Hurricane Intensity Research Articles

A Satellite‐Derived Upper‐Ocean Stratification Data Set for the Tropical North Atlantic With Potential Applications for Hurricane Intensity Prediction

AbstractUpper‐ocean stratification strongly impacts vertical mixing and the heat flux between the ocean and atmosphere, especially under extreme conditions of tropical cyclones (TCs). Knowledge of prestorm stratification is important for accurate TC intensity prediction. In situ observations of the tropical ocean have significantly increased in the past decade. However, they are still too sparse to resolve ocean stratification variability in near‐real time and on small spatial scales. In this study, based on long‐term observations and an ocean reanalysis data set from 2004–2017, we investigate the possibility of retrieving upper‐ocean stratification from sea surface temperature (SST), sea surface salinity (SSS), and sea surface height (SSH) using a simple regression method. It is found that more than 90% of the mean seasonal cycle and about 30% to 80% of temperature and salinity stratification anomalies can be reconstructed using surface data from either observations or an ocean reanalysis. Simple regression can be used with satellite observations to create a high‐resolution, near‐real‐time‐gridded ocean stratification data set that successfully reproduces both the large and mesoscale variability of ocean stratification. When used in a simple expression for TC‐induced SST cooling, the satellite‐derived stratification shows improvements over an ocean analysis in terms of variance explained of SST cooling, offering promise as a near‐real‐time indicator of the ocean's impact on TC intensification.

Diurnal Variation of the Convective Area and Eye Size Associated with the Rapid Intensification of Tropical Cyclones

AbstractThis study examines the diurnal variation of the convective area and eye size of 30 rapidly intensifying tropical cyclones (RI TCs) that occurred in the western North Pacific from 2015 to 2017 utilizing Himawari-8 satellite imagery. The convective area can be divided into the active convective area (ACA), mixed phase, and inactive convective area (IACA) based on specific thresholds of brightness temperature. In general, ACA tends to develop vigorously from late afternoon to early the next morning, while mixed phase and IACA develop during the day. This diurnal pattern indicates the potential for ACA to evolve into mixed phase or IACA over time. From the 30 samples, RI TCs tend to have at least a single-completed diurnal signal of ACA inside the radius of maximum wind (RMW) during the rapidly intensifying period. In the same period, the RMW also contracts significantly. Meanwhile, more intense storms such as those of category 4 or 5 hurricane intensity are apt to have continuous ACA inside the RMW and maintain eyewall convective clouds. These diurnal patterns of the ACA could vary depending on the impact of large-scale environments such as vertical wind shear, ocean heat content, environmental mesoscale convection, and terrain. The linear regression analysis shows that from the tropical storm stage, RI commences after a slow intensification period, which enhances both the primary circulation and eyewall convective cloud. Finally, after the eye structure appears in satellite imagery, its size changes inversely to the diurnal variation of the convective activity (e.g., the eye size becomes larger during the daytime).

Evaluation of the Four-Dimensional Ensemble-Variational Hybrid Data Assimilation with Self-Consistent Regional Background Error Covariance for Improved Hurricane Intensity Forecasts

The feasibility of a hurricane initialization framework based on the Gridpoint Statistical Interpolation (GSI)-based four-dimensional ensemble-variational (GSI-4DEnVar) hybrid data assimilation system for the Hurricane Weather Research and Forecasting model (HWRF) model is evaluated in this study. The system considers the temporal evolution of error covariances via the use of four-dimensional ensemble perturbations that are provided by high-resolution, self-consistent HWRF ensemble forecasts. It is different from the configuration of the GSI-based three-dimensional ensemble-variational (GSI-3DEnVar) hybrid data assimilation system, similar to that used in the operational HWRF, which employs background error covariances provided by coarser-resolution global ensembles from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) ensemble Kalman filtering data assimilation system. In addition, our proposed initialization framework discards the empirical intensity correction in the vortex initialization package that is employed by the GSI-3DEnVar initialization framework in operational HWRF. Data assimilation and numerical simulation experiments for Hurricanes Joaquin (2015), Patricia (2015), and Matthew (2016) are conducted during their intensity changes. The impacts of two initialization frameworks on the HWRF analyses and forecasts are compared. It is found that GSI-4DEnVar leads to a reduction in track, minimum sea level pressure (MSLP), and maximum surface wind (MSW) forecast errors in all of the HWRF simulations, compared with the GSI-3DEnVar initialization framework. With assimilating high-resolution observations within the hurricane inner-core region, GSI-4DEnVar can produce the initial hurricane intensity reasonably well without the empirical vortex intensity correction. Further diagnoses with Hurricane Joaquin indicate that GSI-4DEnVar can significantly alleviate the imbalances in the initial conditions and enhance the performance of the data assimilation and subsequent hurricane intensity and precipitation forecasts.

