Hurricane Landfall Research Articles

Projected increase in the frequency of extremely active Atlantic hurricane seasons.

Future changes to the year-to-year swings between active and inactive North Atlantic tropical cyclone (TC) seasons have received little attention, yet may have great societal implications in areas prone to hurricane landfalls. This work investigates past and future changes in North Atlantic TC activity, focusing on interannual variability and evaluating the contributions from anthropogenic forcing. We show that interannual variability of Atlantic TC activity has already increased, evidenced by an increase in the occurrence of both extremely active and inactive TC seasons. TC-resolving general circulation models project a 36% increase in the variance of North Atlantic TC activity, measured by accumulated cyclone energy, by the middle of the 21st century. These changes are the result of increased variability in vertical wind shear and atmospheric stability, in response to enhanced Pacific-to-Atlantic interbasin sea surface temperature variations. Robust anthropogenic-forced intensification in the variability of Atlantic TC activity will continue in the future, with important implications for emergency planning and societal preparedness.

Multistage stochastic programming with a random number of stages: Applications in hurricane disaster relief logistics planning

We consider a logistics planning problem of prepositioning relief commodities in preparation for an impending hurricane landfall. We model the problem as a multi-period network flow problem where the objective is to minimize the total expected logistics cost of operating the network to meet the demand for relief commodities. We assume that the hurricane’s attributes evolve over time according to a Markov chain model, and the demand quantity at each demand point is calculated based on the hurricane’s attributes (intensity and location) at the terminal stage, which corresponds to the hurricane’s landfall. We introduce a fully adaptive multi-stage stochastic programming (MSP) model that allows the decision-maker to adapt their logistics decisions over time according to the evolution of the hurricane’s attributes. In addition, we develop a novel extension of the standard MSP model to address the challenge of having a random number of stages in the planning horizon due to the uncertain landfall time of the hurricane. We benchmark the performance of the adaptive decision policy given by the MSP models with alternative decision policies, including a static policy, a rolling-horizon policy, a wait-and-see policy, and a decision-tree-based policy, all based on two-stage stochastic programming models. Our numerical results and sensitivity analyses provide key insights into the value of MSP in the hurricane disaster relief logistics planning problem.

Standardization of Dual-Doppler Radar–Derived Wind Fields during a Hurricane Landfall

Abstract A new methodology for standardizing radar-derived elevated dual-Doppler (DD)-synthesized wind maps to the near surface is presented, leveraging the spatial variability found within the horizontal wind speed fields. The methodology is applied to a dataset collected by Texas Tech University (TTU) using two TTUKa-band mobile radar systems during the landfall of Hurricane Delta (2020) in coastal Louisiana. Relevant portions of the DD wind fields are extracted from multiple heights between 100 and 400 m above ground level, combined into 10-min segments and standardized to a reference height of 10 m and an open exposure roughness length of 0.03 m. Extractions from these standardized wind fields are compared and validated against the standardized wind measurements from a micronet of seven TTU StickNet platforms providing “ground truth” within the DD analysis domain. The validation efforts confirm the developed DD wind field standardization methodology yields robust results with correlation coefficients greater than 0.88 and mean biases less than 1%. The results of this study provide a new means for incorporating elevated DD radar data into new and existing surface wind field analysis systems geared toward generating a wind field of record during a hurricane landfall. Significance Statement This work presents a new methodology for using radar-generated wind fields during a hurricane landfall to support the construction of a surface wind field analysis. Because radar-based wind measurements are inherently elevated above the surface and because the wind conditions well above the ground do not directly translate to the wind conditions experienced at the ground, a method for standardizing the elevated radar wind fields to the surface provides tremendous value to generating a spatially continuous wind field of record during a hurricane landfall event to better inform event response and recovery efforts. In addition, the need for detailed wind fields of record from landfalling hurricanes that approach structural design limits is critical as a single design-level storm can alter building code return period analysis. Detailed wind fields for these high-impact events can directly inform associated updates in building codes ultimately contributing to a more resilient built environment.

