Tropical Storm Research Articles

OVERVIEW OF THE COASTAL TEXAS MEGA PROJECT

The Texas Gulf coast is a complex system with numerous narrow inlets and broad back bays with intricate river and bayou networks. Due to its complex settings, the Texas coast is susceptible to extreme compound flooding due to fluvial, pluvial, and surge during hurricanes and tropical storms. Five of the top six largest tropical cyclone rainfall totals in the continental U.S. have occurred in Texas (USACE 2021a). The Texas coast is also one of the United States most dynamic regions in terms of population and economic growth. According to the Bureau of Economic Geology, twenty five percent of the population and thirty three percent of the economic resources of Texas are located along the 360 mile long coast. The objective of the Coastal Texas Study was to improve our capabilities to prepare for, resist, recover and adapt to extreme events. A “Multiple Lines of Defense” strategy was used in the Coastal Texas study to design cost-effective, environmentally friendly solutions that will reduce risks of storms impacting the coastal communities and restore important wildlife habitat at the same time.

SHORT TERM SPATIALLY DENSE PREDICTION OF STORM SURGE ALONG THE NEW ZEALAND COASTLINE

Storm surge is the rise in water level generated by wind and atmospheric pressure changes associated with tropical or mid-latitude storms. In conjunction with tides, it is one major driver of coastal flooding associated with storms events. Because local inundation is strongly modulated by the local shape of the coastline and the bathymetric slope, accurate storm surge prediction by the mean of traditional numerical models requires the use of very fine grids and is hence very resource intensive. This means that the performance of a live prediction system based on such methods will likely be subject to a trade-off between prediction accuracy, prediction speed and cost (Wang et al., 2009). Several publications have demonstrated the potential of machine learning approaches for the prediction of storm surge (e.g. (Tiggeloven et al., 2021), (Cagigal et al, 2020)). However, the developed methods often focus on local predictors and aim at predicting storm surge at a single location at a time. In this study, we explore the use of several data driven methods as an alternative to numerical methods to predict storm surge along the coast of New Zealand.

Propagation of tropical squall line-induced storm coastal inundation episodes in Java-Bali, Indonesia

The short-lived tropical squall lines could trigger weather-related hazards to the northern part of the Indonesia Maritime Continent (IMC), such as Sumatra and Kalimantan. Herein, we investigated the rare propagation event of the long-lived Sumatra squall line associated with a severe storm surge that induced coastal inundation in Java-Bali with devastating impacts from 22 May–2 June 2020. With a comprehensive approach combining observational, numerical, and analytical studies, for the first time, we proposed the possible mechanism related to the long-lived squall line over the IMC, which represents the largest equatorial tropical region with the most complicated air-sea interaction area in the world. Our findings suggest that the long-lived squall line related to the supercell-like thunderstorm initiated from multicell over central Sumatra on May 20, 2020, continuously propagated southeastward until several days later reached Bali. The near-quasi steady convective line has 6 hours of time travel from central Sumatra to west Java. The supercell-like rapidly develops from multicell with a deep convective updraft under the strong and fast cold pool (∼13.8 m s−1). The further southeastward propagation of squall line with broken line type seems reinforced by low-level moist transport from the Java Sea. This study also suggested that this unusual event of a long-lived squall line might occur more frequently in the warming upper ocean in the IMC.

Flood and Fire

The intensifying warming of the planet over the past several decades is a manifestation of centuries of uneven and inequitable extractive economies. This warming is well known to be the main force driving shifts in climatological conditions and extreme weather events leading to increasingly severe impacts on planetary systems. Every year, more locations on earth are experiencing heat waves, intense droughts, longer and larger fire seasons, increased tropical storm intensity, and sea level rise at rates that would have been unthinkable a generation ago while near daily news reports document the increasing toll that this changing climate plays in exacerbating social and ecological vulnerabilities. Just this year, at the start of the Northern Hemisphere summer of 2023, a massive tropical cyclone has killed over 145 people in Bangladesh and Myanmar, western Canada has already seen as much forest burned in a few days as it does in an entire summer, drastically diminishing air quality over half a continent, the Po River Valley in Italy has been ravaged by floods after experiencing two years of extreme drought, and California has experienced deadly and pervasive atmospheric rivers after years of record-setting fire seasons and water shortages. In this special issue, rather than prioritizing benign and depoliticized notions of adaptive capacity and resilience, as is far too common within mainstream discussions of climate change, we highlight the theme of flood and fire to examine these events as compounding contemporary crises and responses to phenomena that are devastating, transforming, and reformulating communities, ecologies, and governing processes around the planet.

