Hurricane Database Research Articles

Spatiotemporal Climatology of Georgia Tropical Cyclones and Associated Rainfall

Tropical cyclones (TCs), often characterized by high wind speeds and heavy rainfall, cause widespread devastation, affecting millions of people and leading to economic losses worldwide. TC-specific research in Georgia is scarce, likely due to the minimal geographical extent of its coast and the infrequency of direct landfalls. Research on Georgia TCs does not account for storms that make landfall in other southeastern states (e.g., Florida) and continue north, northeast, or northwest into Georgia. This study used the North Atlantic Basin hurricane database (HURDAT2) to quantify the spatiotemporal patterns of direct and indirect landfalling of Georgia tropical cyclones (>16 ms−1) from 1851 to 2021. TC-induced rainfall was also quantified using rainfall data (nClimGrid-Daily and nClimGrid) from 1951 to 2021 to estimate the proportion of Georgia’s total annual and monthly rainfall attributed to TCs. A multi-methodological approach, incorporating statistics and mapping, is employed to assess the trends of Georgia’s tropical cyclones and the associated rainfall. The study analyzed 113 TCs and found that, on average, less than one TC annually (x¯ = 0.66) traverses the state. September averaged the highest percentage (25%) of TC-induced rainfall, followed by October (14%), and August (13%). This pattern aligns with the TC season, with the highest frequency of TCs occurring in September (n = 35), followed by August (n = 25), and October (n = 18). We found that 10% of tropical storms make landfall on the coastline, while the remaining 91% enter Georgia by making landfall in Florida (92%), Louisiana (7%), or South Carolina (1%) first. A threat of TCs during the peak of the season emphasizes the importance of heightened awareness, increased planning practices, and resource allocation during these periods to protect Georgia’s history and natural beauty, and its residents.

Beta‐diversity within coral atolls: Terrestrial species turnover increases with cyclone frequencies

AbstractAimAtolls are a widely distributed, common type of tropical ecosystem, each consisting of an annular coral reef and up to several hundred individual islets sitting on the reef platform. The small land areas and low elevation render the terrestrial communities susceptible to local extinctions from overwash and inundation due to tropical cyclones. Such recurring catastrophic disturbances should be expected to drive strong priority effects and historical contingency in species assembly, thus also promoting species turnover within atolls. This stands contradictory to the received wisdom of atolls consisting of numerous uniform, replicated island systems. We tested the hypothesis that the individual islets within an atoll exhibit compositional turnover and that this species turnover within atolls is related to cyclone frequency.LocationAtolls worldwide.Time PeriodPresent.Taxa StudiedVascular plants, reptiles, birds.MethodsWe compiled a global dataset at the level of islets for species presence/absence within atolls. We obtained long‐term (80 year) tropical cyclone frequency data for each atoll from the NOAA hurricane database and used Bayesian regression to estimate the effects of tropical cyclone frequency on species turnover within atolls.ResultsWe consistently measured high within‐atoll species turnover (i.e., between the individual islets within an atoll) on atolls worldwide. The degree of within‐atoll species turnover increases with increasing tropical cyclone frequency. Atolls that are more frequently hit by tropical cyclones show higher species turnover between their islets than those that occur outside the tropical cyclone belt.Main ConclusionsTropical cyclones are a significant driver of the community assemblages on atolls. These catastrophic disturbances promote a heterogenous atoll landscape, which challenges perceptions that the islets within an atoll are identical replicates of the entire atoll terrestrial community. Biodiversity surveys undertaken at the islet‐level (alpha‐diversity) might therefore not be representative of the entire atoll metacommunity (gamma) diversity.

A two-stage stochastic model for intermodal terminal location and freight distribution under facility disruptions

ABSTRACT A two-stage stochastic model is developed for intermodal facility location and freight distribution under random disruptions at shipper facilities and/or intermodal terminals (IMTs). The magnitude of the disruption and the impacted locations are uncertain parameters. A two-stage stochastic programming model is used to address supply uncertainty at shippers and throughput capacity uncertainty at IMTs. A level-method based decomposition approach and the L-shaped method are used to solve the model. The state of South Carolina in the U.S.A. is used as a case study with the goal of determining the set of IMT locations that minimise the total long-run network costs due to hurricane disruptions. A methodology is developed to generate realistic scenarios. The Freight Analysis Framework Version 4.5 data set is used to generate demands and supply, and k-means clustering is used with the Hurricane database (HURDAT2) to generate hurricane disruption scenarios. Sensitivity analyses are performed by varying the disruption probabilities, disruption duration, and direct shipping cost parameters. The results indicate that as disruptions increase, less disrupted intermodal facilities are opened. Also, as direct shipping costs increase, the long-term savings increase non-linearly for all magnitudes of disruptions.

