Extreme Wind Research Articles

P-737. Highly Resistant Myroides Species Infections: Insights from a Comprehensive Case Series and Antibiogram Analysis

Abstract Background Myroides species (spp.) are rare opportunistic gram-negative bacteria that are highly resistant to commonly used empiric antibiotics. Able to survive in natural and hospital environments, these microbes pose a growing risk of resistant nosocomial infections. Here we present a case series and antibiogram of Myroides infections at an academic medical center, highlighting this potential pathogen’s antibiotic sensitivity to aid clinicians in choosing effective antimicrobial therapies. Antibiogram Methods Retrospective chart review of all patients diagnosed with Myroides spp. infection at Henry Ford Health between January 1, 2019 and December 31, 2023. Patient characteristics, treatments, and outcomes were analyzed. An antibiogram of Myroides spp. susceptibility to 10 antibiotics was generated. Results A total of 43 patients (median age 62 yr; range 30-94) presented with 46 Myroides spp. infections. Positive cultures were evenly distributed between hospitalizations (50%) and outpatient (50%) settings. Of the 46 infections, 35 (76%) were in a lower extremity wound. Of the 43 patients, 17 (40%) had sepsis at presentation, including 8 (13%) with Myroides bacteremia. Most Myroides spp. cultures (83%) grew additional organisms, including E. faecalis (34%), S. aureus (21%), Proteus mirabilis (21%), and Pseudomonas spp. (16%). Empiric antibiotics were prescribed in 35/46 cases (76%), mostly vancomycin (17/35) and cefepime (9/35); however, Myroides spp. were sensitive to the empiric agents in only 4 cases (11%). Antibiogram analysis showed 100% susceptibility to meropenem and 0%-50% susceptibility to 9 other agents (Table). A total of 5 (12%) patients died, and 6 patients (14%) required readmission within 1 month of treatment. Conclusion Myroides spp. exhibited significant resistance to most empiric antibiotics. Our antibiogram analysis revealed meropenem as the sole effective empiric antibiotic for this opportunistic pathogen. We recommend the prompt empirical use of meropenem when Myroides spp. are identified on antimicrobial culture. Disclosures All Authors: No reported disclosures

P-743. Correlating Climate Conditions with Pseudomonas aeruginosa Prevalence in Diabetic Foot Infections within the United States

Abstract Background Diabetes-related foot infections (DFIs) are often difficult to treat and can result in amputation or even death if allowed to progress. The 2023 ‘International Working Group on the Diabetic Foot (IWGDF)/Infectious Disease Society of America (IDSA) Guidelines on the Diagnosis and Treatment of Diabetes-Related Foot Infections’ provides recommendations on which pathogens to target with empiric antibiotic therapy depending on which solar climate region a patient is from. The United States (US) has numerous different climate zones that can be classified using the US Department of Energy International Energy Conservation Code (IECC), which utilizes annual temperature and precipitation data to assign a climate designation to each county within the US. IECC Climate Map Methods This study was a retrospective national cohort study of all Veterans aged 18 years of age or older between 1/1/2010 – 3/23/2024 with diabetes mellitus and a cultured lower extremity wound. National Veteran data was obtained from the VA Corporate Data Warehouse (CDW) via Structured Query Language (SQL) Server Management Studio. SQL coding was used to acquire demographic information, ICD diagnoses, lower extremity culture results, and Charlson Comorbidity Index for all patients who met inclusion criteria. Pseudomonas Prevalence Results The prevalence of Pseudomonas significantly varied between US climates. Pseudomonas was most prevalent within the Hot Humid climate, where it was isolated in 13.2% of DFI cultures. Pseudomonas was least prevalent within the Cool Dry climate, where it was only isolated in 7.2% of cultures. Conclusion The prevalence of DFI organisms varies within different US climates. Utilization of local climate information may allow for more accurate and targeted empiric antibiotic selection when treating DFIs. Disclosures All Authors: No reported disclosures

Family Impacts of Diabetic Extremity Wound Caregiving: A Qualitative Multiple Case Study Report.