Cyclogenesis: Simulating Hurricanes and Tornadoes

Cyclones are large-scale phenomena that result from complex heat and water transfer processes in the atmosphere, as well as from the interaction of multiple hydrometeors , i.e., water and ice particles. When cyclones make landfall, they are considered natural disasters and spawn dread and awe alike. We propose a physically-based approach to describe the 3D development of cyclones in a visually convincing and physically plausible manner. Our approach allows us to capture large-scale heat and water continuity, turbulent microphysical dynamics of hydrometeors, and mesoscale cyclonic processes within the planetary boundary layer. Modeling these processes enables us to simulate multiple hurricane and tornado phenomena. We evaluate our simulations quantitatively by comparing to real data from storm soundings and observations of hurricane landfall from climatology research. Additionally, qualitative comparisons to previous methods are performed to validate the different parts of our scheme. In summary, our model simulates cyclogenesis in a comprehensive way that allows us to interactively render animations of some of the most complex weather events.

Lifetime Performance of the Operational Hurricane Weather Research and Forecasting Model (HWRF) for North Atlantic Tropical Cyclones

Abstract The Hurricane Weather Research and Forecasting Model (HWRF) was the flagship hurricane model at NOAA’s National Centers for Environmental Prediction for 16 years and a state-of-the-art tool for tropical cyclone (TC) intensity prediction at the National Weather Service and across the globe. HWRF was a joint development between NOAA research and operations, specifically the Environmental Modeling Center and the Atlantic Oceanographic and Meteorological Laboratory. Significant support also came from the National Hurricane Center, Developmental Testbed Center, University Corporation for Atmospheric Research, universities, cooperative institutes, and the TC community. In the North Atlantic basin, where most improvement efforts focused, HWRF intensity forecast errors decreased by 45%–50% at many lead times between 2007 and 2022. These large improvements resulted from increases in horizontal and vertical resolution, as well as advances in model physics and data assimilation. HWRF intensity forecasts performed particularly well over the Gulf of Mexico in recent years, providing useful guidance for a large number of impactful landfalling hurricanes. Such advances were made possible not only by significant gains in computing but also through substantial investment from the Hurricane Forecast Improvement Program.

JALBTCX rapid-response aerial survey reconnaissance missions

Airborne Lidar and imagery is the technology of choice for mapping and quantifying impacts and conducting storm damage assessments in the aftermath of extreme storm events. Following land-falling hurricanes, for example, this technology provides data at high spatial resolution over hundreds of kilometers of hurricane-impacted shorelines in rapid fashion. To support the U.S. Army Corps of Engineers (USACE), the Federal Emergency Management Agency (FEMA), and others in their emergency response mission areas, the Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX) has performed rapid-response mapping for 10 hurricane and nor’easter events since Superstorm Sandy in 2012. Rapid-response data production workflows developed during the Superstorm Sandy mission have matured every year through the development and implementation of the JALBTCX Toolbox in response to requirements from engineers and scientists responsible for assessing storm impacts and making decisions related to recovery and restoration efforts. Today, the JALBTCX Toolbox is comprised of a series of modules that provide standard, streamlined, and repeatable workflows that guide users through the development of accurate datadriven information products on local, regional, and national scales. Rapid-response data products include shoreline and beach volume change in industry-standard, interoperable data formats and web services for consumption in GIS and web-based maps, dashboards, and applications. Post-response, the JALBTCX Toolbox includes modules for multi-dataset change detection analysis, the extraction of geomorphology features (e.g. dune crests and toes and sandbars), and the calculation of a Coastal Engineering Resilience Index (CERI). Future efforts will focus on further development of rapid response products in addition to the national coastal engineering resilience index that is routinely updated as new data becomes available.