Tropical Cyclone Detection from the Thermal Infrared Sensor IASI Data Using the Deep Learning Model YOLOv3

Tropical cyclone (TC) detection is essential to mitigate natural disasters, as TCs can cause significant damage to life, infrastructure and economy. In this study, we applied the deep learning object detection model YOLOv3 to detect TCs in the North Atlantic Basin, using data from the Thermal InfraRed (TIR) Atmospheric Sounding Interferometer (IASI) onboard the Metop satellites. IASI measures the outgoing TIR radiation of the Earth-Atmosphere. For the first time, we provide a proof of concept of the possibility of constructing images required by YOLOv3 from a TIR remote sensor that is not an imager. We constructed a dataset by selecting 50 IASI radiance channels and using them to create images, which we labeled by constructing bounding boxes around TCs using the hurricane database HURDAT2. We trained the YOLOv3 on two settings, first with three “best” selected channels, then using an autoencoder to exploit all 50 channels. We assessed its performance with the Average Precision (AP) metric at two different intersection over union (IoU) thresholds (0.1 and 0.5). The model achieved promising results with AP at IoU threshold 0.1 of 78.31%. Lower performance was achieved with IoU threshold 0.5 (31.05%), showing the model lacks precision regarding the size and position of the predicted boxes. Despite that, we show YOLOv3 demonstrates great potential for TC detection using TIR instruments data.

A Statistical Analysis and Exploration on Atlantic Hurricanes Database (HURDAT2); Do We Expect the Worst?

Hurricane occurrence exploration is a heavy task that requires sophisticated methods to accurately determine the occurrence and the impact of damage on the tropical environs. To control massive damage, several techniques have been developed to measure the accuracy of track forecasting. The accurate analysis and exploration of the occurrence of hurricanes are very crucial for the affected environs evaluation within a short time to minimize the loss of human life and property. Nevertheless, the exact analysis and exploration of these hurricanes is challenging and time-consuming. Therefore, this study proposed a statistical model whereby the analysis of variance (ANOVA) a linear regression scientific data statistical analysis model is applied on the dataset of Atlantic hurricane database (HURDAT2) for the exploration of hurricane occurrence and damage in the tropical environs. The best tracks of a 6 hour interval, location (latitude, and longitude), wind speed, central pressure of all identified hurricanes and subtropical typhoons from the year 2008 to 2017 (10 year period) are used to determine the R2 coefficient, which measures the goodness and fitness of the model to reveal the variability of the real data. Statistical significance and reliability of the data are tested on significance P-value <0.05 where four different parameters are considered for the analysis in order to determine the destructive of these hurricanes in the tropical environs; latitude (x1), longitude (x2), the wind speed (x3), and central pressure. The results of our model proved significant with an accuracy of 99.3%, and a mean standard error (MSE) of 1.4952 for all the hurricanes that were analyzed, and the year 2012 was established as a year that had much damage.

What Governs the Interannual Variability of Recurving North Atlantic Tropical Cyclones?

