The need for multidisciplinary research in addressing a multidisciplinary challenge

Historically, storm surge risk was indicated alongside the Saffir-Simpson Hurricane Wind Scale (SSHWS), and a lasting association remains among the general public. In recent years, the National Hurricane Center (NHC) has continually developed their advisories and products to explicitly communicate the storm surge hazard. In this study, we examine these developments to understand its impact on risk perception and discuss that though not explicitly, underlying associations to the SSHWS remain. We also find that despite the messaging produced through official storm surge guidance, the SSHWS continues to influence public perception and response. Our findings suggest that while improvements in storm surge messaging can improve risk communication and perception and ultimately save lives, it only addresses one component of the multidisciplinary system that defines storm surge risk. Truly effective resilience efforts will require comprehensive, multidisciplinary approaches.

Operational Forecasting of Tropical Cyclone Rapid Intensification at the National Hurricane Center

Although some recent progress has been made in operational tropical cyclone (TC) intensity forecasting, the prediction of rapid intensification (RI) remains a challenging problem. To document RI forecast progress, deterministic and probabilistic operational intensity models used by the National Hurricane Center (NHC) are briefly reviewed. Results show that none of the deterministic models had RI utility from 1991 to about 2015 due to very low probability of detection, very high false alarm ratio, or both. Some ability to forecast RI has emerged since 2015, with dynamical models being the best guidance for the Atlantic and statistical models the best RI guidance for the eastern North Pacific. The first probabilistic RI guidance became available in 2001, with several upgrades since then leading to modest skill in recent years. A tool introduced in 2018 (DTOPS) is currently the most skillful among NHC’s probabilistic RI guidance. To measure programmatic progress in forecasting RI, the Hurricane Forecast Improvement Program has introduced a new RI metric that uses the traditional mean absolute error but restricts the sample to only those cases where RI occurred in the verifying best track or was forecast. By this metric, RI forecasts have improved by ~20–25% since the 2015–2017 baseline period.

Predictive Agent-Based Modeling of Natural Disasters Using Machine Learning

Current applications of Agent-based Modeling (ABM) in natural phenomena like wildfire land suppression and hurricane forecasting are in monitoring emergent behavior patterns among large groups of people (Hilljegerdes 2018). However, current evacuation times and plans for natural disaster management leave underserved communities vulnerable to substantial financial and welfare losses, especially when false positives during current predictions continue to influence evacuation decisions. A Machine Learning ABM (ML-ABM) model of hurricane trajectories introduces a pioneering opportunity to capture complex physical processes associated with hurricanes while minimizing computational costs and errors, thereby providing more accurate real-time prediction of hurricanes for improved disaster management. This Hurricane Track Prediction ML-ABM model aims to quickly model and predict hurricane tracks in only a few minutes while retaining some of the complex physical process interactions of real storms through feature engineering and deep learning. This work focuses on the implementation of an RNN with bidirectional time-distributed Long-Short Term Memory cells, accounting for positive and negative time direction in time series forecasting. The observations and predictions were represented as a multi-agent system in NetLogo for further emergent pattern analysis in an expanded research by Arthur Drake et. al (2020). The model also evaluates benchmark comparisons against the National Hurricane Center’s 5-Year Average Forecast Errors and the BCD5 Error Model, a combined intensity and track prediction error model that utilizes best track input and models decay over land.

Detecting tropical cyclones from climate- and oscillation-free tree-ring width chronology of longleaf pine in south-central Georgia

