Severe Weather Research Articles

Drone Detection Performance Evaluation via Real Experiments with Additional Synthetic Darkness

Detecting drones is increasingly challenging, particularly when developing passive and low-cost defense systems capable of countering malicious attacks in environments with high levels of darkness and severe weather conditions. This research addresses the problem of drone detection under varying darkness levels by conducting an extensive study using deep learning models. Specifically, the study evaluates the performance of three advanced models: Yolov8, Vision Transformers (ViT), and Long Short-Term Memory (LSTM) networks. The primary focus is on how these models perform under synthetic darkness conditions, ranging from 20% to 80%, using a composite dataset (CONNECT-M) that simulates nighttime scenarios. The methodology involves applying transfer learning to enhance the base models, creating Yolov8-T, ViT-T, and LSTM-T variants. These models are then tested across multiple datasets with varying darkness levels. The results reveal that all models experience a decline in performance as darkness increases, as measured by Precision-Recall and ROC Curves. However, the transfer learning-enhanced models consistently outperform their original counterparts. Notably, Yolov8-T demonstrates the most robust performance, maintaining higher accuracy across all darkness levels. Despite the general decline in performance with increasing darkness, each model achieves an accuracy above 0.6 for data subjected to 60% or greater darkness. The findings highlight the challenges of drone detection under low-light conditions and emphasize the effectiveness of transfer learning in improving model resilience. The research suggests further exploration into multi-modal systems that combine audio and optical methods to enhance detection capabilities in diverse environmental settings.

Performance Assessment of Basic Oxygen Furnace Slag (BOFS) as an Ice-Melting Abrasive Material

Abrasives play a crucial role in surface blasting, especially in cold climates, where snow and ice significantly challenge transportation infrastructure and road safety. The main purpose of this research is to address the critical need for effective and sustainable winter maintenance techniques. This study examined the possibilities of Basic Oxygen Furnace Slag (BOFS) as a substitute (an abrasive substance) for conventional aggregates in ice-melting applications. Thus, this research assessed the physical properties of BOFS, such as absorption capacity, tested at -5°C, and aggregate angularity test, and designed for evaluation of the surface texture, friction, and percentage of fractured faces in uncompacted voids (SSD%) of the aggregates. Moreover, the potential use of a blend of BOFS with de-icing salts, specifically sodium chloride (NaCl) and calcium chloride (CaCl2), was investigated as an effective ice-melting agent. For this purpose, three tests were carried out: the petri dish test according to SHRP H-205.1, the polishing ice melting test using a modified ASTM C 944 (rotational cutter), and the surface temperature measurement test. By assessing the performance of BOFS, our goal was to justify its efficacy, offering a practical alternative for regions experiencing severe winter conditions. Eventually, the findings from this study assert that BOFS can be used for surface blasting, indicating its potential as a substitute for traditional abrasives.

Supervised Learning-Based Prediction of Lightning Probability in the Warm Season

The accurate prediction of lightning is crucial for forecasters to respond effectively to its related hazards. The rapid development and confined spatial extent of convective storms, in which lightning frequently occurs, pose considerable challenges for accurately predicting their locations using numerical weather prediction (NWP) models. Lightning occurrence is often prognosed using thermodynamic parameters, convective available potential energy (CAPE), the severe weather threat index (SWEAT), the lifted index (LI), etc. A high-resolution NWP model provides a prediction of these thermodynamic parameters at high spatiotemporal resolution with high accuracy for a few hours. However, a complicated algorithm is required to handle all the useful high-resolution variables from the NWP model. The recently emerging machine learning technique can solve this issue by properly handling these “big data” without any model distributional assumption. In this study, we developed a random forest algorithm for nowcasting and very short-range forecasting (useful for ~6 h), named LightningRF. LightningRF was trained by using lightning occurrence as a response variable and characteristic parameters from the NWP as predictors. It was also applied to analysis and forecast fields, showing a high probability of lightning within the observed lightning regions. This highlights the potential of helping forecasters improve their lightning forecasting skills using real-time probabilistic forecasts from a trained model.