Introduction

Introduction

Assessing the morphodynamic response of a New England beach-barrier system to an artificial reef

As a result of climate change and the ensuing accelerating sea level rise, the Ninigret Trustom Pond beach barrier system, located along the southern shore of Rhode Island, USA, has become increasingly vulnerable to tropical storms, hurricanes, and Nor’easters affecting the region. In this work, we explore the ability of a segmented artificial reef (AR) to protect the Ninigret Trustom Pond beach barrier system, by reducing the incident wave energy and ultimately the beach erosion. A set of nine AR concepts are selected, which are predicted to be accretive based on Ranasinghe et al.’s (2010) design criterion (RVB), extended by van der Baan (2013) to apply to segmented submerged breakwaters. The effectiveness of each concept is then assessed through numerical simulations, for both average wave climate conditions and storm events, with and without sea level rise, using the 2D morphodynamic model XBeach. The AR concepts are evaluated based on their ability to reduce coastal erosion during the storm events, including with a near future sea level rise of 0.31 m (scenario based on local prediction by 2050 relative to 2020 following the intermediate NOAA scenario) , while ensuring a slightly accretive behavior in average wave conditions. The reduction of the coastal erosion during the storm events is quantified by comparing the subaerial eroded volume at the site, with or without sea level rise, when implementing each of the nine pre-selected AR concepts. While all concepts are found to protect the shoreline during storm events, including with the 0.31 m sea level rise scenario, four of the nine AR concepts, pre-selected to be accretive based on the RVB criterion, are found to be erosive in average conditions. Assuming a given submergence level, the distance to the shoreline and the AR lateral confinement ratio, LC, (gap width to length ratio of the segments) are the main controlling parameter.

Tropical Storm Path Prediction Using Long Short-Term Memory Model, Similarity Measurement of Trajectories and Contextual Information

Tropical Storm Path Prediction Using Long Short-Term Memory Model, Similarity Measurement of Trajectories and Contextual Information

SUITABLE BOUNDARY LOCATION IDENTIFCATION FOR RAINFALL-RUNOFF AND SURGE MODEL COUPLING TO EVALUATE COMPOUND FLOOD HAZARDS IN COASTAL REGIONS

Flooding in many coastal regions is exacerbated due to complex interactions of multiple flood drivers such as rainfall-runoff and coastal surge which can occur simultaneously or sequentially during storm events, leading to compound flooding hazards. The Texas Gulf Coasts (USA) are especially vulnerable from compound flooding due to frequent occurrence of tropical storms which bring strong winds to drive storm surge and heavy rainfall to generate inland flooding. Large scale studies {Coastal Texas Restoration and Protection Study (CTX, 2021), Texas General Land Office’s Texas River Basin Study, Base Level Engineering Study} are being conducted by state and federal agencies for flood hazard assessment and for development of mitigation and abatement strategies for reducing this risk and increasing community resilience. Traditionally, large scale storm surge models do not account for runoff contributions to water levels in the interior of their domain. Riverine models for their part use either normal depth or mean high water levels to assign downstream boundary conditions within tidally influenced area, thus ignoring effects of storm surge. In this Planning Assistance to States (PAS) study, located in the Lower Clear Creek and Dickinson Bayou watersheds (Texas, USA), we have evaluated rainfall-runoff and storm surge interactions to determine suitable locations to use as model boundary conditions for exchanging data between rainfall-runoff and surge models under compound flooding conditions.

Fewer, but More Intense, Future Tropical Storms Over the Ganges and Mekong Basins

AbstractUnderstanding climate change impacts on Tropical Storm (TS) activity is crucial for effective adaptation planning and risk assessment, particularly in densely populated low‐lying delta rivers basins like the Ganges and Mekong. The change to TS characteristics with warming is uncertain due to limitations in global climate model resolution and process‐representation and storm tracking algorithms (trackers). Here, we used 13 HighResMIP models and two trackers to estimate the uncertainty in projections of TS characteristics. We found different trackers producing qualitatively similar but quantitatively different results. Our results show a decline (median ∼52%) in the frequency of TS but increase in the strongest TS and Available Cyclone Energy (ACE) of TS over both basins. The higher‐resolution models extract TS with much higher intensity and ACE values compared to the lower‐resolution models. These results have implications for adaptation planning and risk assessment for TS and suggest the need for further high‐resolution modeling studies.

