Storm Research Articles

Morphodynamics of a composite sand-cobble beach in response to extratropical cyclone Fiona and seasonal wave variability

Climate change is driving higher coastal water levels, and models project accelerated future sea-level rise and coastal storm intensification. These dynamics paired with anthropogenic coastal alterations will drive drastic coastal change worldwide. Composite beaches with mixed sediment sizes warrant detailed study as these exhibit complex morphodynamics in response to changing hydrodynamics due to the distinct transport thresholds of different sediment types. This study uses a novel multi-method approach to investigate a composite sand-cobble beach in Atlantic Canada experiencing a shortening seasonal sand-covered period. Hydrodynamic forcing and associated beach changes were monitored over a focused eight-month period, while satellite-based visual imagery and reconstructed wave data were analyzed over longer periods. Results show that intra-annual wave energy changes drive sand dynamics, with reduced summer wave energy facilitating short-term deposition. Long-term positive trends were identified in late spring wave heights, which likely contribute to the shortening sand-covered period. Seasonal dynamics were overwhelmed by extratropical cyclone Fiona, which made landfall on September 24, 2022, generating significant wave heights up to 6.8 m in the bay, mobilizing sediment, and steepening cobble berms. A new index approach based on visual imagery facilitated the investigation of beach sand appearance/disappearance using the relative redness of sand compared to cobble. Finally, the UAV-based surveys yielded high-resolution orthomosaics and LiDAR-based elevation mapping, and highlighted pronounced longshore variability in erosion and deposition during Fiona. The beach mostly recovered to pre-storm conditions in <4 months, which indicates that proposed beach nourishment activities may only experience temporary success. The longer-term results showing a conversion of sand to cobble suggest that loss of sandy beach habitat is likely to increase, even without shoreline migration or coastal squeeze driven by sea-level rise.

Shoreline superelevation, clues to coastal processes of Great Salt Lake

Coastal processes create the shoreline evidence of Great Salt Lake. Shoreline superelevation is the difference in elevation between still water lake level and the shoreline evidence produced by the lake at that level. Processes of formation include effects of wind strength, fetch, beach attributes, coastline aspect, and coast morphology. A series of field studies from 1986 through 2000 concluded strong storm winds from the northwest contribute to the patterns and magnitude of shoreline superelevation. Weather data for 2020-2023 for Gunnison Island and Hat Island document strong storm winds from the north and northwest for Gunnison Bay and with more complexity for Gilbert Bay. The strongest wind patterns are consistent with the geologic evidence of shoreline superelevation produced by the high lake stands of 1986-1987. Wind strength, fetch, and storm duration cause Great Salt Lake wave regimes. The wave-regimes of Great Salt Lake are fetch-limited due to the size and morphology of the water body. In contrast, the long fetch of large lakes such as Lake Bonneville (the enlarged manifestation of the Great Salt Lake lacustrine system), determines the magnitude and patterns of their shoreline superelevation. Geologic evidence of shoreline superelevation of modern- and paleo- fetch-limited lakes similar to Great Salt Lake may be durable evidence of storm wind direction.

Robust Wind Power Ramp Control Strategy Considering Wind Power Uncertainty

Recent climate change has worsened the risk of extreme weather events, among which extreme offshore wind storms threaten secure operation by inducing offshore wind power ramps. Offshore wind power ramps cause the instantaneous power fluctuation of interconnected onshore grids and may lead to unexpected load shedding or generator tripping. In this paper, considering offshore wind power uncertainties, we propose a novel robust coordinated offshore wind power ramp control strategy by dispatching thermal units, energy storage systems, and hydrogen storage systems cooperatively. First, the impact of extreme wind storms on an offshore wind farm output power ramp is analyzed, and the general framework of offshore wind power ramp control is presented based on the two-stage robust optimization considering the dual uncertainties of load demand and wind power. Second, a coordinated wind power ramp control model is established considering the operational characteristics of different ramp control sources such as thermal units, energy storage systems, and offshore wind farms. Third, a robust ramp control strategy is developed using the column-and-constraint generation (CC&amp;G) algorithm. Simulation results show the effectiveness of the proposed ramp control strategy.