Hurricane-induced power outage risk under climate change is primarily driven by the uncertainty in projections of future hurricane frequency

Nine in ten major outages in the US have been caused by hurricanes. Long-term outage risk is a function of climate change-triggered shifts in hurricane frequency and intensity; yet projections of both remain highly uncertain. However, outage risk models do not account for the epistemic uncertainties in physics-based hurricane projections under climate change, largely due to the extreme computational complexity. Instead they use simple probabilistic assumptions to model such uncertainties. Here, we propose a transparent and efficient framework to, for the first time, bridge the physics-based hurricane projections and intricate outage risk models. We find that uncertainty in projections of the frequency of weaker storms explains over 95% of the uncertainty in outage projections; thus, reducing this uncertainty will greatly improve outage risk management. We also show that the expected annual fraction of affected customers exhibits large variances, warranting the adoption of robust resilience investment strategies and climate-informed regulatory frameworks.

Understanding Error Distributions of Hurricane Intensity Forecasts during Rapid Intensity Changes

AbstractThe characteristics of official National Hurricane Center (NHC) intensity forecast errors are examined for the North Atlantic and east Pacific basins from 1989 to 2018. It is shown how rapid intensification (RI) and rapid weakening (RW) influence yearly NHC forecast errors for forecasts between 12 and 48 h in length. In addition to being the tail of the intensity change distribution, RI and RW are at the tails of the forecast error distribution. Yearly mean absolute forecast errors are positively correlated with the yearly number of RI/RW occurrences and explain roughly 20% of the variance in the Atlantic and 30% in the east Pacific. The higher occurrence of RI events in the east Pacific contributes to larger intensity forecast errors overall but also a better probability of detection and success ratio. Statistically significant improvements to 24-h RI forecast biases have been made in the east Pacific and to 24-h RW biases in the Atlantic. Over-ocean 24-h RW events cause larger mean errors in the east Pacific that have not improved with time. Environmental predictors from the Statistical Hurricane Intensity Prediction Scheme (SHIPS) are used to diagnose what conditions lead to the largest RI and RW forecast errors on average. The forecast error distributions widen for both RI and RW when tropical systems experience low vertical wind shear, warm sea surface temperature, and moderate low-level relative humidity. Consistent with existing literature, the forecast error distributions suggest that improvements to our observational capabilities, understanding, and prediction of inner-core processes is paramount to both RI and RW prediction.

Investigating the Impact of High-Resolution Land–Sea Masks on Hurricane Forecasts in HWRF

Realistic wind information is critical for accurate forecasts of landfalling hurricanes. In order to provide more realistic near-surface wind forecasts of hurricanes over coastal regions, high-resolution land–sea masks are considered. As a leading hurricane modeling system, the National Centers for Environmental Prediction (NCEP) Hurricane Weather Research Forecast (HWRF) system has been widely used in both operational and research environments for studying hurricanes in different basins. In this study, high-resolution land–sea mask datasets are introduced to the nested domain of HWRF, for the first time, as an attempt to improve hurricane wind forecasts. Four destructive North Atlantic hurricanes (Harvey and Irma in 2017; and Florence and Michael in 2018), which brought historic wind damage and storm surge along the Eastern Seaboard of the United States and Northeastern Gulf Coast, were selected to demonstrate the methodology of extending the capability to HWRF, due to the introduction of the high-resolution land–sea masks into the nested domains for the first time. A preliminary assessment of the numerical experiments with HWRF shows that the introduction of high-resolution land–sea masks into the nested domains produce significantly more accurate definitions of coastlines, land-use, and soil types. Furthermore, the high-resolution land–sea mask not only improves the quality of simulated wind information along the coast, but also improves the hurricane track, intensity, and storm-size predictions.