More than three million people in the United States are treated for diabetic extremity wounds every year, with numerous physical, financial, and psychosocial impacts not only to patients but also their families who care for them. This study examined the experiences of families who care for adult members with a diabetic extremity wound. A qualitative multiple case study was conducted with four family cases recruited from an urban academic medical center in the Southeastern United States, with data collection consisting of individual interviews, demographic survey instruments, and family caregiving genogram construction. Individual- and cross-case synthesis was completed using reflexive thematic analysis. Themes related to impacts on family functioning, the caregiving experience, and formal health care utilization were identified. These findings provide insight into the experiences of families living with diabetic extremity wounds, as well as clinical and research directions for the future.

Effects of meteorological factors on the retention of particulate matter in lawn grass blades

Plant leaves can reduce the concentration of atmospheric particulate matter (PM) by absorbing it in the air, and this mitigates the deleterious human health effects of PM. However, the ability of plant leaves to retain dust is limited and varies continually due to various meteorological factors such as rainfall, extreme wind speed, and PM10 concentrations. Here, we measured the ability of seven types of turfgrass with leaves similar in macromorphology but varying in micromorphology to retain dust particles of different sizes; we also analyzed the effects of various meteorological factors, such as rainfall, maximum wind speed, and PM10 concentration, on the ability of leaves to retain particles of different sizes. There were significant differences in the ability of the seven types of turfgrass to retain particles of different sizes; the dust retention capacity of Zoysia sinensis was the strongest(2.04 g·m-2), and that of Festuca elata was the weakest(1.39 g·m-2). The elution rates of PM>10 after rainfall of 3 mm and 4 mm were significantly higher than those of PM2.5-10 and PM2.5; the elution rates of PM>10, PM2.5-10, and PM2.5 increased as the amount of rainfall increased. When the amount of dust on leaves is low, wind promotes increases in leaf PM retention. When the blade retains a certain amount of dust, the maximum wind speed is greater than 9.1 m·s-1, which leads to a decrease in the dust retention of lawn grass blades. The concentrations of PM10 and PM2.5 were positively correlated with the retention of particles of different particle sizes. Therefore, evaluations of the dust retention ability of plant leaves require consideration of the effects of local rainfall, maximum wind speed, PM10 concentration, and other factors.

Comparative analysis of seasonal wind power using Weibull, Rayleigh and Champernowne distributions

Accurate statistical modeling of wind speed variability is crucial for assessing wind energy potential, particularly in regions with low wind speeds and significant calm hours. This study evaluates the Champernowne distribution as a novel model for wind speed analysis, comparing its performance with the two-parameter Weibull, three-parameter Weibull, and Rayleigh-Rice distributions. Wind speed data at 10 m hub height over three years (2021–2023) from Ben Guerir, Morocco, were analyzed using statistical metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Bias Error (MBE), Coefficient of Determination (R2), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The Champernowne distribution outperformed the other models across all metrics, achieving the lowest RMSE (0.00036), MAE (0.00022), AIC (650.52), and BIC (689.46), and the highest R2 (0.99998). Its ability to capture calm hours and extreme wind speeds provided more accurate power density estimates, with errors averaging 23%, compared to 33% and 42% for the Weibull and Rayleigh-Rice distributions, respectively. Despite low mean wind speeds (2.7 m/s), Ben Guerir’s ground-based power density ranged from 18 to 54 W/m2. The results suggest that conventional large-scale wind energy projects are unsuitable for Ben Guerir. Instead, small Vertical-Axis Wind Turbines (VAWTs) or alternative strategies should be considered. The Champernowne distribution’s robustness makes it a valuable tool for wind energy assessments, especially in regions with intermittent wind patterns, providing a foundation for more accurate modeling and energy planning.