Assessing RRFS versus HRRR in Predicting Widespread Convective Systems over the Eastern CONUS

Abstract This study provides a comparison of the operational HRRR version 4 and its eventual successor, the experimental Rapid Refresh Forecast System (RRFS) model (summer 2022 version), at predicting the evolution of convective storm characteristics during widespread convective events that occurred primarily over the eastern United States during summer 2022. In total 32 widespread convective events were selected using observations from the MRMS composite reflectivity, which includes an equal number of MCSs, quasi-linear convective systems (QLCSs), clusters, and cellular convection. Each storm system was assessed on four primary characteristics: total storm area, total storm count, storm area ratio (an indicator of mean storm size), and storm size distributions. It was found that the HRRR predictions of total storm area were comparable to MRMS, while the RRFS overpredicted total storm area by 40%–60% depending on forecast lead time. Both models tended to underpredict storm counts particularly during the storm initiation and growth period. This bias in storm counts originates early in the model runs (forecast hour 1) and propagates through the simulation in both models indicating that both miss storm initiation events and/or merge individual storm objects too quickly. Thus, both models end up with mean storm sizes that are much larger than observed (RRFS more so than HRRR). Additional analyses revealed that the storm area and individual storm biases were largest for the clusters and cellular convective modes. These results can serve as a benchmark for assessing future versions of RRFS and will aid model users in interpreting forecast guidance.

Perception of climate change impacts, urbanization, and coastal planning in the Gaeta Gulf (central Tyrrhenian Sea): A multidimensional approach

&lt;abstract&gt; &lt;p&gt;The coasts, with their intricate combination of natural and anthropogenic fragilities, can always be considered a crucial component in the geography of risk and territorial governance. Furthermore, coastal areas worldwide are currently facing profound and immediate impacts of climate change, presenting unparalleled challenges for both ecosystems and coastal communities. In these contexts, high socio-environmental vulnerability has often been linked to planning and management practices that, at times, have exacerbated coastal exposure, making it more prone to extreme natural phenomena, such as coastal floods and storm surges, as well as degradation. The case of the Gaeta Gulf, a largely urbanized part of the central Tyrrhenian coast in Italy that encompasses two administrative areas between the northern Campania and the southern Lazio Regions, provides an opportunity to investigate these criticalities both along the coastline and within the interconnected inland areas. This research aims to understand how administrations and communities perceive, experience, and understand the coastal risks and challenges posed by climate change, as well as their level of information and preparedness to address such risks. These aspects will be analyzed through a multidisciplinary approach, shedding light on the political, social, environmental, and economic practices in these areas, and the potential implications for coastal planning policies. In addition, this contribution presents the results of a qualitative survey involving the administration of questionnaires related to the perception of climate change impacts on the coasts and the level of information on the mitigation and adaptation practices within the communities living in these areas.&lt;/p&gt; &lt;/abstract&gt;

Determination of orientation parameters and automatic wind wheel speed control

The design of the wind wheel and storm protection will help greatly improve the performance and safety of the wind-powered water-lifting unit. This, in turn, will contribute to improving the quality of life of rural residents. The authors of this study developed the design of a wind-powered water-lifting unit. The research aims to develop a wind-powered water-lifting unit with a capacity of up to 2.5 m3/hour, resistant to hurricane gusts of wind, with automatic control of wind wheel speed. The mathematical expressions used consider the force of the airflow and the screen area of the side blade, which takes the plane of the wind wheel out of the wind when it increases to values above the permissible operating wind speed. Numerical methods were used to calculate the forces on the spring returning the wind wheel plane to the initial position at different wind speeds. The dependences of the effect of airflow velocity on the free rotation of the wind wheel, i.e., without its running out of the wind, have been determined.