The knowledge of past tropical cyclone (TC) activity is vital to understanding patterns of current and future TCs and how they will impact society, infrastructure, and the natural system. Various historical, biological, and geological proxies are commonly used to reconstruct TC behavior; however, these records vary significantly in their temporal resolution. Tree rings are known to provide high-resolution data and have been proven as a valuable proxy record of past TC activity. Here we aim to produce a TC reconstruction based on tree-ring width data of longleaf pine (Pinus palustris Mill.) located in Lake Louise near Valdosta, Georgia. Results of growth-climate analysis showed that all climate and oscillation variables influence the tree growth, and removing this climate influence may be necessary to unmask the TC signal. For this purpose, we used stepwise linear regression to iteratively model tree growth with monthly climate factors (precipitation, temperature, drought) and monthly oscillation indices (ENSO, AMO, NAO, PDO) to obtain residual chronologies. Tree-ring chronologies were compared to TC data from the National Hurricane Center's North Atlantic Hurricane Database (HURDAT2) for a 150-km buffer zone around Lake Louise between 1894 and 1999. Of the storms that entered the buffer, 13 of the 17 ≤−0.3 growth rings and 8 of the 10 ≤−0.4 growth rings occurred in the year directly after a TC event. Our results revealed the strong climate and oscillation influence in the raw chronology and no significant TC signal. However, the removal of temperature and precipitation influence from raw chronology unmasked the TC signal (p < 0.05). The TC signal was even more pronounced if influence of oscillation indices on growth had been also removed. The most pronounced TC signal was identified between most intense TCs (≥ 33 ms−1) and the climate and oscillation signal-free chronology. We emphasize that future research is needed for validation and elimination of the influence of other external growth drivers, as well as replication studies at multiple sites using similar statistical applications.

In the eye of the storm

AbstractThe airborne NOAA Wide Swath Radar Altimeter (WSRA) is a 16 GHz digital beamforming radar altimeter that produces a topographic map of the waves as the aircraft advances. The wave topography is transformed by a two-dimensional FFT into directional wave spectra. The WSRA operates unattended on the aircraft and provides continuous real-time reporting of several data products: (1) significant wave height, (2) directional ocean wave spectra, (3) the wave height, wavelength, and direction of propagation of the primary and secondary wave fields, (4) rainfall rate and (5) sea surface mean square slope (mss). During hurricane flights the data products are transmitted in real-time from the NOAA WP-3D aircraft through a satellite data link to a ground station and on to the National Hurricane Center (NHC) for use by the forecasters for intensity projections and incorporation in hurricane wave models. The WSRA is the only instrument that can quickly provide high-density measurements of the complex wave topography over a large area surrounding the eye of the storm.

The Combined Effects of SST and the North Atlantic Subtropical High-Pressure System on the Atlantic Basin Tropical Cyclone Interannual Variability

The combined effect of the sea surface temperature (SST) and the North Atlantic subtropical high-pressure system (NASH) in the interannual variability of the genesis of tropical cyclones (TCs) and landfalling in the period 1980–2019 is explored in this study. The SST was extracted from the Centennial Time Scale dataset from the National Oceanic and Atmospheric Administration (NOAA), and TC records were obtained from the Atlantic Hurricane Database of the NOAA/National Hurricane Center. The genesis and landfalling regions were objectively clustered for this analysis. Seven regions of TC genesis and five for landfalling were identified. Intercluster differences were observed in the monthly frequency distribution and annual variability, both for genesis and landfalling. From the generalized least square multiple regression model, SST and NASH (intensity and position) covariates can explain 22.7% of the variance of the frequency of TC genesis, but it is only statistically significant (p &lt; 0.1) for the NASH center latitude. The SST mostly modulates the frequency of TCs formed near the West African coast, and the NASH latitudinal variation affects those originated in the Lesser Antilles arc. For landfalling, both covariates explain 38.7% of the variance; however, significant differences are observed in the comparison between each region. With a statistical significance higher than 90%, SST and NASH explain 33.4% of the landfalling variability in the archipelago of the Bahamas and central–eastern region of Cuba. Besides, landfalls in the Gulf of Mexico and Central America seem to be modulated by SST. It was also found there was no statistically significant relationship between the frequency of genesis and landfalling with the NASH intensity. However, the NASH structure modulates the probability density of the TCs trajectory that make landfall once or several times in their lifetime. Thus, the NASH variability throughout a hurricane season affects the TCs trajectory in the North Atlantic basin. Moreover, we found that the landfalling frequency of TCs formed near the West Africa coast and the central North Atlantic is relatively low. Furthermore, the SST and NASH longitude center explains 31.6% (p &lt; 0.05) of the variance of the landfalling intensity in the archipelago of the Bahamas, while the SST explains 26.4% (p &lt; 0.05) in Central America. Furthermore, the 5-year moving average filter revealed decadal and multidecadal variability in both genesis and landfalling by region. Our findings confirm the complexity of the atmospheric processes involved in the TC genesis and landfalling.