Improving disaster relief plans for hurricanes with social media

Decisions on humanitarian responses to natural disasters are subject to considerable epistemic uncertainty. This paper advocates for postponing the decision point of pre-positioning relief supplies as close to landfall as possible and searching social media right post-landfall so that the demands can be estimated more accurately. We use a realistic hurricane preparedness case to demonstrate the effectiveness of our models and parametric estimation using social media data. The optimal timing to deploy relief supplies before hurricane landfall is noted to be 12 h in advance, which reduces the total cost by 13% more than if relief supplies are deployed 18+ hours in advance. Meanwhile, utilizing social media information can reduce the total cost as well as all kinds of specific costs being considered, excluding the point of dispensing (POD) sites setup cost, by approximately 15%. As the attitude toward risk goes from optimistic, to neutral, and to pessimistic, the number of PODs increases from 3 to 7, and to 8. A similar pattern can be noted in the total costs incurred by these decision-makers. Further, as the aversion to risk increases, locations tend to be chosen farther from landfall with these farther locations serving the less severe patients.

Rapid simulation of storm surge inundation for hurricane evacuation in Florida by multi-scale nested modeling approach

Hurricane evacuations require fast updates of coastal inundation predictions based on the update of hurricane forecasting track. NOAA usually updates the hurricane track at about 6 h interval. This paper presents a multi-scale nested modeling method for faster simulations of storm surge and coastal inundations. A medium-resolution model with minimum mesh size of 1200 m for the Gulf of Mexico is used to simulate the storm surge in the Gulf of Mexico. A high-resolution model with 120m–150 m mesh sizes is used to predict coastal inundations in the area of potential hurricane landfall. A nested modeling method has been developed to transfer boundary conditions from the large-scale storm surge model to the nested local-scale high-resolution model. The nested models have been satisfactorily validated and applied in the case study of Hurricane Michael. Results indicate that, by applying the nested models, it takes about 85 min for the simulation of one hurricane track for a 5-day forecasting, which will provide sufficient time before the next NOAA forecast update of hurricane's track in 6 h interval. The nested model application to the case study of Hurricane Michael demonstrates the coastal inundation patterns in the city of Mexico Beach with the root-mean-square error of 0.12 m from all measurement stations. Results of the nested model inundations on coastal critical infrastructure and roadways are further used with models that investigate risk assessments to support hurricane mitigation planning and evacuation operations sufficiently in advance.

The time- and distance-decay effects of hurricane relevancy on social media: an empirical study of three hurricanes in the United States

ABSTRACT Hurricane activity has been increasing in frequency and severity in recent years. This has serious implications for coastal and nearby communities who, when recovering from hurricanes, seek outside assistance from relevant government, non-governmental agencies, and nearby communities. The ever-increasing popularity of social media offers a new medium through which such social relevancy can be derived to inform targeted assistance-seeking efforts. This study utilizes Twitter to develop an understanding of disaster relevancy across space and time to establish a clearer context for impacted communities as to when and where assistance may be derived. Tweets were collected for three hurricanes within the contiguous United States (Hurricane Harvey in 2017, Florence in 2018 and Laura in 2020) and examined over a 12-week period following hurricane landfalls. The relationships between tweets and time and between tweets and distance were examined through correlation analysis. Results show statistically significant time- and distance-decay effects of hurricane relevancy on social media, though the time-decay effect was stronger. Most tweets occurred during the first week following hurricane landfall within the states wherein the hurricanes made landfall as well as around large cities. These findings could inform aid-seeking efforts in the event of hurricanes and other disasters.

Hurricane Impacts on Land in the Central and Eastern Caribbean Since 1494 CE From Written Records

AbstractWritten accounts of hurricanes and their impacts in the Caribbean region date back to 1494 CE We report a new compilation of hurricanes in the longest settled regions of the central and eastern Caribbean region (CECR) that is the most complete yet produced and present basic statistics. We assess likely undercounts of hurricanes due to incomplete documentary records in the earliest part of the record by using the most complete reporting from Puerto Rico relative to the remainder of the CECR. We compare our results with other documentary and proxy data from the region and demonstrate using wavelet analysis significant activity related to the El Niño‐Southern Oscillation (ENSO) at inter‐annual time scales and lower frequency activity from decadal to multi‐decadal scales. Our results offer evidence that, along with ENSO, fluctuations in sulfate aerosols are a possible contributor to the hurricane history. The frequency of hurricane landfalls and impacts in the CECR was lower in the 20th century than any time since 1494 CE.