Abstract Recurving tropical cyclones (TCs) can cause extensive damage along the U.S. East Coast and later in their life cycle over Europe as post-tropical cyclones. While the existing literature attempts to understand the drivers of basinwide and regional TC variability, less work has been undertaken looking at recurving TCs. The roles played by the interannual variabilities of TC frequency and the steering flow in governing recurving TC interannual variability are investigated in this study. Using a track-matching algorithm, we identify observed TC tracks from the NHC “best track” hurricane database, version 2 (HURDAT2) in the ERA5 and MERRA2 reanalyses. This allows for detailed analysis of the post-tropical stages of the tracks in the observational TC record, enabling robust identification and separation of TCs that recurve. We show that over 75% of the interannual variance in annual recurving TC frequency can be explained by just two predictors—the frequency of TCs forming in the subtropical Atlantic, and hurricanes (TCs with wind speeds > 33 m s−1) forming in the main development region (MDR). An index describing the seasonal mean meridional steering flow shows a weak, nonsignificant relationship with recurving TC frequency, supported by composite analysis. These results show that the interannual variability in recurving TC frequency is primarily driven by the seasonal TC activity of the MDR and the subtropical Atlantic, with seasonal anomalies in the steering flow playing a much smaller, secondary role. These results help to quantify the extent to which skillful seasonal forecasts of Atlantic hurricane activity benefit regions vulnerable to recurving TCs. Significance Statement Recurving tropical cyclones (TCs) can cause extensive damage to the U.S. East Coast, eastern Canada, and Europe. It is, therefore, crucial to understand why some years have a higher frequency of recurving TCs than other years. In this study, we show that the frequency of recurving TCs is very strongly linked to the frequency that hurricanes (TCs with wind speeds > 33 m s−1) form in the main development region, and the frequency that TCs form in the subtropical Atlantic. This result suggests that skillful seasonal prediction of hurricane activity could be used to give enhanced seasonal warning to the regions often impacted by recurving TCs.

Estimation of Affected Customers and Load Loss Under Wind Storms in the Caribbean Region

The disruption of extreme weather events can significantly impact the operation of nation’s critical infrastructures. With the growing trend of extreme weather events in the Caribbean region, evaluating hazard effects of wind storms on power distribution systems becomes increasingly important for disaster preparedness and fast responses in utilities. These catastrophic events disable critical paths of electricity overhead infrastructure, resulting in extended power outages and loss of critical services. In this article, probabilistic wind storm models for the study region have been built by mining the 169-year storm events recorded in the Hurricane Database from the National Hurricane Center of the National Oceanic and Atmospheric Administration. In addition, a simulation approach is presented to evaluate the impacts of wind storms over the power distribution system under different levels of hazards using a random storm trajectory generator and geographic information system data. Extensive studies for the Puerto Rico’s electrical distribution system indicate that our models and procedure effectively estimate the number of affected customers and load loss due to a wind storm impact.

Unraveling the Relationship Between Tropical Storms and Agricultural Drought

AbstractDrought and tropical storm (TS) are associated with water deficit and surplus, respectively. Soil moisture is a key component in the hydrological cycle that plays an important role in monitoring drought and reflects the infiltrated or stored water due to TS rainfall. Therefore, soil moisture information can be used for the assessment of whether TS can ameliorate severe drought conditions. Here, we use downscaled 1 km Soil Moisture Active Passive data set generated at the Center for Complex Hydrosystems Research at the University of Alabama, and Hurricane Database 2nd generation to examine the coincidence of extreme events between agricultural droughts and Atlantic TS in the contiguous United States from 2015 to 2019. The Cumulative Distribution Function (CDF) matching approach is employed to correct the bias using the root zone soil moisture data provided by the North American Land Data Assimilation System Phase 2 (NLDAS‐2), and then weekly Standardized Soil Moisture Index is calculated for characterization of agricultural drought. As a result, we estimate the frequency of TS impacted regions in the US, the ratio of droughts ameliorated and exacerbated by TS, and the regions where TS highly affect the offset of drought. Our findings indicate detailed spatial information of the offset of drought conditions based on a high‐resolution data set and provide potential information in terms of mitigating drought and TS for the future.

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.

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.

The Great Havana Hurricane of 1846: A Reconstruction of the Storm’s Track, Intensity, and Impacts