Assessment of life cycle environmental impacts of materials, driving pattern, and climatic conditions on battery electric and hydrogen fuel cell vehicles in a cold region

Battery electric vehicles (BEVs) and hydrogen fuel cell vehicles (HFCVs) can play an important role in addressing climate change by diminishing greenhouse gas (GHG) emissions in the worldwide road transportation sector. There is limited research on the implications of the use of lightweight materials, driving pattern, and climatic impact on the life cycle GHG emissions in a cold region. To address this limitation, we developed a framework to assess eighteen BEV and four HFCV scenarios for a cold region that consider aforementioned parameters through a combination of driving patterns (in rural, city, and highway driving) and climatic conditions (i.e., summer, mild winter, and severe winter) for both conventional and carbon fiber-reinforced plastic (CRFP)-based BEVs. A case study was conducted for Canada, considering its cold regions, using available data for HFCVs. We assessed city driving in summer and highway driving in severe winter conditions for conventional and CFRP-based HFCVs. The results show that the lowest GHG emissions are in cities in summer, with life cycle GHG emissions values of 68.7 g CO2 eq/km for CFRP-based BEVs. The highest life cycle GHG emissions are 364.4 g CO2 eq/km with conventional HFCVs on the highway in severe winter conditions' scenario. The operation phase emerges as the primary contributor to life cycle GHG emissions, closely trailed by the production phase. The analysis shows that the most sensitive parameters for CFRP-based BEVs in the city in summer scenario are vehicle lifetime and for conventional HFCVs in the highway in severe winter scenario, fuel cell efficiency. The analysis also shows the range of life cycle GHG emissions for a cold region, with conventional HFCVs on highways in severe winter conditions exhibiting the highest emissions (331.0 g CO2 eq/km) and CFRP-based HFCVs in the city in summer scenario the lowest (51.0 g CO2 eq/km).

Relationship between temperature and acute myocardial infarction: a time series study in Xuzhou, China, from 2018 to 2020

BackgroundIt is widely known that the incidence rate and short-term mortality of acute myocardial infarctions (AMIs) are generally higher during the winter months. The goal of this study was to determine how the temperature of the environment influences fatal acute myocardial infarctions in Xuzhou.MethodsThis observational study used the daily meteorological data and the data on the cause of death from acute myocardial infarction in Xuzhou from January 1, 2018, to December 31, 2020. After controlling meteorological variables and pollutants, the distributed nonlinear lag model (DLNM) was used to estimate the correlation between temperature and lethal AMI.ResultsA total of 27,712 patients with fatal AMI were enrolled. 82.4% were over the age of 65, and 50.9% were men. Relative to the reference temperature (15 ℃), the 30-day cumulative RRs of the extremely cold temperature (− 2 ℃) for the general population, women, and people aged 65 years and above were 4.66 (95% CI: 1.76, 12.30), 15.29 (95% CI: 3.94, 59.36), and 7.13 (95% CI: 2.50, 20.35), respectively. The 30-day cumulative RRs of the cold temperature (2 ℃) for the general population, women, and people aged 65 years and above were 2.55 (1.37, 4.75), 12.78 (2.24, 5.36), and 3.15 (1.61, 6.16), respectively. No statistically significant association was observed between high temperatures and the risk of fatal AMI. The influence of the cold effect (1st and 10th) was at its peak on that day, and the entire cold effect persisted for 30 days. Temperature extremes had an effect on the lag patterns of distinct age and gender stratifications.ConclusionAccording to this study, the risk of fatal AMI increases significantly in cold weather but not in hot weather. Women above the age of 65 are particularly sensitive to severe weather events. The influence of frigid weather on public health should also be considered.

The Climate-Changing Context of Inflation: Fossilflation, Climateflation, and the Environmental Politics of Green Central Banks

Abstract In this forum, I seek to demonstrate how the growing confluence of climate change and inflation offers a fruitful research agenda for environmental politics scholars. It develops two independent, yet interrelated, concepts first proposed by Schnabel: first, fossilflation, the legacy cost of the dependency on fossil energy sources, which has not been reduced forcefully enough over the past decades, and second, climateflation, the growing impact of natural disasters and severe weather events on economic activity and prices. With the subject of inflation often considered to be a contextual or descriptive feature of environmental issues, the economic implications of climate change have come to challenge the conventional understanding of the inflationary phenomenon. This article seeks to foster an interdisciplinary dialogue between scholars from different disciplinary backgrounds on the subject of climate change–induced inflation and the questions it presents for notions of “green” central banking.