Spatial Accessibility Analysis of Emergency Shelters with a Consideration of Sea Level Rise in Northwest Florida

Hurricane-induced storm surge and flooding often lead to the closures of evacuation routes, which can be disruptive for the victims trying to leave the impacted region. This problem becomes even more challenging when we consider the impact of sea level rise that happens due to global warming and other climate-related factors. As such, hurricane-induced storm surge elevations would increase nonlinearly when sea level rise lifts, flooding access to highways and bridge entrances, thereby reducing accessibility for affected census block groups to evacuate to hurricane shelters during hurricane landfall. This happened with the Category 5 Hurricane Michael which swept the east coast of Northwest Florida with long-lasting damage and impact on local communities and infrastructure. In this paper, we propose an integrated methodology that utilizes both sea level rise (SLR) scenario-informed storm surge simulations and floating catchment area models built in Geographical Information Systems (GIS). First, we set up sea level rise scenarios of 0, 0.5, 1, and 1.5 m with a focus on Hurricane Michael’s impact that led to the development of storm surge models. Second, these storm surge simulation outputs are fed into ArcGIS and floating catchment area-based scenarios are created to study the accessibility of shelters. Findings indicate that rural areas lost accessibility faster than urban areas due to a variety of factors including shelter distributions, and roadway closures as spatial accessibility to shelters for offshore populations was rapidly diminishing. We also observed that as inundation level increases, urban census block groups that are closer to the shelters get extremely high accessibility scores through FCA calculations compared to the other block groups. Results of this study could guide and help revise existing strategies for designing emergency response plans and update resilience action policies.

Eco-morphological evolution of the Bolivar Peninsula (Texas, U.S.A.) during the last 2,000 years: A multi-proxy record of coastal environmental changes

In the face of rapidly changing climate and increasing natural and anthropogenic disturbances, the long-term dynamics of coastal ecosystems in relation to hurricanes and human activities has become a focus of research for ecologists and geomorphologists across the Gulf of Mexico. This study integrates diatom, remote sensing, geochemical, and hydrodynamic analyses to document the ecological and morphological development of the barrier-island and estuary-wetland complex bordering Galveston Bay, Texas, from the late Holocene to the Anthropocene. The multi-proxy dataset revealed two major ecological shifts between 1320 and 840 cal yr BP and between 230 cal yr BP and 2008 AD in Galveston Bay. The first shift was driven by the landfall of a backdoor hurricane at ∼1000 cal yr BP in combination with the aperiodic and intense long-shore current that caused the barrier complex to retreat over fluvial deposits while the Bolivar Peninsula was growing toward the west. These morphological changes resulted in a narrower inlet, hence, restricting the marine influence from entering the bay and converting Galveston Bay to a wave-dominated estuary that was shallower and fresher. The second shift was caused by increasing modification to the waterway and infrastructures since 1800 AD. As a result, rapid sediment accretion was induced near the North Jetty, forming the current Bolivar Flats and setting the stage for mangrove colonization. The warming climate and hurricanes in recent decades acted as catalysts that accelerated mangrove expansion on the Bolivar Peninsula. Our results imply that the proposed coastal barrier system will likely increase the sediment deficit, alter the biogeochemical environment, and impact the primary productivity in the bay. However, on a regional scale, more and more mangrove stands are expected to appear around estuaries and lagoons, and along man-made jetties and barriers along the Gulf.

Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone–Driven Storm Tides and Inundation

Abstract This study proposes and assesses a methodology to obtain high-quality probabilistic predictions and uncertainty information of near-landfall tropical cyclone–driven (TC-driven) storm tide and inundation with limited time and resources. Forecasts of TC track, intensity, and size are perturbed according to quasi-random Korobov sequences of historical forecast errors with assumed Gaussian and uniform statistical distributions. These perturbations are run in an ensemble of hydrodynamic storm tide model simulations. The resulting set of maximum water surface elevations are dimensionality reduced using Karhunen–Loève expansions and then used as a training set to develop a polynomial chaos (PC) surrogate model from which global sensitivities and probabilistic predictions can be extracted. The maximum water surface elevation is extrapolated over dry points incorporating energy head loss with distance to properly train the surrogate for predicting inundation. We find that the surrogate constructed with third-order PCs using elastic net penalized regression with leave-one-out cross validation provides the most robust fit across training and test sets. Probabilistic predictions of maximum water surface elevation and inundation area by the surrogate model at 48-h lead time for three past U.S. landfalling hurricanes (Irma in 2017, Florence in 2018, and Laura in 2020) are found to be reliable when compared to best track hindcast simulation results, even when trained with as few as 19 samples. The maximum water surface elevation is most sensitive to perpendicular track-offset errors for all three storms. Laura is also highly sensitive to storm size and has the least reliable prediction. Significance Statement The purpose of this study is to develop and evaluate a methodology that can be used to provide high-quality probabilistic predictions of hurricane-induced storm tide and inundation with limited time and resources. This is important for emergency management purposes during or after the landfall of hurricanes. Our results show that sampling forecast errors using quasi-random sequences combined with machine learning techniques that fit polynomial functions to the data are well suited to this task. The polynomial functions also have the benefit of producing exact sensitivity indices of storm tide and inundation to the forecasted hurricane properties such as path, intensity, and size, which can be used for uncertainty estimation. The code implementing the presented methodology is publicly available on GitHub.

Influence of the Madden‐Julian Oscillation on Continental United States Hurricane Landfalls

AbstractThe Madden‐Julian oscillation (MJO) significantly impacts North Atlantic hurricanes, with increased hurricane activity occurring when the MJO enhances convection over Africa and the tropical Indian Ocean and suppressed hurricane activity occurring when the MJO enhances convection over the tropical Pacific. Using data from 1905 to 2015, we find more tropical cyclones (TCs) make landfall in the continental United States when the MJO enhances tropical Indian Ocean convection. In addition, when the MJO enhances Western Pacific and Western Hemisphere convection, TC activity is preferentially favored in the Caribbean, leading to more Gulf Coast landfalls. As MJO‐enhanced convection moves to the Indian Ocean and Maritime Continent, more storms form in the tropical Atlantic, favoring Florida Peninsula and East Coast landfalls. The MJO's TC steering wind modulation appears to be secondary to its genesis location modulation.

Dynamic Mechanisms Associated with the Structure and Evolution of Roll Vortices and Coherent Turbulence in the Hurricane Boundary Layer: A Large Eddy Simulation During the Landfall of Hurricane Harvey

Roll vortices are a series of large-scale turbulent eddies that nearly align with the mean wind direction and prevail in the hurricane boundary layer. In this study, the one-way nested WRF-LES model simulation results from Li et al. (J Atmos Sci 78(6):1847–1867, https://doi.org/10.1175/JAS-D-20-0270.1, 2021) are used to examine the structure and generation mechanism of roll vortices and associated coherent turbulence in the hurricane boundary layer during the landfall of Hurricane Harvey from 00 UTC 25 to 18 UTC 27 August 2017. Results indicate that roll vortices prevail in the hurricane boundary layer. The intense roll vortices and associated large turbulent eddies above them (at a height of ~ 200 to 3000 m) accumulate within a hurricane radius of 20–40 km. Their intensity is proportional to hurricane intensity during the simulation period. Before and during hurricane landfall, strong inflow convergence leads to horizontal advection of roll vortices throughout the entire hurricane boundary layer. Combined with the strong wind shear, the strongest roll vortices and associated large turbulent eddies are generated near the eyewall with suitable thermodynamic (Richardson number at around − 0.2 to 0.2) and dynamic conditions (strong negative inflow wind shear). After landfall, the decayed inflow weakens the inflow convergence and quickly reduces the strong roll vortices and associated large turbulent eddies. Diagnosis of vertical turbulent kinetic energy indicates that atmospheric pressure perturbation, caused by horizontal convergence, transfers the horizontal component of turbulence to the vertical component with a mean wavelength of about 1 km. The buoyancy term is weak and negative, and the large turbulent eddies are suppressed.