Harnessing new data technologies for nature-based solutions in assessing and managing risk in coastal zones

Responsiveness and resilience to coastal disasters is an increasingly critical need for governments, businesses, and communities as the intensity of hurricanes, coastal storms and sea level rise exacerbates flood and erosion damages. The number and severity of disasters have increased significantly over the past two decades while intense coastal development has steadily degraded the protective and other ecosystem benefits from natural habitats such as coral reefs, mangroves and seagrasses. Such ecosystems can provide cost-effective, long-term risk reduction and other benefits to coastal communities. Yet easy, low-cost methodologies for incorporating such natural capital values into metrics that can inform policy and investment decisions have been lacking until recently. Whereas new approaches and studies have developed ways to quantify and assess such risk reduction benefits, finding the data to assess such services in data poor environments, both for regional and local scales, remains challenging. This paper outlines how breakthroughs in data technologies can help with incorporating ecosystem benefits into real-world policy and investment applications. Specific new data and analytical technologies are increasingly being used to quantify coastal risk reduction and other ecosystem benefits. We review several case studies where ecosystem benefits were quantified based on new data technologies and used for policy and investment strategies to improve a broader suite of ecosystem benefits to coastal communities. Equipping public and private sector actors with cost-effective and accessible approaches and tools to incorporate nature-based solutions into disaster risk management and coastal zone management planning more generally can transform climate resilience strategies around the world.

Resilience in the aftermath of hurricanes: Fluctuations in a Critically Endangered population of West Indian Woodpeckers Melanerpes superciliaris nyeanus over two decades

SummaryCyclonic storms (often called hurricanes, typhoons, or cyclones) often cause population declines in vulnerable bird species, and the intensity of these storms appears to be increasing due to climate change. Prior studies have reported short-term impacts of hurricanes on avifauna, but few have examined long-term impacts. Over two decades (1993–2018), we periodically surveyed a subspecies of West Indian Woodpecker Melanerpes superciliaris nyeanus on San Salvador, a small island in The Bahamas, to determine its distribution on the island, habitat use, and effects of hurricanes on abundance and population size. We conducted passive and playback surveys, supplemented with mist-netting. Woodpeckers were found only in the northern part of San Salvador, despite extensive surveys throughout other accessible areas of the island. Birds occupied areas with taller coppice adjacent to sabal palm Sabal palmetto groves, which were used for nesting. After hurricanes with >160 kph winds passed over San Salvador, woodpecker densities declined to 35–40% of pre-hurricane densities, but generally recovered back to pre-hurricane densities within 2–3 years. Based on an estimated density of woodpeckers within a ~1,400 ha occupied area, we calculated a population size of approximately 240 individuals (CI = 68-408). However, the population declined to far lower numbers immediately following hurricanes. Under IUCN Red List criteria, M. s. nyeanus classifies as ‘Critically Endangered’, and could be especially sensitive to future hurricanes if they occur at a high enough frequency or intensity to prevent the population from rebounding. Given the small size, isolation, and vulnerability of this population, we recommend preservation of the core habitat, continued monitoring, and further research. Our study shows that small, threatened bird populations can be resilient to the effects of hurricanes, but increased intensity of hurricanes, in combination with other threats, may limit this resilience in the future.

A Geochemical Record of Late‐Holocene Hurricane Events From the Florida Everglades

AbstractA 5.25‐m sediment core SRM‐1 and 45 surface samples from mangrove forests at the Shark River Estuary in the Everglades National Park, Florida, were examined by using X‐ray fluorescence and carbon isotopic analyses to study the history of intense hurricane landfall during the Late‐Holocene. Significance testing of the surface samples in relation to storm deposits from Hurricane Wilma suggests that elemental concentration of Sr and Cl and the ratio of Cl/Br are the most sensitive indicators for major hurricane events in our study area. The geochemical data sets of core SRM‐1 identified five active periods of intense hurricane activities during the last 3,500 years at ~3,400–3,000, ~2,200–1,500, ~1,000–800, ~600–300, and ~150 calibrated years before present to present. This is the longest paleohurricane record to date from South Florida. Our results are consistent with the view that intense hurricane activities in South Florida were modulated by Intertropical Convergence Zone (ITCZ) movements, El Niño/Southern Oscillation (ENSO) activities, and North Atlantic Oscillation (NAO) strength. This study contributes to the methodological advancement in paleotempestological studies by demonstrating that geochemical signals, particularly signals of saltwater intrusions, can be preserved in the sediment profiles on millennial time‐scale and measured by X‐ray fluorescence techniques, thereby enabling more storm records to be produced from otherwise suboptimal sand‐limited coastal systems such as the Florida Everglades. More work needs to be done to explore the use of geochemical and stable isotopic analyses in detecting storm signals from sand‐limited coastal environments.