The Cost of the increasing Tropical Storms: Examining the Long Run Economic Implications of Climate Change

Climate change has altered economics profoundly; it impacts major aspects of the global economy and has consequences that can be unforeseeable in the long run. Atmospheric temperatures have been increasing twice as fast since 1981 and it has caused detrimental impacts globally; one being the increase in the intensity and frequency of tropical storms. Humans have contributed about 95% to this warming of the Earth. This paper explores the long run economic impacts of a specific consequence of this pressing issue to further understand the impacts of individual actions. The warming of the Earth has caused an increase in the frequency of tropical storms as well as the intensity. Severe tropical storms destroy agriculture and demolish infrastructure and commercial buildings. This heavily impacts the economy in various ways, and this paper will focus on the impact of long run economic growth of an economy. To combat this issue, the government and central bank can take corrective actions. However, these actions come with their own economic consequences, and it is important to be aware of the outcomes to make an informed decision on critical issues. Through extensive research, it can be concluded that the best course of action is a united act amongst the central bank and the government to grow the economy while combating climate change.

Boundary layer profile of decaying and non‐decaying tropical storms near landfall

AbstractThe vertical profile of the wind structure of translating tropical cyclones, including the associated azimuthal asymmetry, has been the subject of existing theoretical and observational studies using dropsondes. Most of these studies are based on data collected from relatively strong cyclones over the Atlantic. Here we explore the tropical cyclone boundary layer wind profile of mainly relatively weak landfalling cyclones near Hong Kong. We find that decaying tropical storms have a much larger mid‐ to low‐level inflow angle than those that are intensifying or in steady‐state. The inflow angles of intensifying, steady‐state and decaying tropical storms converge towards the top of the boundary layer. The wind speed reduces through the boundary layer in a similar way in all three cases. The combination of these factors means that decaying tropical storms have stronger inflow than intensifying and steady‐state ones. We attribute these local effects to remote enhanced surface friction over land when the storms are weakening.

Evolution of flow field around high-speed trains meeting at the tunnel entrance under strong wind-rain environments

Tropical storms pose a great threat to the traffic safety of tunnel-embankment transition sections in coastal areas, especially when two trains are meeting. A series of 3D computational fluid dynamics numerical simulations of wind-rain-tunnel-embankment-train are established using the Eulerian multiphase and the Shear-Stress Transport k-ω models. The numerical model's reliability is verified by wind tunnel tests using rainfall simulation technology. The differences in the train's aerodynamic performance before and during the meeting under different wind-rain conditions are analyzed. The rain phase's impact mechanism on the flow field is revealed. Results show that: Before the meeting, the rain phase will worsen the train's aerodynamic performance. When the wind speed and rainfall intensity is 20 m/s and 400 mm/h, the head train's average lift force (C‾y), yawing and pitching moments (C‾mz) increase by 6.25%, 9.68% and 10.31%, respectively. The rain phase increases the wind-rain load amplitude during the meeting, and the head train's pitching moment increases by 10.7%. The moment is more worthy of attention than that of force under a rainy day, and the change rate of C‾mz is 4.9 times of that of C‾y. The amplification effect of rain on wind-rain loads may endanger the driving safety of trains at the tunnel entrance.

Bed level changes in the surf zone during post-storm beach recovery

The study of post-storm beach recovery is important for economic development and the protection of life in coastal areas. In this study, field observations were conducted for 21 days in the surf zone of Dongdao Beach, Hailing Island, China, after tropical storm “Cempaka”. Data on depth, wave, Eulerian velocity, sediment, three-dimensional topography of the beach, and high-frequency variations in bed-level elevation were collected. The results showed that the beach experienced medium- to low- to medium-energy waves during field observations and covered two complete astronomical tide cycles. Contrary to the effect of wave energy conditions on beaches under normal wave conditions, a higher wave energy during beach recovery can promote silting and accelerate beach recovery. Tidal water level is an important factor affecting beach restoration, and a smaller tidal range is conducive to beach accretion. In a mixed semidiurnal tide, beach erosion and accretion occurred in the “highest tide” and “sub-highest tide” tidal cycles, respectively, and the combined effect of the two affected the change in the bed level in a mixed semidiurnal tide. After the storm, the hydrodynamic forcing mechanism and self-organization process of the sand bar jointly drove the formation of the topography of the bar channel in the surf zone. After the storm stopped, the spectral energy in free surface elevation was mainly distributed in the very low frequency and decayed rapidly at the infragravity band. The very low-frequency pulsation of the surf zone during recovery is a prominent feature of bed-level elevation, depth, and velocity. This study provides a good case for the study of hydrodynamic and bed level changes in the post-storm surf zone, as well as a reference for future studies of the intrinsic mechanisms post-storm beach recovery processes around the world.