Parametric study of hurricanes intensity as they move over land

Two of the most important tropical cyclone parameters, which are used as a measure of their intensity, are the maximum surface wind speed (local parameter) and the pressure difference between the free atmosphere and the pressure in the center of the storm (global parameter). In this paper we reexamined the relationship between these parameters via the Euler number, which is a nondimensional parameter that relates the overall pressure difference to the dynamic pressure generated by the maximum value of the wind speed. The calculations were performed all along the track of 20 tropical cyclones using the National Hurricane Center’s extended best track database for the Atlantic basin. It was found that when the cyclone is over water, the Euler number tends to remain constant, while over land the parameter varies drastically with a tendency to grow. These results confirm that over sea, when they have reached hurricane strength, tropical cyclones evolve slowly remaining near the steady state, highlighting the importance of transient states during strong dissipation. The Euler number seems to be independent of the global warming.

The NASA‐JAXA Global Precipitation Measurement mission – part II: New frontiers in precipitation science

The <scp>NASA‐JAXA</scp> Global Precipitation Measurement mission – part <scp>II</scp>: New frontiers in precipitation science

Comparison of Tropical Cyclone Center Positions Determined from Satellite Observations at Infrared and Microwave Frequencies

AbstractDetermining tropical cyclone (TC) center positions is of interest to many researchers who conduct TC analysis and forecasts. In this study, we develop and apply a TC centering technique to Cross-Track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) observations of brightness temperature and report on an improvement of accuracy by adding a TC spectral analysis to the state of the art [Automated Rotational Center Hurricane Eye Retrieval (ARCHER)], especially for ATMS. We show that the ARCHER TC center-fixing algorithm locates TC centers more successfully based on the infrared channel with center frequency at 703.75 cm−1 (channel 89) of the CrIS than the ATMS channel 22 (183.31 ± 1.0 GHz) due to small-scale features in ATMS channel’s brightness temperature field associated with strong convective clouds. We propose to first apply the ARCHER TC center-fixing algorithm to ATMS channel 4 (51.76 GHz) that is less affected by small-scale convective clouds, and then to perform a set of the azimuthal spectral analysis of the ATMS channel-22 observations with tryout centers within a squared box centered at the ATMS channel-4-determined center. The center that gives the largest symmetric component is the final ATMS-determined center. Compared to the National Hurricane Center best track, the root-mean-square center-fixing errors determined from the two ATMS channels (one single CrIS channel) are 29.9 km (35.8 km) and 28.0 km (30.9 km) for 104 tropical storm and 81 hurricane cases, respectively, in the 2019 hurricane season.

Climatology of Tropical Cyclone Strikes along the Southeastern Coastline of the United States

Lindner, B.L.; Holden, W.; Neuhauser, A., and Evsich, R., 2020. Climatology of tropical cyclone strikes along the southeastern coastline of the United States. Journal of Coastal Research, 36(6), 1162–1177. Coconut Creek (Florida), ISSN 0749-0208.It has been theorized that tropical cyclones originating in or passing through the Gulf of Mexico (hereafter referred to as GOM tropical cyclones) may significantly impact communities along the Atlantic coast of the southeastern United States. To explore this hypothesis, site-specific climatologies were compiled using National Hurricane Center records of tropical cyclones that passed within 139 km of either Savannah, Georgia, or Wilmington, North Carolina, during the years 1851–2018. Return periods for tropical cyclones are longer for Savannah than for Wilmington, particularly for intense hurricanes. Intense GOM hurricanes are weakened by land interaction, which would result in longer return periods. A secondary maximum in the number of tropical storms early in hurricane season is more pronounced with proximity to the Gulf of Mexico, which is again consistent with the contribution from GOM tropical cyclones. Moreover, the percentage of tropical cyclones that passed near Savannah but did not make landfall is higher than that for Wilmington, again an indication of the significance of GOM tropical cyclones. Further evidence of the influence of GOM tropical cyclones is seen in the difference in approach angle and translational velocity between tropical storms and hurricanes. In addition, translational velocities for tropical cyclones increase with latitude, and translational velocities for tropical cyclones near either Savannah or Wilmington increase as the hurricane season progresses. Both relationships are likely due to the interaction of tropical cyclones with synoptic and planetary-scale winds. The median date for tropical cyclones has shifted earlier in recent decades for both Savannah and Wilmington, which is potentially an indication of climate change. An improved understanding of the climatology of tropical cyclones could lead to enhanced city planning, building codes, infrastructure, and resource management.