Understanding tropical forest abiotic response to hurricanes using experimental manipulations, field observations, and satellite data

Abstract. With projected increasing intensity of hurricanes and large uncertainty in the path of forest recovery from hurricanes, studies are needed to understand the fundamental response of forests to canopy opening and debris deposition: the response of the abiotic factors underneath the canopy. Through two manipulative experiments and instrumenting prior to Hurricane Maria (2017) in the Luquillo Experimental Forest (LEF) of Puerto Rico, this study found a long recovery time of primary abiotic factors (beneath canopy light, throughfall, and temperature) influenced by the disturbance of canopy opening, as well as complex responses by the secondary abiotic factors (relative humidity, soil moisture, and leaf saturation) influenced by the disturbance of the primary factors. Recovery took 4–5 years for beneath canopy light, while throughfall recovery took 4–9 years and neither had recovered when Hurricane Maria passed 3 years after the second experiment. Air and soil temperature seemingly recovered quickly from each disturbance (<2.5 years in two experiments for ∼+1 ∘C of change); however, temperature was the most important modulator of secondary factors, which followed the long-term patterns of the throughfall. While the soil remained wetter and relative humidity in the air stayed lower until recovery, leaves in the litter and canopy were wetter and drier, with evidence that leaves dry out faster in low rainfall and saturate faster in high rainfall after disturbance. Comparison of satellite and field data before and after the 2017 hurricanes showed the utility of satellites in expanding the data coverage, but the muted response of the satellite data suggests they measure dense forest as well as thin forest that is not as disturbed by hurricanes. Thus, quick recovery times recorded by satellites should not be assumed representative of all the forest. Data records spanning the multiple manipulative experiments followed by Hurricane Maria in the LEF provide evidence that intermediate hurricane frequency has the most extreme abiotic response (with evidence on almost all abiotic factors tested) versus infrequent or frequent hurricanes.

A Simple Strategy to Communicate about Climate Attribution

AbstractHurricane Harvey and other recent weather extremes stimulated extensive public discourse about the role of anthropogenic climate change in amplifying, or otherwise modifying, such events. In tandem, the scientific community has made considerable progress on statistical “climate attribution.” However, explaining these statistical methods to the public has posed challenges. Using appropriately designed “spinner boards,” we find that even members of the general public who do not understand the difference between weather and climate are readily able to understand basic concepts of attribution and explain those concepts to others. This includes both understanding and explaining the way in which the probability of an extreme weather event may increase as a result of climate change and explaining how the intensity of hurricanes can be increased. If properly developed and used by TV weather forecasters and news reporters, this method holds the potential to significantly improve public understanding of climate attribution.

Landform type mediates compositional change in a hurricane-disturbed sub-tropical forest

BackgroundCategorization of topographical features into landform type is a long-standing method for understanding physiographic patterns in the environment. Differences in forest composition between landform types are driven primarily by concurrent differences in soil composition and moisture, but also disturbance regime. Many studies have focused on the interaction between fire disturbance, succession, and landforms, but the effects of hurricane disturbance on compositional differences between landforms are poorly understood. In the study presented here, we assess compositional and structural differences between landform types in the tree community of a young sub-tropical forest that is frequently subjected to hurricanes. Specifically, we ask whether the tree community (1) changed structurally over the study period, (2) experienced compositional change over the study period, (3) is compositionally different between landform types, and (4) exhibits compositional change mediated by landform type.ResultsThe tree community experienced significant structural change over the course of our study, but compositional change was only significant for some landforms.ConclusionDespite large-scale, intense, and frequent hurricane disturbance to our study system, compositional change in the tree community was localized and only significant for some landform types.