This article reconstructed the track, intensity, and societal impacts of the Great Havana Hurricane of October 1846, using all available historical data, which include ship logs, newspapers, diaries, and early instrumental records. Most of the data were extracted from original manuscripts at historical libraries and repositories. Meteorological aspects of the hurricane were analyzed by mapping twice-daily surface synoptic weather maps from geographic information systems methods, estimating central pressures from known wind–pressure relationships derived from modern hurricane studies, and assessing intensity based on damage descriptions from the Saffir-Simpson Hurricane Wind Scale and an inland decay model. The storm was found to be much stronger than previously known. In this study, we clearly define the first known record of a landfalling Category 5 hurricane in the Atlantic Basin, which likely ranks among the top of all those known in the modern (1851–present) Atlantic Basin official hurricane database. Although this storm has been previously referred to as one of the most destructive hurricanes in the history of Cuba, the impacts of the Great Havana Hurricane actually spanned well beyond the Caribbean, tracking from Florida through many major populated cities along the East Coast and into Atlantic Canada. Clearly, such a storm today, with much larger metropolitan areas, would have caused enormous economic damage and should be anticipated in long-term hurricane mitigation, zoning, and worst-case scenarios. This study highlights the importance of examining historical documentary sources for extreme events, providing a framework on how to research case studies of premodern Atlantic hurricanes back in time.

Projecting the effects of a warming climate on the hurricane hazard and insured losses: Methodology and case study

This paper summarizes research (conducted previously by the author) to incorporate (1) sea-surface temperature projections, (2) event-based hurricane simulation models, and (3) loss models to assess the impacts of the changing climate on the hurricane hazard and estimated insured losses. The focus of the work is on the US east coast and hence the formation and movement of hurricanes over the Atlantic Basin. As an illustrative case, the area around Charleston, South Carolina is considered. Specifically, this paper presents a methodology to probabilistically estimate regional hurricane loss considering both hurricane intensity and size as well as projected sea surface temperature change as a function of climate change.It is most common to characterize the hurricane wind hazard using a measure of intensity only. Recent studies have demonstrated the importance of accounting for storm size when characterizing the hurricane hazard in order to properly estimate/predict the spatial extent of damage. For purposes of regional loss estimation, therefore, considering maximum wind speed only may not be adequate. A storm with larger size but lower intensity might result in more damage and hence greater total loss than a smaller, more intense storm. Here, the maximum wind speed (intensity) and the radius of maximum winds (spatial extent) are selected as the two dominant indicators (parameters) characterizing the hurricane (event) hazard. The resulting bivariate hazard model is then used as input to a regional loss estimation model to illustrate its potential application. Only hurricane wind damage (and associated losses from both wind damage and water intrusion) is considered in this study. Collateral damage due to flood or storm surge is not addressed here.Simulated events impacting this region are extracted from a synthetic hurricane database (developed by the author and his colleagues) and the joint distribution of intensity and size is established to characterize the hurricane hazard. Regional loss for each simulated hurricane event is then estimated using the loss estimation module in HAZUS_MH. Finally, distributions of regional loss at different (event) hazard levels are generated.A future climate scenario (increasing sea surface temperature) is next considered and the joint distribution of hurricane intensity and size and the resulting loss distribution curve is recomputed. The results are then able to be compared to those under the current climate scenario. Such comparisons can help to inform codes and standards committees, for example, as they consider whether and how to take potential climate change impacts (in the design lifetime) into account. They also can inform ongoing public discourse around mitigation and adaptation, whether hardening of buildings and other infrastructure systems, community resilience planning, construction practices, or related policies.

Interactions of tropical cyclones with the Windward and Leeward Islands

Using the Atlantic Hurricane Database (HURDAT2) and the General Bathymetry Chart of the Oceans (GEBCO‐2014), the probability of tropical cyclones of various intensities (hurricane, tropical storm, or a tropical depression) making landfall through the Lesser Antilles (i.e., the Windward and Leeward Islands—from 12.5°N to 17.5°N and 59.0°W to 63.0°W) from 1991 to 2016 was evaluated. HURDAT2 data from 1979 to 2016 also were used to assess storm location, maximum sustained winds, and minimum central pressure as they passed through the Lesser Antilles.In this region, tropical depressions generally did not intensify with respect to maximum sustained wind speeds and only one intensified with respect to a drop in its central pressure. By contrast, hurricanes were more likely to show intensification with respect to maximum sustained wind speed and central pressure. Tropical storms were equally likely to intensify or dissipate. Also, the data indicate that with the passage of a hurricane, maximum sustained winds were less likely to increase despite a drop in the central pressure. For tropical storms and particularly with tropical depressions, the effect of the islands is more pronounced.