Comparison of Aerodynamic Effects on the Commonwealth Advisory Aeronautical Research Council (CAARC) Tall Building Model Tested in Two Wind Tunnel Laboratories

Wind tunnel test results can be influenced by various factors such as the blockage ratio and scaling ratio. These factors may introduce errors in the experimental outcomes, impacting the accuracy and reliability of the data obtained. This study quantitatively assesses consistency and identifies uncertainty sources to enhance result uniformity across various wind tunnel laboratories. This study conducted a systematic comparison between different wind tunnels in terms of rigid model pressure measurement wind tunnel experiments on the same Commonwealth Advisory Aeronautical Research Council (CAARC) standard tall building model. The study analyzes and discusses the results of mean and root-mean-square (RMS) wind pressure coefficients, peak factors, extreme wind pressure coefficients, probability density distributions, and base overturning force coefficients. The results indicated that in the open-circuit wind tunnel laboratory, the mean wind pressure coefficient is underestimated in the positive pressure region and overestimated in the negative pressure region. This is due to the static pressure which significantly decreases the streamwise direction within the test section, and the difference in static pressure is logarithmically proportional to the mean wind speed. Additionally, dynamic pressure is uniformly distributed along the test section axis. The inaccurate measurement of static pressure leads to these results. To address this issue, an indirect measurement method was employed to correct the static pressure results and reduce the error in the mean wind pressure coefficient to within 10%. Furthermore, differences in turbulence integral scale result in an error of up to 16% in the RMS wind pressure coefficient. Therefore, when conducting rigid model pressure measurement wind tunnel experiments, especially in open-circuit wind tunnel laboratories, careful consideration should be given to the influence of static pressure drop and integral length scale of turbulence.

Integrating Artificial Intelligence in Medical Writing: Balancing Technological Innovation and Human Expertise, with Practical Applications in Lower Extremity Wounds Care.

Artificial Intelligence (AI) is revolutionizing medical writing by enhancing the efficiency and precision of healthcare communication and health research. This review explores the transformative integration of AI in medical writing, highlighting its dual role of enhancing efficiency while maintaining the crucial elements of human expertise. AI technologies, including natural language processing and AI-driven literature review tools, have significantly advanced, facilitating rapid draft generation, literature summarization, and consistency in medical documentation. Key applications include aiding study design, enhancing content drafting, and optimizing literature reviews through specific AI tools. Moreover, this review delves into practical applications of AI in the context of lower extremity wounds, specifically ischemic leg ulcers, demonstrating how AI can streamline the synthesis of relevant literature. While AI presents notable advantages, it also raises ethical concerns, such as potential biases and data privacy issues, highlighting the need for human oversight in the writing process. A proposed future framework suggests that AI could take over routine tasks, allowing medical writers to devote more attention to analytical and ethical aspects. Additionally, there is a strong need for further research on the cost-effectiveness of both clinical trials utilizing AI interventions and the incorporation of AI in medical writing. Ultimately, balancing the integration of AI in medical writing promises to improve both healthcare communication and health research, ensuring the production of high-quality, patient-centric and research-focused content.

A Case Series Analysis of Missed Foreign Bodies in the Upper Extremity Following Penetrating Traumatic Wounds: New Insights into Old Challenges

Abstract Background: Retained foreign bodies (FBs) are uncommon, despite the higher prevalence of traumatic puncture wounds. Injuries that cause a feeling of a foreign body should be examined thoroughly. Graphite or other pigmenting elements, wooden fragments, glass, or other vegetative FB are encountered post-trauma. Persistent sensation and associated pain are indicators that a foreign body has to be removed. Objectives: The objectives of the present study were to evaluate and prompt management of patients presenting with retained FBs following penetrating traumatic wounds in the upper extremity. Materials and Methods: All the patients presenting with foreign body sensation with history of penetrating injuries were evaluated and managed promptly in the single surgical unit of a tertiary healthcare centre for a duration of one year. Results: Accurately identifying high-risk individuals with a thorough medical history and physical examination is the first step in a recommended strategy. The majority of FB are the result of trauma or accidental injury. FB is possible in any wound. The next course of action should be obtaining plain film radiographs with views in at least two anatomical planes if the patient or the practitioner has a reasonable suspicion. If the clinical examination is inconclusive and only radiopaque objects (metal, glass, or gravel) are suspected, the provider may stop here. However, an ultrasound check of the area needs to be done if radiolucent things such as thorns, wood, or plastic are detected. Depending on the degree of suspicion, the physician may decide to proceed with CT or MRI if the foreign body is still not found. Prior to removing a foreign body, radiography or ultrasonography should be used for wound exploration and early imaging. It is important to go over the advantages and risks of removal with the patient. While certain FB might be kept in situ, if there is a high risk of issues, removal should be taken into consideration, as seen in our cases for the effective management of the patients. Conclusion: Effective foreign body removal requires a patient who is cooperative and has adequate vision of the site. Sufficient analgesia combined with judicious use of sedation and anxiolytics may prove beneficial. It is advised to irrigate the wound with a stream or normal saline water following the removal of FB.