Comparing spatially explicit approaches to assess social vulnerability dynamics to flooding

Madagascar is one of the poorest countries in the world, and therefore highly vulnerable to tropical cyclone-induced coastal and inland flooding. This study aims to assess Madagascar's social vulnerability to flooding resulting from tropical storms, in a spatially explicit manner ,accounting for dynamics in vulnerability over time. For this, the research applied three different social vulnerability models in a data-scarce context, for the years 2009 and 2018. Around these years, two tropical cyclones hit the island (in 2008 and 2018). The three models are 1) inductive (social vulnerability index: SoVI), 2) deductive (Weighted Median, WM), and 3) social vulnerability profiling (SVP). Our results show that the most vulnerable regions are in the south of Madagascar, which is consistent across all three models. While the calculated vulnerability score (based on hazard and exposure data) indicated a decrease in vulnerability over time, only the SVP predicted a similar vulnerability decrease, but this comparison is surrounded by uncertainty since the 2008 and 2018 events differ in flood hazard characteristics. The application of the SoVI method in such regions has some limitations, for example, introducing subjective modeling decisions. The application of the WM method could be suitable, but only if relations with variables and social vulnerability are known and understood. The SVP model seems a suitable approach for a first scoping study of social vulnerability, but it provides less insight into spatial variation. The main recommendation from this study is to further focus future research on model validation in data-scarce regions and to assess model accuracy by exploring validation data.

Improvement of GPS displacement measurement accuracy for high-rise buildings by machine learning

This paper aims to enhance the accuracy of displacement responses of high-rise buildings measured by global positioning systems (GPS) under windstorm conditions with machine learning. To this end, a long short-term memory model (LSTM) and a particle swarm optimization-LSTM (PSO-LSTM) model are developed. The models are trained and validated by using field measurements, including strain and acceleration responses, original GPS-measured signals from a 600-m-high skyscraper during Typhoon Kompasu, and the performance of the models is assessed against several standard evaluation criteria. Then, the PSO-LSTM model with optimized hyperparameters is utilized to enhance the accuracy of displacement measurements by the GPS in the skyscraper during Tropical Storm Lionrock. The results of this study reveal that the developed PSO-LSTM model can effectively improve the accuracy of GPS-measured displacements of high-rise buildings under windstorms conditions.

Scale Analysis of Typhoon In-Fa (2021) Based on FY-4A Geostationary Interferometric Infrared Sounder (GIIRS) Observed and All-Sky-Simulated Brightness Temperature

Based on the Final Operational Global Analysis (FNL) data from the National Centers for Environmental Prediction and the ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts, the all-sky brightness temperatures of the Geostationary Interferometric Infrared Sounder (GIIRS) onboard the Fengyun-4A satellite (FY-4A) are simulated, which are then used to investigate Typhoon In-Fa (2021). The results show that the all-sky simulations based on ERA5 and FNL for FY-4A GIIRS channels 27 (716.25 cm−1), 90 (755.625 cm−1), and 417 (960 cm−1) can reproduce well the structure and intensity of Typhoon In-Fa. But the FNL simulations overestimate the typhoon intensity with more high ice clouds around the typhoon center. Fourier analysis of Typhoon In-Fa at severe tropical storm (STS) and typhoon (TY) stages is conducted. The results reveal that the dominant features of Typhoon In-Fa are primarily large-scale, with a relatively small proportion of observations and simulations dedicated to small-scale features at the STS stage. However, the proportion of large-scale features decreases while the amount of small-scale information increases during the TY stage. The purpose of this study is to assess the performance of FY-4A GIIRS all-sky simulations based on the ERA5 and FNL data, as well as to prepare for future all-sky data assimilations.

Assessing the benefit of variational quality control for assimilating Aeolus Mie and Rayleigh wind profiles in NOAA's global forecast system during tropical cyclones

AbstractIn this article, we show a “proof‐of‐concept” study to assess the utility of a variational quality control algorithm in increasing the number of assimilated Aeolus Mie‐cloudy and Rayleigh‐clear winds in National Oceanic and Atmospheric Administration (NOAA)'s global data assimilation and forecast system. The National Centers for Environmental Prediction (NCEP) Variational Quality Control (NCEP‐VQC) algorithm was tuned and applied during the minimization process. This type of quality control uses optimal control theory principles to treat outliers in the probability density function (PDF) of observational departure statistics, assuming that the observation errors follow a family of logistic distributions. In the case of Aeolus Mie‐cloudy and Rayleigh‐clear winds, the NCEP‐VQC algorithm permitted the relaxation of the gross error and one of the recommended ESA quality controls (reject Rayleigh‐clear observations below 850 hPa), assigned adaptive observation weights ranging from 0 to 1, and led to an increase in the number of retained Aeolus observations for the calculation of global analyses, which in turn improved the verification statistics on analyzed tropical storms This article discusses the advantage of implementing the NCEP‐VQC algorithm in the Aeolus data assimilation, the benefits of retaining more wind profiles that contribute to the analysis calculation, and shows improvements in the initialization and short‐term forecasts on several tropical cyclone cases.