Sound‐Side Inundation and Seaward Erosion of a Barrier Island During Hurricane Landfall

AbstractBarrier islands are especially vulnerable to hurricanes and other large storms, owing to their mobile composition, low elevations, and detachment from the mainland. Conceptual models of barrier‐island evolution emphasize ocean‐side processes that drive landward migration through overwash, inlet migration, and aeolian transport. In contrast, we found that the impact of Hurricane Dorian (2019) on North Core Banks, a 36‐km barrier island on the Outer Banks of North Carolina, was primarily driven by inundation of the island from Pamlico Sound, as evidenced by storm‐surge model results and observations of high‐water marks and wrack lines. Analysis of photogrammetry products from aerial imagery collected before and after the storm indicate the loss of about 18% of the subaerial volume of the island through the formation of over 80 erosional washout channels extending from the marsh and washover platform, through gaps in the foredunes, to the shoreline. The washout channels were largely co‐located with washover fans deposited by earlier events. Net seaward export of sediment resulted in the formation of deltaic bars offshore of the channels, which became part of the post‐storm berm recovery by onshore bar migration and partial filling of the washouts with washover deposits within 2 months. This event represents a volumetric setback in the overwash/rollover behavior required for barrier transgression, but the new ponds and lowland habitats may provide beneficial habit for endangered species and will likely persist for years.

Understanding Meteorological Changes Following Severe Defoliation during a Strong Hurricane Landfall: Insights from Hurricane Michael (2018)

Abstract Despite prompting persistent meteorological changes, severe defoliation following a tropical cyclone (TC) landfall has received relatively little attention and is largely overlooked within hurricane preparedness and recovery planning. Changes to near-track vegetation can modify evapotranspiration for months after tropical cyclone passage, thereby altering boundary layer moisture and energy fluxes that drive the local water cycle. This study seeks to understand potential spatial and temporal changes in defoliation-driven meteorological conditions using Hurricane Michael (2018) as a testbed. In this sensitivity study, two Weather Research and Forecasting (WRF) Model simulations, a normal-landscape and a post-TC scenario, are compared to determine how a defoliation scar placed along Michael’s path alters surface heat fluxes, temperature, relative humidity, and precipitation near the storm’s track. In the month following the foliage reduction, WRF resolves a 0.7°C 2-m temperature increase, with the greatest changes occurring at night. Meanwhile, the simulations produce changes to the sensible and latent heat fluxes of +8.3 and −13.9 W m−2, respectively, while average relative humidity decreases from 73% to 70.1%. Although the accumulated precipitation in the defoliated simulation was larger along a narrow corridor paralleling and downwind of the hurricane track, neither simulation satisfactorily replicated post-Michael precipitation patterns as recorded by NCEP Stage IV QPE, casting doubt as to whether the downwind enhancement was exclusively due to the defoliation scar. This sensitivity analysis provides insight into the types of changes that may be possible following rapid and widespread defoliation during a TC landfall.

A potential explanation for the global increase in tropical cyclone rapid intensification

Tropical cyclone rapid intensification events often cause destructive hurricane landfalls because they are associated with the strongest storms and forecasts with the highest errors. Multi-decade observational datasets of tropical cyclone behavior have recently enabled documentation of upward trends in tropical cyclone rapid intensification in several basins. However, a robust anthropogenic signal in global intensification trends and the physical drivers of intensification trends have yet to be identified. To address these knowledge gaps, here we compare the observed trends in intensification and tropical cyclone environmental parameters to simulated natural variability in a high-resolution global climate model. In multiple basins and the global dataset, we detect a significant increase in intensification rates with a positive contribution from anthropogenic forcing. Furthermore, thermodynamic environments around tropical cyclones have become more favorable for intensification, and climate models show anthropogenic warming has significantly increased the probability of these changes.