Nonlinear analysis of the occurrence of hurricanes in the Gulf of Mexico and the Caribbean Sea

Abstract. Hurricanes are complex systems that carry large amounts of energy. Their impact often produces natural disasters involving the loss of human lives and materials, such as infrastructure, valued at billions of US dollars. However, not everything about hurricanes is negative, as hurricanes are the main source of rainwater for the regions where they develop. This study shows a nonlinear analysis of the time series of the occurrence of hurricanes in the Gulf of Mexico and the Caribbean Sea obtained from 1749 to 2012. The construction of the hurricane time series was carried out based on the hurricane database of the North Atlantic basin hurricane database (HURDAT) and the published historical information. The hurricane time series provides a unique historical record on information about ocean–atmosphere interactions. The Lyapunov exponent indicated that the system presented chaotic dynamics, and the spectral analysis and nonlinear analyses of the time series of the hurricanes showed chaotic edge behavior. One possible explanation for this chaotic edge is the individual chaotic behavior of hurricanes, either by category or individually regardless of their category and their behavior on a regular basis.

Evaluation of weather forecast systems for storm surge modeling in the Chesapeake Bay

Accurate forecast of sea-level heights in coastal areas depends, among other factors, upon a reliable coupling of a meteorological forecast system to a hydrodynamic and wave system. This study evaluates the predictive skills of the coupled circulation and wind-wave model system (ADCIRC+SWAN) for simulating storm tides in the Chesapeake Bay, forced by six different products: (1) Global Forecast System (GFS), (2) Climate Forecast System (CFS) version 2, (3) North American Mesoscale Forecast System (NAM), (4) Rapid Refresh (RAP), (5) European Center for Medium-Range Weather Forecasts (ECMWF), and (6) the Atlantic hurricane database (HURDAT2). This evaluation is based on the hindcasting of four events: Irene (2011), Sandy (2012), Joaquin (2015), and Jonas (2016). By comparing the simulated water levels to observations at 13 monitoring stations, we have found that the ADCIR+SWAN System forced by the following: (1) the HURDAT2-based system exhibited the weakest statistical skills owing to a noteworthy overprediction of the simulated wind speed; (2) the ECMWF, RAP, and NAM products captured the moment of the peak and moderately its magnitude during all storms, with a correlation coefficient ranging between 0.98 and 0.77; (3) the CFS system exhibited the worst averaged root-mean-square difference (excepting HURDAT2); (4) the GFS system (the lowest horizontal resolution product tested) resulted in a clear underprediction of the maximum water elevation. Overall, the simulations forced by NAM and ECMWF systems induced the most accurate results best accuracy to support water level forecasting in the Chesapeake Bay during both tropical and extra-tropical storms.

Characterizing the Variations of the motion of the North Atlantic tropical cyclones

In this study, we examine the seasonal and interannual variability of the North Atlantic (NATL) tropical cyclone (TC) motion from the historical Hurricane Database (HURDAT2) over the period 1988–2014. We characterize these motions based on their position, lifecycle, and seasonal cycle. The main findings of this study include: (1) of the 11,469 NATL TC fixes examined between 1988 and 2014, 81% of them had a translation speed of 40 mph) are exclusively found north of 30 N, the slow-moving TCs have a wide range of latitude. This is largely a consequence of the background steering flow being weaker (stronger) in the tropical (higher) latitudes with a minimum around the subtropical latitudes of NATL; (3) there is an overall decrease in the frequency of all categories of translation speed of TCs in warm relative to cold El Nino Southern Oscillation (ENSO) years. However, in terms of the percentage change, TCs with a translation speed in the range of 10–20 mph display the most change (42%) in warm relative to cold ENSO years; and (4) there is an overall decrease in frequency across all categories of TC translation speed in small relative to large Atlantic Warm Pool years, but in terms of percentage change in the frequency of TCs, there is a significant and comparable change in the frequency over a wider range of translation speeds than the ENSO composites. This last finding suggests that Atlantic Warm Pool variations have a more profound impact on the translation speed of Atlantic TCs than ENSO.