How Effective Were the Beach Nourishments at Cancun?

Beach nourishment is generally seen as the preferred means of rectifying coastal erosion, due to its low environmental impact and natural evolution. The largest beach nourishment project ever carried out in Mexico took place on Cancun beach in 2006, as a response to the most intense hurricane season ever registered in Mexico, in 2005. After Hurricane Dean, in 2009, a second nourishment was conducted, which evidenced flaws in the design and execution of the first project. Previous investigations report that the need for beach re-fills directly correlates with wave energy. However, following a thorough revision of the extreme climatic events that occurred between 1978 and 2018, it has been found that the amount of erosion also depends on the frequency and duration of high energy events. The findings also show that the apparent success of the second nourishment is mainly associated with a decline in the number of extreme wave power events impacting the beach. In the conclusion to this paper, we share the knowledge gained, but not yet applied, in Mexico or elsewhere, regarding beach use, urbanization, and protection in beach planning.

As the Planet Warms, Intense Storms Become More Common

Thirty-nine years of satellite data reveal that the prevalence of intense hurricanes, cyclones, and typhoons—category 3 and above on the Saffir-Simpson scale—is increasing.

Hurricane Frequency and Intensity May Decrease Dispersal of Kemp’s Ridley Sea Turtle Hatchlings in the Gulf of Mexico

Environmental variability can be an important factor in the population dynamics of many species. In marine systems, for instance, whether environmental conditions facilitate or impede the movements of juvenile animals to nursery habitat can have a large influence on subsequent population abundance. Both subtle differences in the position of oceanographic features (such as meandering currents) and major disturbances (such as hurricanes) can greatly alter dispersal outcomes. Here, we use an ocean circulation model to explore seasonal and annual variation in the dispersal of post-hatchling Kemp’s ridley sea turtles (Lepidochelys kempii). We simulated the transport of 24 cohorts of young Kemp’s ridley dispersing from three main nesting areas in the western Gulf of Mexico to describe variability in transport during the main hatching season and across years. We examined whether differences in transport distance among Kemp’s ridley cohorts could be explained by hurricane events. We found that years with high numbers of hurricanes corresponded to shorter dispersal distances within the first six months. Our findings suggest that differences in dispersal among sites and the impact due to hurricanes could influence the survivorship and somatic growth rates of turtles from different nesting sites and hatching cohorts. Considering such factors in future population assessments may aid in predicting how the potential for increasing tropical storms, a phenomenon linked to climate change, could affect Kemp’s ridley and other populations of sea turtles in the Atlantic.

Hurricane Wind and Storm Surge Effects on Coastal Bridges under a Changing Climate

Hurricanes and their cascading hazards have been responsible for widespread damage to life and property, and are the largest contributor to insured annual losses in coastal areas of the U.S.A. Such losses are expected to increase because of changing climate and growing coastal population density. An effective methodology to assess hurricane wind and surge hazard risks to coastal bridges under changing climate conditions is proposed. The influence of climate change scenarios on hurricane intensity and frequency is explored. A framework that couples the hurricane tracking model (consisting of genesis, track, and intensity) with a height-resolving analytical wind model and a newly developed machine learning-based surge model is used for risk assessment. The proposed methodology is applied to a coastal bridge to obtain its traffic closure rate resulting from the observed (historical) and future (projected) hurricane winds and storm surges, demonstrating the effects of changing climate on the civil infrastructure in a hurricane-prone region.

Study on the Relationship between Global Hurricane and Global Warming

In order to measure the intensity of global hurricanes, this paper selects the frequency ofhurricanes, the average grade and the mean wind speed as the indexes of intensity. Firstly, the annual hurricanes frequencies in the Atlantic Ocean, the Western Pacific, the East Pacific, the North Indian Ocean and the South Indian Ocean and the global average hurricanes scale and average wind speed, train the existing data through wavelet neural network and predict the occurrence frequency of hurricanes in the next 20 years. According to the scatter plot drawn from the known data, it is found that the global hurricane frequency, the average grade and theaverage speed of wind, which showing periodic changes, but generally increase. As a result,the intensity of global hurricanes has generally been enhanced in the current phase of observations.