MAFNet: Multimodal Asymmetric Fusion Network for Radar Echo Extrapolation

Radar echo extrapolation (REE) is a crucial method for convective nowcasting, and current deep learning (DL)-based methods for REE have shown significant potential in severe weather forecasting tasks. Existing DL-based REE methods use extensive historical radar data to learn the evolution patterns of echoes, they tend to suffer from low accuracy. This is because data of radar modality face difficulty adequately representing the state of weather systems. Inspired by multimodal learning and traditional numerical weather prediction (NWP) methods, we propose a Multimodal Asymmetric Fusion Network (MAFNet) for REE, which uses data from radar modality to model echo evolution, and data from satellite and ground observation modalities to model the background field of weather systems, collectively guiding echo extrapolation. In the MAFNet, we first extract overall convective features through a global shared encoder (GSE), followed by two branches of local modality encoder (LME) and local correlation encoders (LCEs) that extract convective features from radar, satellite, and ground observation modalities. We employ an multimodal asymmetric fusion module (MAFM) to fuse multimodal features at different scales and feature levels, enhancing radar echo extrapolation performance. Additionally, to address the temporal resolution differences in multimodal data, we design a time alignment module based on dynamic time warping (DTW), which aligns multimodal feature sequences temporally. Experimental results demonstrate that compared to state-of-the-art (SOTA) models, the MAFNet achieves average improvements of 1.86% in CSI and 3.18% in HSS on the MeteoNet dataset, and average improvements of 4.84% in CSI and 2.38% in HSS on the RAIN-F dataset.

The Impact of Climate Change on Global Economic Stability: Developing Economic Policies to Manage Climate Risks

The paper explores the impact of climate change on global economic stability while also placing consideration on the role of economic policy towards the management of climate change risk. Climate change represents one of the pressing environmental challenges of the 21st century with profound repercussion on the economic stability while also affecting several sectors such as agriculture, energy, tourism and health. The increased rate of change in the climatic condition has led to a wide spread of severe weather conditions such as hurricanes, flooding, drought, tsunamis, wild fire etc. This has necessitated the need for a robust economic policy that would aid in the mitigation effort against the release of greenhouse gases into the atmosphere, as well as providing adaptation against the harmful impact of climate change. The role of the agreement of 2015 Paris frame work was examined as it provides an important foundation for global cooperation in the fight against climate change. It is expected that proper integration of financial institution and the private sector would provide the necessary funding and technical support that would aid in the implementation of green technology as well as promote sustainable development.

Climate Profile Over Jeldu Woreda of West Shewa Zone, Oromia, Ethiopia

Identifying the climate profile of an area is essential for effective water resource management, agricultural planning and disaster preparedness. This study focuses on characterizing rainfall patterns and identifying the variability of Jeldu woreda in Oromia, Ethiopia. Previously and recently, little study tried to show the woreda profile, which did not bases on station data properly. Forty years (1981-2020) of stations’ rainfall and temperature data were utilized. The study area had natural forest, highland and mid land with varying topography. Based on the annual rainfall cycle, the woreda had bi-modal rainfall types, with the peak of the year being July. Statistical methods and python script were applied to identify the climate profile. The results show that main rainy seasonal rainfall starts on average from 12-14 June and ceases averagely around 10-15 October. During second rainy period, the rain starts from 25 February to 10 March. The mean annual rainfall of woreda is 1395.1 mm, and the average maximum temperature is 19.5°. Kiremt contributes the highest percent, which is more than 64 % of the annual total rainfall. The 1987 and 2015 were the driest years, and 1996 and 2010 is the wettest during kiremt, 1987 and 2010 the wettest, and 1999 and 2015 the driest during Belg. Seasonal rainfall had a regular to moderate precipitation concentration over woreda. Identifying the causes of unseasonal rainfall that happened during dry season over the study area can help to decrease weather hazard during the season.