Can 210Pb be used as a paleo-storm proxy?

There is increasing interest within the scientific community, as well as the public, to determine if tropical cyclones are increasing in frequency and/or magnitude because of climate change. Unfortunately, the instrumental weather record only extends over the last 100–150 years depending upon location. It is thus desirable to develop paleo-storm proxies to extend the tropical cyclone record to hundreds, if not thousands, of years. Overwash sand deposits, sediment grain size, and various geochemical parameters (e.g., δ13C, δ15N, C/N) have been applied successfully for coastal lake sediments. We present here another potential proxy candidate, the natural radionuclide 210Pb. With a 22.2-year half-life, 210Pb age dating is often used to calibrate other proxies over essentially the same period as the instrumental storm record. We hypothesize here that trends in the 210Pb profiles, in addition to providing chronological information, can provide independent records of storm deposits in some situations. To illustrate how 210Pb may serve as an additional storm proxy, we present data from sediment cores collected from the Chao Phraya Delta (Upper Gulf of Thailand), an area characterized by a rapidly retreating shoreline. Some of the 210Pb profiles mark sedimentary layers with anomalously low excess 210Pb activities interspersed between layers with significant excess activities. We suggest that these layers may result from rapid deposition offshore of older storm-eroded shoreline material. The estimated ages of these 210Pb-deficient layers match, within estimated uncertainties, years when typhoons or tropical storms are reported to have severely eroded the nearby Thai coastline. The matching of the time scales of 210Pb and recorded tropical cyclones, both about 100–150 years, allows this approach to be used to explore for storm deposits and tune geochemical and other proxies for extended study over time scales of several decades or longer.

Evaluation of Power Transmission Lines Hardening Scenarios Using a Machine Learning Approach

Abstract The power transmission infrastructure is vulnerable to extreme weather events, particularly hurricanes and tropical storms. A recent example is the damage caused by Hurricane Maria (H-Maria) in the archipelago of Puerto Rico in September 2017, where major failures in the transmission infrastructure led to a total blackout. Numerous studies have been conducted to examine strategies to strengthen the transmission system, including burying the power lines underground or increasing the frequency of tree trimming. However, few studies focus on the direct hardening of the transmission towers to accomplish an increase in resiliency. This machine learning-based study fills this need by analyzing three direct hardening scenarios and determining the effectiveness of these changes in the context of H-Maria. A methodology for estimating transmission tower damage is presented here as well as an analysis of impact of replacing structures with a high failure rate with more resilient ones. We found the steel self-support-pole to be the best replacement option for the towers with high failure rate. Furthermore, the third hardening scenario, where all wooden poles were replaced, exhibited a maximum reduction in damaged towers in a single line of 66% while lowering the mean number of damaged towers per line by 10%.

Survival of young planted mangroves in a calm bay environment during a tropical cyclone

The efforts of mangrove plantations are not always successful, and the reasons for their failure remain unclear. In this study, the growth of planted mangroves (Kandelia sp.) was monitored in a mangrove forest on Amami Island, Japan, to investigate their survival over the first three years. A strong typhoon passed near the island 16 months after planting. Although the mangroves were planted at the frontal edge of the intertidal zone, most of young mangroves survived without visible damage. The external forces acting on the plants, such as wave forces, storm surge forces, wind drag forces, and oscillatory acceleration, were estimated by applying an integrated analysis model for tropical cyclones, storm surges, wind waves, and plant kinematics. These analyses yielded three main results. First, although large waves over 10 m in height were generated offshore, the mangrove forest was located in the innermost part of the bay, resulting in considerable wave attenuation. Second, the wave period in the bay was much shorter than that in the open ocean, which may have promoted resonance between waves and mangrove plants. The oscillatory acceleration of the young mangroves was estimated to have approximately the same magnitude as that of the gravitational force. Third, the young mangroves could withstand a fairly strong typhoon, but this may have been fortuitous because the timing of the adverse wind wave conditions coincided with low tide. It is estimated that if the tides had been higher, the oscillatory acceleration would have been five times that of the gravitational acceleration. Although this study only presents examples of specific mangrove species in a particular location, the findings can be used to improve the attempts to restore mangrove forests and thereby protect coastal settlements from the adverse effects of tropical cyclones.