Hurricane Ida (2021): Rapid Intensification Followed by Slow Inland Decay

Abstract Hurricane Ida recently became one of the strongest hurricanes to hit Louisiana on record, with an estimated landfalling maximum sustained wind of 130 kt (1 kt ≈ 0.51 m s−1). Although Hurricane Ida made landfall at a similar time of year and landfall location as Hurricane Katrina (2005), Ida’s postlandfall decay rate was much weaker than Hurricane Katrina. This manuscript includes a comparative analysis of pre- and postlandfall synoptic conditions for Hurricane Ida and other historical major landfalling hurricanes (category 3+ on the Saffir–Simpson hurricane wind scale) along the Gulf Coast since 1983, with a particular focus on Hurricane Katrina. Abundant precipitation in southeastern Louisiana prior to Ida’s landfall increased soil moisture. This increased soil moisture along with extremely weak overland steering flow likely slowed the storm’s weakening rate postlandfall. Offshore environmental factors also played an important role, particularly anomalously high nearshore sea surface temperatures and weak vertical wind shear that fueled the rapid intensification of Ida just before landfall. Strong nearshore vertical wind shear weakened Hurricane Katrina before landfall, and moderate northward steering flow caused Katrina to move inland relatively quickly, aiding in its relatively fast weakening rate following landfall. The results of this study improve our understanding of critical factors influencing the evolution of the nearshore intensity of major landfalling hurricanes in the Gulf of Mexico. This study can help facilitate forecasting and preparation for inland hazards resulting from landfalling hurricanes with nearshore intensification and weak postlandfall decay.

Location intelligence reveals the extent, timing, and spatial variation of hurricane preparedness

Hurricanes are one of the most catastrophic natural hazards faced by residents of the United States. Improving the public’s hurricane preparedness is essential to reduce the impact and disruption of hurricanes on households. Inherent in traditional methods for quantifying and monitoring hurricane preparedness are significant lags, which hinder effective monitoring of residents’ preparedness in advance of an impending hurricane. This study establishes a methodological framework to quantify the extent, timing, and spatial variation of hurricane preparedness at the census block group level using high-resolution location intelligence data. Anonymized cell phone data on visits to points-of-interest for each census block group in Harris County before 2017 Hurricane Harvey were used to examine residents’ hurricane preparedness. Four categories of points-of-interest, grocery stores, gas stations, pharmacies and home improvement stores, were identified as they have close relationship with hurricane preparedness, and the daily number of visits from each CBG to these four categories of POIs were calculated during preparation period. Two metrics, extent of preparedness and proactivity, were calculated based on the daily visit percentage change compared to the baseline period. The results show that peak visits to pharmacies often occurred in the early stage of preparation, whereas the peak of visits to gas stations happened closer to hurricane landfall. The spatial and temporal patterns of visits to grocery stores and home improvement stores were quite similar. However, correlation analysis demonstrates that extent of preparedness and proactivity are independent of each other. Combined with synchronous evacuation data, CBGs in Harris County were divided into four clusters in terms of extent of preparedness and evacuation rate. The clusters with low preparedness and low evacuation rate were identified as hotspots of vulnerability for shelter-in-place households that would need urgent attention during response. Hence, the research findings provide a new data-driven approach to quantify and monitor the extent, timing, and spatial variations of hurricane preparedness. Accordingly, the study advances data-driven understanding of human protective actions during disasters. The study outcomes also provide emergency response managers and public officials with novel data-driven insights to more proactively monitor residents’ disaster preparedness, making it possible to identify under-prepared areas and better allocate resources in a timely manner.