The Effect of Time Display Format on Cognitive Performance of Integrated Meteorological Radar Information.

A proper time display format is essential for pilots to understand integrated meteorological radar information, thereby making informed flying decisions and steering clear of hazardous weather. Previous studies on time display format supported the advantages of digital format, while some studies found that analog clock format is superior to digital format. This study explored the effect of time display format on the cognitive performance of integrated meteorological radar information through two experiments. Experiment 1 first examined the effects of digital and analog clock displays on the timing of individual processing advance or delay changes in a general scenario. Then, Experiment 2 was conducted in a simulated flight scenario to investigate the advantages and disadvantages of digital and analog clock display in delay time processing with and without time pressure. The results showed the following: (1) Analog clock has more advantages than digital display format in processing the varying time difference. (2) Whether with or without time pressure, analog clock is more conducive to individual cognition of integrated meteorological radar information than digital time display. (3) The length of delay time is an important factor affecting individual time cognition, and it can also affect the cognition of radar information. The longer the delay time, the more difficult it is to identify the time and understand the information. These findings provide a certain reference for the design of the integrated meteorological radar information display interface.

Global and local dynamics of X-flare-producing active regions during solar cycle 25 peak phase

The configuration of the longitudinally elongated region that active regions (ARs) cluster around, known as a toroid belt, has been shown to be an indicator of intense activity. In particular, complex ARs at locations in the north and/or south toroids tend to appear "tipped-away" with respect to each other. On the other hand, magnetic helicity has been used as an indicator of flare activity in ARs. As solar cycle (SC) 25 approaches its peak, a number of significant (X-class) flares have been produced. Here, we investigate the circumstances surrounding two of the most flare-prolific ARs of solar cycle 25, namely, ARs 13590 and 13514. Two aspects of the evolution of these ARs are investigated in this work: the global-scale magnetic toroid configuration and small-scale magnetic field morphology and topology -- before, during, and after the onset of major flares. We studied the global morphology of the solar magnetic fields near intense flares in terms of the spatial distribution of ARs on magnetic fields synoptic maps. On AR scales, we analyzed the magnetic helicity accumulation, as well as its current-carrying and potential components. Our results are consistent with major flare-prolific ARs from solar cycles 23 and 24. In particular, we observe a consistent dominance of current-carrying magnetic helicity at the time of major flares. The evolution of global magnetic toroids, indicating the occurrence of flare-prolific ARs in the tipped-away portion of the toroid, together with the local dynamics of complex ARs, could offer a few weeks of lead time to prepare for upcoming space weather hazards.

Grid Search Optimized Machine Learning based Modeling of CO2 Emissions Prediction from Cars for Sustainable Environment

Carbon emissions have increased dramatically because of industrialization, trapping heat in the atmosphere and hastening climate change. This is a serious threat to the wealth, security, and well-being of the world. The effects are extensive, ranging from severe weather, disease outbreaks, and economic disruption to food insecurity and water scarcity. The World Health Organization (WHO) has determined that climate change poses the greatest threat to public health in the twenty-first century. Thus, precise CO2 emissions have emerged as a crucial concern in recent times. Several studies have tried to forecast the amount CO2 from industry and power plant using statistical analysis. Efficiency, robustness and diverse application was the limitation of the study. In this study, we have proposed an AI based model that is able to predict the amounts of CO2 emissions from cars. We applied a grid search-optimized machine learning approach using the publicly available Canadian dataset. Incorporation of different statistical analyses and preprocessing techniques such as duplicate data management, outlier rejection, scaling contributed to enhance the quality of the dataset. Later, grid search techniques were applied to tune the KNN, RF, and SVR models. The approach has enhanced the performance of CO2 emissions prediction. In the study, we further used the explainability of the random forest model to check the bias and fairness of predictability. MSE, RMSE, and R-squared metrics of the proposed approach were the highest as the state of the art.