Study On Flood Management and Analysis Using Geographical Information Systems On Godavari, Konaseema District, India

The Konasema area in Andhra Pradesh, India is prone to flooding due to coastal storms, storm surges, and river overflows. Flooding can be dangerous and pose a threat to human life and property. Floods are unpredictable and can happen suddenly, causing significant damage. Therefore, planning for flood management is essential for rescuing flood victims, resolving flood issues, and implementing preventive measures. In recent years, flooding has become more frequent in the Konaseema area, especially in the Godavari river's and near surrounding areas. Heavy floods from the Godavari River have been caused severe flooding in nearby konaseema villages. Flood management by water resource planners using GIS (Geographic Information System) developed land use and land cover maps, Flood zone maps in the study region. This study aims to analyse impact of Godavari River floods in Konaseema and East Godavari district Andhra Pradesh, India. DEM (Digital Elevation Model) is generated and prepared various thematic maps of Flood prone regions. The GIS generated flood impacted maps are analyzed and these maps will help to rescue the people from flood regions and migrate them in time to the safe zones.

The climatology and classification of coastal storms on the Southeastern coast of Rio de Janeiro State, Brazil

The climatology and classification of coastal storms on the Southeastern coast of Rio de Janeiro State, Brazil

ВОДНЫЕ РАСТЕНИЯ ПРИБРЕЖНОЙ ЗОНЫ КАСПИЙСКОГО МОРЯ И ИХ ПРАКТИЧЕСКОЕ ПРИМЕНЕНИЕ

Seagrasses of the Zosteraseae family are a valuable biological resource and raw material used for the production of zosterol, a polysaccharide of pectic nature, since the early 40-s of the XX century. Currently, the volumes of Zostera reserves in the Caspian Sea have not been sufficiently studied. The places of mass growth of Zostera, as well as the places of mass accumulation of storm emissions of Zostera formed after seasonal storm winds, remain unknown. The presented research results are devoted to determining the species composition of aquatic vegetation in the coastal zone of the Caspian Sea, as well as the problem of collecting and storing storm emissions of the sea grass Nanozostera noltei. The coast of the Caspian Sea with a length of about 150 km from the Agrakhan Bay to the city of Izberbash was explored. Dominant species of aquatic plants were found: Ruppia maritima, Ceratophyllum demersum, Laurencia caspica, Cladophora, storm surges of N. noltei. Technological instructions for the collection and preparation of dried sea grass of the Zosteraseae family have been developed. It was revealed that Nanozostera and Laurencia are of particular practical interest.

Documenting the Progressions of Secondary Eyewall Formations

Abstract Intense tropical cyclones can form secondary eyewalls (SEs) that contract toward the storm center and eventually replace the inner eyewall, a process known as an eyewall replacement cycle (ERC). However, SE formation does not guarantee an eventual ERC, and often, SEs follow differing evolutionary pathways. This study documents SE evolution and progressions observed in numerous tropical cyclones, and results in two new datasets using passive microwave imagery: a global subjectively labeled dataset of SEs and eyes and their uncertainties from 72 storms between 2016 and 2019, and a dataset of 87 SE progressions that highlights the broad convective organization preceding and following an SE formation. The results show that two primary SE pathways exist: “No Replacement,” known as “Path 1,” and “Replacement,” known as the “Classic Path.” Most interestingly, 53% of the most certain SE formations result in an eyewall replacement. The Classic Path is associated with stronger column average meridional wind, a faster poleward component of storm motion, more intense storms, weaker vertical wind shear, greater relative humidity, a larger storm wind field, and stronger cold-air advection. This study highlights that a greater number of potential SE pathways exist than previously thought. The results of this study detail several observational features of SE evolution that raise questions about the physical processes that drive SE formations. Most important, environmental conditions and storm metrics identified here provide guidance for predictors in artificial intelligence applications for future tropical cyclone SE detection algorithms.