The 20‐Year Highest Tropical Cyclone‐Generated Waves Associated With the Maximum Energy of Seismic Noises

AbstractThe extreme winds of tropical cyclones generate high waves over the ocean, causing severe damage to offshore facilities and coastal communities. Disaster mitigation requires accurate prediction and forecasting of the highest potential waves. However, the physics of wave development is not fully understood, and the number of mid‐ocean observations during extreme tropical cyclones is extremely insufficient. Therefore, physically and statistically evaluating the highest potential waves is difficult. However, ocean waves excite seismic noise (microseisms). Although source sites of microseisms under specific tropical cyclones have been identified by case studies, the extreme ocean wave magnitude under tropical cyclones have not been systematically analyzed using long‐term historical records. Here, we, for the first time, utilize long‐term microseisms observed by seismic observation networks to associate the historical maximum microseisms events with the highest tropical cyclone‐generated wave events around Japan. We show that Typhoon Wipha in 2013, Typhoon Lan in 2017, and Typhoon Hagibis in 2019 were the maximum microseisms events in the past 20 years based on microseism energy within an 8–10 s period. We associate the events with the highest wave heights and the largest wave‐induced sea surface pressures generated by tropical cyclones. Although ocean wave models have a large uncertainty of tropical cyclone‐generated extreme waves, microseisms observations can endorse the results of an ocean wave model even if lack of direct ocean wave observation under tropical cyclones. Furthermore, rich information of microseisms on wave development and propagation has a potential to proceed understanding of the extreme wave physics.

A simplified statistical model for regional offshore typhoon wind risk assessment

The accuracy of typhoon wind risk assessment methods is of great significance for offshore or coastline areas. Due to the practical limitations of typhoon measurement, it is common to perform Monte-Carlo simulation (MCS) to extrapolate such data to thousands of years to estimate the extreme wind speed. However, there are technical limitations for engineering applications of this technique, and the computational costs are significant for assessment on a regional level. For the preliminary typhoon risk assessment for a region, a simplified statistical method (SSM) is proposed in this paper. In the proposed method, the extreme wind speed is estimated with the extended “Improved Method of Independent Storms” (XIMIS) method, based on simulated wind field results using the best track typhoon data, i.e., the historical typhoon record information. The results of the simplified statistical method are validated by comparison with local code wind speeds and with full track MCS results in the Northwest Pacific basin using the best track database of the Joint Typhoon Warning Center (JTWC). Finally, the proposed SSM method is used to produce a wind map for typhoon risk assessment in offshore and coastal regions of southeast China, with high efficiency and acceptable accuracy.

Mars Nightside Ionospheric Response During the Disappearing Solar Wind Event: First Results

AbstractWe investigated, for the first time, the impact of the disappearing solar wind (DSW) event [26–28 December 2022] on the deep nightside ionospheric species using MAVEN data sets. An enhanced plasma density has been observed in the Martian nightside ionosphere during extreme low solar wind density and pressure periods. At a given altitude, the electron density surged by ∼2.5 times, while for ions (NO+, O2+, CO2+, C+, N+, O+, and OH+), it enhanced by > 10 times, respectively, compared to their typical average quiet‐time periods. This investigation suggests that an upward ionospheric expansion likely took place in a direct consequence to the contrasting low dynamic/magnetic pressure and relatively higher nightside ionospheric pressure (by 1–2 orders) causing an increased ionospheric density. Moreover, the day‐to‐night plasma transport may also be a contributing factor to the increased plasma density. Thus, this study offers a new insight about planetary atmosphere/ionosphere during extreme quiescent solar wind periods.