Quantifying the statistical distribution of Tropical Cyclone Activity in the historical record of the Dominican Republic

In this study, we re-examined the Official Hurricane Database from the National Hurricane Center (HURDAT-NHC), an agency associated with NOAA, for tropical cyclone activity from 1851 to 2012for the Dominican Republic on the island of Hispaniola in the Caribbean Basin. We performed analyses at two different levels for the island (i.e., all of the storm tracks in the Caribbean Basin near to the study area that made landfall and all of the events that crossed the Dominican Republic from a radius of 300 km from the coastline). This study includes the statistical occurrence of these phenomena during the study period and the climatological analysis of all tropical cyclone tracks (112 total events) by decadal seasonal distribution, fifty-year seasonal distribution and monthly seasonal distribution to show the lowest and highest activities. We performed wavelet analysis on the continuous data over a long time series to determine the important frequencies. This analysis provided a general statistical conclusion resulting from the data collected. A landfall probability for the study area corresponding to the long time series of (it’s 162) years within a radius of ~100, ~185 and ~300 km, based on the historical climatology tropical cyclone tracks, reveals the likelihood of a strike for a major or a minor hurricane. We present a review of the tropical cyclone activities that passed the Dominican Republic, which also forms part of the author’s dissertation.

Hazard-based regional loss estimation considering hurricane intensity, size and sea surface temperature change

This paper presents a methodology to probabilistically estimate regional hurricane loss considering both hurricane intensity and size, as well as projected sea surface temperature change as a function of climate change. A synthetic hurricane database is generated using established event-based hurricane hazard simulation techniques. The area along the South Carolina coast is selected as the study region. Simulated events impacting this region are extracted from the database and the joint distribution of intensity and size is established to characterize the hurricane hazard. Regional loss for each simulated hurricane event is then estimated using the loss estimation module in HAZUS_MH. Finally, distributions of regional loss at different (event) hazard levels are generated. These may be useful for planners, decision-makers, insurers, and emergency responders. The results from this study also can help to inform codes and standards committees as they consider whether and how to take potential climate change impacts into account.

Reanalysis of climate influences on Atlantic tropical cyclone activity using cluster analysis

AbstractWe analyze, using Poisson regressions, the main climate influences on North Atlantic tropical cyclone activity. The analysis is performed using not only various time series of basin‐wide storm counts but also various series of regional clusters, taking into account shortcomings of the hurricane database through estimates of missing storms. The analysis confirms that tropical cyclones forming in different regions of the Atlantic are susceptible to different climate influences. We also investigate the presence of trends in these various time series, both at the basin‐wide and cluster levels, and show that, even after accounting for possible missing storms, there remains an upward trend in the eastern part of the basin and a downward trend in the western part. Using model selection algorithms, we show that the best model of Atlantic tropical cyclone activity for the recent past is constructed using Atlantic sea surface temperature and upper tropospheric temperature, while for the 1878–2015 period, the chosen covariates are Atlantic sea surface temperature and El Niño–Southern Oscillation. We also note that the presence of these artificial trends can impact the selection of the best covariates. If the underlying series shows an upward trend, then the mean Atlantic sea surface temperature captures both interannual variability and the upward trend, artificial or not. The relative sea surface temperature is chosen instead for stationary counts. Finally, we show that the predictive capability of the statistical models investigated is low for U.S. landfalling hurricanes but can be considerably improved when forecasting combinations of clusters whose hurricanes are most likely to make landfall.

Using Proxy Records to Document Gulf of Mexico Tropical Cyclones from 1820-1915.

Observations of pre-1950 tropical cyclones are sparse due to observational limitations; therefore, the hurricane database HURDAT2 (1851–present) maintained by the National Oceanic and Atmospheric Administration may be incomplete. Here we provide additional documentation for HURDAT2 from historical United States Army fort records (1820–1915) and other archived documents for 28 landfalling tropical cyclones, 20 of which are included in HURDAT2, along the northern Gulf of Mexico coast. One event that occurred in May 1863 is not currently documented in the HURDAT2 database but has been noted in other studies. We identify seven tropical cyclones that occurred before 1851, three of which are potential tropical cyclones. We corroborate the pre-HURDAT2 storms with a tree-ring reconstruction of hurricane impacts from the Florida Keys (1707–2009). Using this information, we suggest landfall locations for the July 1822 hurricane just west of Mobile, Alabama and 1831 hurricane near Last Island, Louisiana on 18 August. Furthermore, we model the probable track of the August 1831 hurricane using the weighted average distance grid method that incorporates historical tropical cyclone tracks to supplement report locations.