The Classification of Tropical Storm Systems in Infrared Geostationary Weather Satellite Images Using Transfer Learning

The Classification of Tropical Storm Systems in Infrared Geostationary Weather Satellite Images Using Transfer Learning

Deep Learning Image Segmentation for Atmospheric Rivers

Abstract The identification of atmospheric rivers (ARs) is crucial for weather and climate predictions as they are often associated with severe storm systems and extreme precipitation, which can cause large impacts on society. This study presents a deep learning model, termed ARDetect, for image segmentation of ARs using ERA5 data from 1960 to 2020 with labels obtained from the TempestExtremes tracking algorithm. ARDetect is a convolutional neural network (CNN)-based U-Net model, with its structure having been optimized using automatic hyperparameter tuning. Inputs to ARDetect were selected to be the integrated water vapor transport (IVT) and total column water (TCW) fields, as well as the AR mask from TempestExtremes from the previous time step to the one being considered. ARDetect achieved a mean intersection-over-union (mIoU) rate of 89.04% for ARs, indicating its high accuracy in identifying these weather patterns and a superior performance than most deep learning–based models for AR detection. In addition, ARDetect can be executed faster than the TempestExtremes method (seconds vs minutes) for the same period. This provides a significant benefit for online AR detection, especially for high-resolution global models. An ensemble of 10 models, each trained on the same dataset but having different starting weights, was used to further improve on the performance produced by ARDetect, thus demonstrating the importance of model diversity in improving performance. ARDetect provides an effective and fast deep learning–based model for researchers and weather forecasters to better detect and understand ARs, which have significant impacts on weather-related events such as floods and droughts.

Digitization of Precipitation

In addressing the challenges posed by outdated and insufficiently documented urban drainage, irrigation, and storm systems, this study aims to revolutionize urban infrastructure through digitalization. Leveraging insights from 25 invited experts representing various organizations, including highway committees, city administration, and city improvement departments, the research focuses on the city of Tashkent. Employing geodesic methodologies, the team meticulously documented the locations and conditions of underground facilities, utilizing city maps and visual aids provided by specialists. The resulting digital database, curated by geodesists, identifies responsible personnel, object types, and areas requiring attention. This innovative initiative not only fills crucial knowledge gaps but also lays the foundation for enhanced maintenance strategies and resource optimization, offering a paradigm shift in urban planning and management. Highlights: Comprehensive Digital Mapping: The study employs advanced geodesic techniques to create a detailed digital map of aging drainage, irrigation, and storm systems, offering a comprehensive overview of their current conditions. Knowledge Integration from Diverse Experts: Gathering insights from 25 experts across various organizations, the research consolidates valuable information on underground facilities, enabling a holistic understanding of the urban infrastructure challenges. Strategic Resource Optimization: The resulting digital database not only identifies critical maintenance areas but also assigns responsibility, paving the way for strategic resource allocation and efficient urban infrastructure management. Keyword: Urban Infrastructure, Digitalization, Drainage Systems, Geodesic Mapping, Resource Optimization

Economic loss assessment of typhoon-induced storm surge disasters in the South China Sea based on GSA-BP model

In the context of global climate warming and rising sea levels, the frequency of tropical cyclones in the South China Sea region has shown a significant upward trend in recent years. Consequently, the coastal areas of the South China Sea are increasingly vulnerable to storm surge disasters induced by typhoon, posing severe challenges to disaster prevention and mitigation in affected cities. Therefore, establishing a multi-indicator assessment system for typhoon storm surges is crucial to provide scientific references for effective defense measures against disasters in the region. This study examines 25 sets of typhoon storm surge data from the South China Sea spanning the years 1989–2020. A comprehensive assessment system was constructed to evaluate the damages caused by storm surges by incorporating the maximum wind speed of typhoons. To reduce redundancy among multiple indicators in the assessment system and enhance the stability and operational efficiency of the storm surge-induced disaster loss model, the entropy method and bootstrap toolbox were employed to process post-disaster data. Furthermore, the genetic simulated annealing algorithm was utilized to optimize a backpropagation neural network intelligent model (GSA-BP), enabling pre-assessment of the risks associated with storm surge disasters induced by typhoon and related economic losses. The results indicate that the GSA-BP model outperforms the genetic algorithm optimized BP model (GA-BP) and the simulated annealing algorithm-optimized BP model (SA-BP) in terms of predicting direct economic losses caused by storm surges. The GSA-BP model exhibits higher prediction accuracy, shorter computation time, and faster convergence speed. It offers a new approach to predicting storm surge losses in coastal cities along the South China Sea.