Effect of designed stiffener configuration on natural frequency and buckling behaviour of 5 MW NREL wind turbine blade structure using FE method

ABSTRACT Long and flexible offshore wind turbine blades are easily damaged during extreme wind conditions (typhoon or tornado). A parametric study was conducted in this work using the finite element method to study the effect of stiffener configurations, including stiffener number, thickness, and spacing, on the aeroelastic response and buckling of the wind turbine blade by modeling the National Renewable Energy Laboratory (NREL) 5 MW blade structure design. This study investigates wind turbine blade stiffeners' buckling behaviour and natural frequencies. The results show that the critical buckling load value increases significantly as the number of stiffeners, stiffener thickness, and spacing width between stiffeners increase. The modal analysis results show that the variation in the number and thickness of stiffeners increases the natural frequencies of the flaps and torsional modes but decreases the edgewise mode. Variation of the distance between stiffeners increases the flapwise and edgewise modes' natural frequencies.

Unveiling Nepal’s Hydro-Climatic Diversity: A Comprehensive Study from East to West

This study delves into the comprehensive analysis of temperature, precipitation, and wind patterns across four distinct blocks in Nepal, shedding light on regional climate variations. Examining a 30-year (1990 – 2020) dataset, temperature variations reveal the East: Block 1 consistently experiencing higher average temperatures. Findings align with existing research, emphasizing local geographical features influence on temperature gradients. The precipitation analysis indicates the Block 1 receives the highest rainfall, correlating with established monsoon patterns. Western regions (Block 3) transition to arid climates, influenced by its most area in rain shadow effect of the Himalayas. Regression analysis reveals a warming trend in all blocks, reinforcing global climate change impacts. Precipitation trends exhibit nuances, with the East and Centre (Block 2) experiencing increases, while the Block 4 shows a decreasing trend. Extreme precipitation values highlight spatial variability, crucial for climate adaptation strategies. The study extends to extreme wind analysis, showcasing distinct patterns in each block, providing insights into potential risks. Wind direction analysis reveals unique patterns, contributing to a holistic understanding of regional climate dynamics. These findings contribute to the scientific discourse on climate resilience, emphasizing the need for tailored strategies in diverse geographical contexts.

Enhanced Cyclone Intensity Estimation Using Deep Learning Models

Every year in India, tropical cyclones pose one of the greatest threats to life and property. They include a variety of hazards, each of which can have a significant impact on life and property, such as storm surge, flooding, extreme winds, tornadoes, and lighting. These hazards interact with one another, substantially increasing the risk of loss of life and material damage. According to a study on extreme weather events, as many as 117 cyclones hit India in 50 years from 1970 to 2019, claiming over 40,000 lives. Due to advancements in technology, detection of cyclones has become much easier. Cyclones are categorized into various categories based on their intensity which is calculated by various methods and sources. Estimation of the intensity of cyclone helps them to categorize. In this paper we are trying to estimate the intensity of cyclones by using the state- of-the-art deep learning models as they are pre-trained on large data sets. Keywords: Tropical cyclones, Hazards, Intensity estimation, Deep learning models, Detection, Data sets, Cyclone intensity, Weather prediction, Disaster management.

Forest dynamics where typhoon winds blow.

Tropical cyclones (TCs) sporadically cause extensive damage to forests. However, little is known about how TCs affect forest dynamics in mountainous terrain, due to difficulties in modelling wind flows and quantifying structural changes. Typhoon Mangkhut (2018) was the strongest TC to strike Hong Kong in over 40 yr, with gusts > 250 km h-1. Remarkably, the event was captured by a dense anemometer network and repeated LiDAR surveys across natural forests and plantations. We mapped long-term mean and extreme wind speeds using CFD models and analysed corresponding changes in canopy height, which uncovered TC-forest dynamics at unprecedented scales (> 400 000 pixels, 1108 km2). Forest height was more strongly limited by wind exposure than by background topography, a limitation attributable to a dynamic equilibrium between growth and disproportionate TC damage to taller forests. Counterintuitively, wind-sheltered forests also suffered heavy damage. As a result, canopies of wind-sheltered forests were more rugged, which contrasted with flat-topped forests at wind-exposed sites. Plantations were more susceptible to TCs compared to natural rainforests of similar stature (canopy height change -0.86 m vs -0.39 m). Our findings highlight TCs as important, often overlooked factor that fundamentally shapes forest structure and dynamics.