Influence of Satellite Observation Angle to Tropical Cyclone Intensity Estimation Using the Deviation Angle Variance Technique

The deviation angle variance (DAV) method was developed to objectively estimate tropical cyclone (TC) intensity from geostationary infrared (IR) brightness temperature data. Here, we demonstrate that improvements of 25% root mean square error (RMSE) in major hurricane intensity estimation (relative to best track) can be obtained by considering the pixel-by-pixel satellite view angle in the estimation. Using data from the Chinese Fengyun 2E and 2F satellites for Super Typhoon Soudelor (2015), we demonstrate how the satellite observation angle can reduce the accuracy of intensity estimation, especially for the strongest TCs. Based on these results, an improved DAV estimator is developed using 12 years (2004–2015) of Geostationary Operational Environmental Satellite (GOES)-East satellite IR images over the North Atlantic basin.

Concentration and isotopic composition of mercury in a blackwater river affected by extreme flooding events

AbstractTorrential rain and extreme flooding caused by Atlantic hurricanes mobilize a large pool of organic matter (OM) from coastal forested watersheds in the southeastern United States. However, the mobilization of toxic metals such as mercury (Hg) that are associated with this vast pool of OM are rarely measured. This study aims to assess the variations of total Hg (THg) and methylmercury (MeHg) levels and the isotopic compositions of Hg in a blackwater river (Waccamaw River, SC, U.S.A.) during two recent extreme flooding events induced by Hurricane Joaquin (October 2015) and Hurricane Matthew (October 2016). We show that extreme flooding considerably increased filtered THg and MeHg concentrations associated with aromatic dissolved organic matter. During a 2‐month sampling window each year (October–November), we estimate that about 27% (2015) and 78% (2016) of the average amount of Hg deposited atmospherically during these 2 months was exported via the river. The isotopic composition of Hg in the river waters was changed only slightly by the substantial inputs of runoff from surrounding landscapes, in which mass‐dependent fractionation (as δ202Hg) decreased from −1.47 to −1.67‰ and mass‐independent fractionation (as ∆199Hg) decreased from −0.15 to −0.37‰. The slight variations in Hg isotopic composition during such extreme flooding events imply that sources of Hg in the river are nearly unchanged even under the very high wet deposition of Hg derived from the intensive rainfall. The majority of Hg exported by the river (74–85%) is estimated to have been derived from dry deposition to the watersheds. An increase in frequency and intensity of Atlantic hurricanes is expected in the next few decades due to further warming of ocean surface waters. We predict that increased hurricanes will mobilize more dry‐deposited Hg and in situ produced MeHg from these coastal watersheds where MeHg can be extensively bioaccumulated and biomagnified in the downstream aquatic food webs.

Development and Evaluation of an Evolutionary Programming-Based Tropical Cyclone Intensity Model

AbstractA statistical–dynamical tropical cyclone (TC) intensity model is developed from a large ensemble of algorithms through evolutionary programming (EP). EP mimics the evolutionary principles of genetic information, reproduction, and mutation to develop a population of algorithms with skillful predictor combinations. From this evolutionary process the 100 most skillful algorithms as determined by root-mean square error on validation data are kept and bias corrected. Bayesian model combination is used to assign weights to a subset of 10 skillful yet diverse algorithms from this list. The resulting algorithm combination produces a forecast superior in skill to that from any individual algorithm. Using these methods, two models are developed to give deterministic and probabilistic forecasts for TC intensity every 12 h out to 120 h: one each for the North Atlantic and eastern and central North Pacific basins. Deterministic performance, as defined by MAE, exceeds that of a “no skill” forecast in the North Atlantic to 96 h and is competitive with the operational Statistical Hurricane Intensity Prediction Scheme and Logistic Growth Equation Model at these times. In the eastern and central North Pacific, deterministic skill is comparable to the blended 5-day climatology and persistence (CLP5) track and decay-SHIFOR (DSHF) intensity forecast (OCD5) only to 24 h, after which time it is generally less skillful than OCD5 and all operational guidance. Probabilistic rapid intensification forecasts at the 25–30 kt (24 h)−1 thresholds, particularly in the Atlantic, are skillful relative to climatology and competitive with operational guidance when subjectively calibrated; however, probabilistic rapid weakening forecasts are not skillful relative to climatology at any threshold in either basin. Case studies are analyzed to give more insight into model behavior and performance.