Влияние опасных гидрометеорологических явлений на эксплуатацию транспортного перехода через Керченский пролив

The article considers the natural and climatic conditions of the Kerch Strait, the Taman Peninsula and the Kerch Peninsula through which passes the transport crossing that connects the Republic of Crimea with the Russian mainland. The paper reflects the main features of the formation of dangerous hydrometeorological phenomena in the Kerch Strait. Hydrometeorological conditions of formation of ice and wind-wave regimes and other dangerous hydrological and hydrometeorological phenomena in the Kerch Strait are highlighted based on the coastal and expedition observations, measurements of currents at automatic buoy stations, numerical modeling of water dynamics in the Kerch Strait for different types of atmospheric effects on the grid with high spatial resolution. The research shows that in the coastal zone and on the Kerch Strait water area periodically there are dangerous hydrometeorological phenomena, such as ice blockages on the bridge piers, strong storm or hurricane wind, heavy precipitation, fogs, which significantly worsen the visibility and complicate marine cargo transportation in the Strait, thunderstorms and tornados. The data presented in this work may be the basis for the assessment of the current state of the environment within the environmental support of the projects, as well as the subsequent ice and hydrometeorological monitoring in the area of the Crimean bridge.

Water Quality and Flooding Impact of the Record-Breaking Storm Gloria in the Ebro Delta (Western Mediterranean)

Extreme events are increasing in frequency and severity due to climate change, making the littoral zone even more vulnerable and requiring continuous monitoring for its optimized management. The low-lying Ebro Delta ecosystem, located in the NW Mediterranean, was subject to Storm Gloria in the winter of 2020, the most severe coastal storm registered in the area in decades and one of the most intense ever recorded in the Mediterranean. This event caused intense rainfall, severe flooding, the erosion of beaches, and the destruction of coastal infrastructures. In this study, the Landsat-8 and Sentinel-2 satellites were used to monitor the flooding impact and water quality status, including chlorophyll-a, suspended particulate matter, and turbidity, to evaluate the pre-, syn-, and post-storm scenarios. Image processing was carried out using the ACOLITE software and the on-the-cloud Google Earth Engine platform for the water quality and flood mapping, respectively, showing a consistent performance for both satellites. This cost-effective methodology allowed us to characterize the main water quality variation in the coastal environment during the storm and detect a higher flooding impact compared to the one registered three days later by the Copernicus Emergency Service for the same area. Moreover, the time series revealed how the detrimental impact on the water quality and turbidity conditions was restored two weeks after the extreme weather event. While transitional plumes of sediment discharge were formed, no phytoplankton blooms appeared during the study period in the delta. These results demonstrate that the workflow implemented is suitable for monitoring extreme coastal events using open satellite imagery at 10–30 m spatial resolution, thus providing valuable information for early warning to facilitate timely assistance and hazard impact evaluation. The integration of these tools into ecological disaster management can significantly improve current monitoring strategies, supporting decision-makers from the local to the national level in prevention, adaptation measures, and damage compensation.

The importance of forests in the environment

The importance of forests in the environment

State of the shoreline since Hurricane Sandy: Summary of changes at New Jersey Beach Profile Network (NJBPN) sites

Following the devastating impacts from Hurricane Sandy in October 2012, Congress passed Public Law 113-2 in January 2013 which provided the funding for the U.S. Army Corps of Engineers (USACE) to restore authorized federal coastal storm risk management projects in New Jersey to their original design templates. Between 2013 and 2016, approximately 28.3 million cubic yards of sand were used to rebuild the beaches and dunes along 92.7 miles of the 127-mile Atlantic Ocean shoreline (an average of 283,290 cubic yards per mile). Between 2017 and 2019, initial federal beachfill had been completed at the northern Ocean County beaches (an additional 11 million cubic yards). A review of the changes to the New Jersey Beach Profile Network (NJBPN) locations indicates that this influx of sand benefitted nearly all shorelines as well as former erosion hot spots. Examples are provided from each of the four counties (Monmouth, Ocean, Atlantic, Cape May) to show trends in profile volume and shoreline position. State support of beach nourishment continues through the efforts of the New Jersey Department of Environmental Protection and its Office of Coastal Engineering.