Extreme wind waves on the northeastern Shelf of the Black Sea

The research objective is an investigation of extreme storm waves in the coastal zone from Volna Village (Kerch Strait area) to Adler on the north-eastern Shelf of the Black Sea over a climatic time span. The primary research method is numerical modelling using the MIKE 21 SW spectral wave model. As a result of calculations, hourly fields of spatial distributions of key wave parameters were obtained for the Black Sea area from January 1979 to December 2023, i.e., for the past 45 years. The analysis was conducted considering the separation of the wave field into two components: wind waves and swell. The research revealed that over the last 45 years, the north-eastern coast of the Black Sea experienced the impact of 41 storms with significant wave heights exceeding 5 meters. Two November storms in 2007 and 2023 were truly extreme, with significant sea wave heights reaching 9 meters and power exceeding 500 kW/m. Average heights of storm waves along the coast varied between 4-6 meters, and average power was around 150 kW/m. The highest waves developed in the areas of Anapa, Utrish, and Idokopas. Average values of heights and powers of wind waves showed a general tendency to decrease when moving from the northwest coast to the southeast, while swell, on the contrary, increased. Several relatively homogeneous sections in terms of the development of wind waves and swell were identified in the coastal zone. In the structure of average storm waves between Volna and Anapa, wind waves absolutely dominate. Between Cape Myskhako and Idokopas, the contribution of wind waves to the total wave energy exceeds swell by 2–2.5 times. Between Arkhipo-Osipovka and Tuapse, the energy of wind waves slightly surpasses the energy of swell. Further, south of Tuapse, the contribution of swell becomes predominant. In terms of the ratio of the contribution of wind waves and swell along the coast, two distinct regions stand out: near Anapa, the overall wave climate is determined by wind waves, while in Adler, it is determined by swell.

Primary Cutaneous Anaplastic Large Cell Lymphoma With Rare Extracutaneous Disseminated Disease: A Case Report.

Primary cutaneous anaplastic large cell lymphoma (pcALCL) is a CD30+ lymphoproliferative disorder with generally favorable outcomes and infrequent extracutaneous spread, usually limited to local lymph nodes. However, there may be extensive histologic overlap with more aggressive CD30+ lymphomas, such as large cell transformation of mycosis fungoides or secondary skin involvement by anaplastic lymphoma kinase (ALK)-negative anaplastic large cell lymphoma. Definitive diagnosis relies on clinicopathologic correlation. We report on a 26-year-old woman who presented to our institution with progressive lower extremity wounds for several months, previously treated with antibiotics and vacuum-assisted closure dressings. Consultation with dermatology and 2 separate biopsies eventually led to the diagnosis of pcALCL. Subsequent imaging revealed stage IV disease with innumerable intensely fluorodeoxyglucose (FDG)-avid subcutaneous, intramuscular, and visceral foci, but paucity of lymph node involvement. The patient's condition deteriorated, and she died during her hospitalization. This case reviews the clinicopathologic findings of pcALCL, emphasizes the importance of clinicopathologic correlation in differentiating between CD30+ lymphoproliferative disorders, highlights the extremely rare phenomenon of systemic intramuscular and visceral disseminated disease occurring in pcALCL, and discusses implications for prognosis.

Combining machine learning and computational fluid dynamics for solar panel tilt angle optimization in extreme winds

Although a key driver for green energy development, solar photovoltaic power plants face the major risk of severe wind damages, as there is currently no best practice on how to best stow the panels under strong wind conditions. In this research, an out-of-the-box numerical framework is introduced to inform the discussion around panel design and recommended stow positions, one that leverages the increasing use of solar tracker actuators, that allows panels to set an optimal angle relative to the sun to maximize power output, and incidentally offer a great potential for optimal safeguarding through individual panel piloting. The task of concurrently optimizing multiple panel tilts in a turbulent atmospheric boundary layer wind flow is modeled as a Markov decision process and solved with a single-step deep reinforcement learning algorithm, intended for situations where the optimal policy to be learnt by a neural network does not depend on state. The numerical reward fed to the neural network is computed from high-fidelity numerical simulations combining variational multiscale modeling of the Navier–Stokes equations and anisotropic boundary layer mesh adaptation, to accurately represent critical flow features at affordable computational costs, regardless of the panel tilts chosen by the learning agent. A range of experiments is performed across various learning objectives accounting for different possible causes of breakage (such as tear, vibrations, and fatigue), for which the proposed approach successfully minimizes the aerodynamic efforts on two-dimensional and three-dimensional arrangements of six ground-mounted panels under an incident wind speed of 50 km/h, while outperforming baseline safeguarding practices considered in the literature by several dozen per cent. This gives hope that, by interacting with its computational fluid dynamics environment in a trial-and-error manner, a deep reinforcement learning agent can learn unexpected solutions to this complex decision-making problem and come up with innovative, feasible solutions capable of managing utility-scale solar assets during high-wind events while efficiently complementing engineering intuition and practical experience.

Understanding Decadal-Scale Dynamics in the Bering Sea: Investigating Trends and Variability in Sea Ice, Winds, and Waves

Abstract Despite the societal and economic importance of the Bering Sea, recent rapid changes in surface ocean conditions are not well documented. We address this gap by characterizing the Bering Sea winter ice–wind–wave climate using satellite observations and reanalysis data. Nine of the last 10 years (2014–23) have experienced anomalously low sea ice extent relative to the 1980–2023 mean. Between 2003 and 2023, gale force wind speeds have become more common and satellite altimeter observations show significant wave height (SWH) has increased ≥6 m, and has increased by 1 m between 2003 and 2023, while 20 days yr−1 exhibit SWH > 9 m. Winters between 2018 and 2023 were the six stormiest since 2003, and the sea ice season decreases by 1–4 days yr−1. Should ice loss in the Bering Sea continue to decline, we hypothesize that the frequency of extreme waves over the Bering Sea shelf would increase, severely impacting coastal communities. In September 2022, extratropical cyclone Merbok caused unprecedented SWH (>15 m) over the shelf which were uncharacteristic for the season, and caused widespread coastal inundation and property damage. Such anomalous wave conditions exemplify the emerging threat storms pose in a future where sea ice is absent more frequently in winter, when severe storms are most common. Satellite altimeters have improved the observation of surface ocean conditions in the Bering Sea during the most intense storms, and we demonstrate that a suite of five or more altimeters is required to capture regional events adequately. Significance Statement Although the Bering Sea region has recently endured rapid sea ice loss and the decline of several species important for subsistence and commercial fishing, it has relatively few resources to monitor and mitigate changing climatic conditions. In this article, we show that extreme wind and wave conditions in the Bering Sea have strongly increased in recent years, as sea ice has declined. This points to a need for reliable and continued monitoring, and with only two wave buoys providing year-round observations, an adequate constellation of ocean remote sensing instruments is required to capture extreme ocean–ice–atmosphere events in an isolated and challenging environment.

Characterizing lower tail components of multivariate non-Gaussian wind loads under typhoon climate: using the Huang-Cui triangular pulse function model

Wind loads at the edge and corner areas of the roof surface of low-rise buildings under extreme winds exhibit intensive non-Gaussianity, especially a high degree of negative skewness. The cumulative effect of negative wind loads below a specific threshold u0 in the time domain is the direct cause of structure destruction under extreme winds. Therefore, this study proposes a triangular pulse function model to quantitatively characterize lower tail components of wind loads with negative skewness via equal cumulative integral on time. In this model, the two critical factors are the intensity for the absolute value of the threshold u0 and the minimum continuous duration T0. Additionally, this article investigates the dependence structures of the lower tail components from multivariate wind loads, and their values vary from 0.65 to 0.80 at quantiles (0.25,0.25). In characterizing the peak locations, a position function combining Poisson pulses and periodic pulses is established, and the period T for generating periodic pulses is a constant. Through analysis, the median value of storm lengths varies from 7.0 to 8.5, and 95% are in the interval (5.0, 15.0). Finally, an example is given to illustrate the effectiveness of the continuous integral by the proposed model. For wind loads measured from different locations, the dependence of pulse locations, lower tail storm length, and intensity are investigated comprehensively. In the end, a physical perspective to understand the generation mechanism of lower tail storms is provided via the fractal dimension. The proposed model and analyzed results are of significant engineering guidance value in time-domain numerical simulation of multi-point synchronous non-Gaussian wind loads to improve simulation accuracy.