Predicting the risk of tree fall onto railway lines

Tree fall onto railway lines puts passengers at risk and causes large economic losses due to disruption of train services and damage to infrastructure. Railway lines in Germany are vulnerable to tree fall because of the large number of trackside trees that exist in that country with approximately 70% of all railway lines being tree-lined. In this paper we first tested whether a hybrid-mechanistic tree wind damage model, ForestGALES, could identify the sections of the railway network affected by tree fall in two federal states of Germany, Northrhine-Westphalia (NRW) and Thuringia (TH). We secondly tested whether the model, in combination with meteorological forecast models, could predict where tree fall occurred during a damaging windstorm. We used information on tree characteristics derived from LiDAR and aerial photography along the railway line network in NRW and TH to calculate the critical wind speed (CWS) at which damage is expected to happen for every individual tree as a function of its size and species, and the underlying soil. The railway network was then divided into 500 m sections and the statistics of the CWS, tree height, and species composition (broadleaf/conifer mix) within each section were calculated. Analysis of past tree fall events recorded by Deutsche Bahn AG (DB) showed that there was a significantly lower minimum CWS and significantly greater maximum tree height in sections that had recorded damage. In a second step we compared the calculated CWS values for all trees against downscaled wind speed assessments across the two federal states during Storm Friederike (named Storm David internationally) on 18 January 2018 and tested the ability of the model to discriminate sections with recorded damage during the storm. Excellent model discrimination was found with an AUC value of 0.82 and an overall model accuracy of 74.2%. The first test showed that the ForestGALES model with precise individual tree information can identify the sections of a railway network most vulnerable to tree fall. The second analysis showed, for the one storm tested, that the ForestGALES model when combined with predicted storm wind speeds can identify the most probable sections of the railway network to experience tree fall during an approaching damaging storm. Such information could be of value in firstly planning remedial work along railway lines, and secondly preparing the railway network ahead of a major storm.

Assessing Storm Surge Multiscenarios Based on Ensemble Tropical Cyclone Forecasting

AbstractEnsemble forecasting is a promising tool to aid in making informed decisions against risks of coastal storm surges. Although tropical cyclone (TC) ensemble forecasts are commonly used in operational numerical weather prediction systems, their potential for disaster prediction has not been maximized. Here we present a novel, efficient, and practical method to utilize a large ensemble forecast of 1,000 members to analyze storm surge scenarios toward effective decision making such as evacuation planning and issuing surge warnings. We perform the simulation of TC Hagibis (2019) using the Japan Meteorological Agency's (JMA) nonhydrostatic model. The simulated atmospheric predictions were utilized as inputs for a statistical surge model named the Storm Surge Hazard Potential Index to estimate peak surge heights along the central coast of Japan. We show that Pareto‐optimized solutions from an ensemble storm surge forecast can describe potential worst (maximum) and optimum (minimum) storm surge scenarios. These solutions exemplify a diversity of trade‐off surge outcomes across diverse coastal locations, reflecting variations in coastal geometry, including bathymetry. For example, some of the Pareto‐optimized solutions that illustrate worst surge scenarios for inner bay locations are not necessarily accountable for bringing severe surge cases in open coasts. We further emphasize that an in‐depth evaluation of Pareto‐optimal solutions can shed light on how meteorological variables such as track, intensity, and size of TCs influence the worst and optimum surge scenarios, which is not clearly quantified in current multiscenario assessment methods such as those used by JMA/National Hurricane Center in the United States.