Optimizing Numerical Weather Prediction Utility of the Maryland Mesonet with Observing System Simulation Experiments

Abstract The Maryland Mesonet Project will construct a network of 75 surface observing stations with aims that include mitigating the statewide impact of severe convective storms and improving analyses of record. The spatial configuration of mesonet stations is expected to affect the utility newly provided observations will have via data assimilation, making it desirable to study the effects of mesonet configuration. Furthermore, the impact associated with any observing system configuration is constrained by errors inherent to the prediction systems used to generate forecasts, which may change with future advances in data assimilation methodology, physical parameterization schemes, and resource availability. To address such possibilities, we perform sets of observing system simulation experiments using a high-resolution regional modeling system to assess the expected impact of four candidate mesonet configurations. Experiments cover seven 18-hour case-study events featuring moist convective regimes associated with severe weather over the state of Maryland and are performed using two versions of our experimental modeling system: a ’standard-uncertainty’ configuration tuned to be representative of existing convective-allowing prediction systems, and a ’constrained-uncertainty’ configuration with reduced boundary condition and model error that reflects a possible trajectory for future prediction systems. We find that the assimilation of mesonet data produces definitive improvements to analysis fields below 1000 m that are mediated by modeling system uncertainty. Conversely, mesonet impact on forecast verification is inconclusive and strongly variable across verification metrics. The impact of mesonet configuration appears limited by a saturation effect that caps local analysis improvements past a minimal density of observing stations.

Fat storage and drought tolerance in a seasonally‐adapted primate: Implications for modeling the effects of animal responses to global climate change

AbstractGlobal warming is changing habitats and affecting biodiversity, and is expected to exacerbate aridification in many regions. Animals and plants in seasonally dry tropical forests often exhibit adaptations to cope with seasonal resource limitation. However, whether these adaptations will facilitate drought tolerance or increase drought vulnerability is unclear. Here, we combine long‐term individual‐based data on phenology, morphometrics, and demographics to investigate how drought impacts the food resources, health, reproduction, and behavior of a population of Verreaux's sifaka (Propithecus verreauxi), a critically endangered lemur inhabiting dry deciduous forests in Madagascar. Between December 2010 and May 2023, the population experienced 3 years of severe drought (2016, 2017, 2022). During green periods in severe drought, the availability of high‐quality sifaka foods (young leaves, fruit) was significantly reduced and fruit tree mortality increased. This reduced availability of fruit persisted in the year after a drought, despite typical rainfall. Yet surprisingly, we found no negative effects on body condition or commonly‐used metrics for reproductive success during drought years or years following a drought. Instead, sifaka exhibited significantly higher levels of subcutaneous body fat during severe droughts. We observed little change in sifaka behavior between drought and non‐drought periods. However, they were more likely to lick dew during severe drought, and spent significantly less time feeding on young and mature leaves. They also significantly increased their time feeding on flowers and fruits, despite the reduced abundance of fruit in the habitat. Together, our results suggest that increased consumption of water‐rich fruit and flowers during severe droughts could facilitate physiological mechanisms that help sifaka cope with water scarcity, including fructose‐mediated fat storage, metabolic water production, and water conservation. These results provide new insights into how critically endangered animals may respond to climate change, suggesting that behavioral and physiological adaptations to seasonal resource limitation may buffer some mammals from the effects of severe drought or other extreme weather events.

The Effect of Weathering Conditions in Combination with Natural Phenomena/Disasters on Microplastics’ Transport from Aquatic Environments to Agricultural Soils

Concern over microplastics (MPs) in the environment is rising. Microplastics are generally known to exist in aquatic settings, but less is known about their occurrence in soil ecosystems. When plastic waste builds up in agricultural areas, it can have a negative impact on the environment and food sources, as well as have an indirect effect on all trophic levels of the food chain. This paper addresses the relationship between microplastics and the management of plastic waste, which contributes to their accumulation, and it describes the sources and the movement processes of microplastics in agricultural soils as a result of natural events and disasters. Evaluating the impact of weather on coastal microplastic contamination is critical, as extreme weather events have become more frequent in recent years. This study sheds light on how weather patterns affect the dispersion of plastic waste in terrestrial habitats, including the impacts of seasonality and extreme weather. According to the results of this review, typhoons, monsoons, rainfall, and floods contribute significantly more microplastics to the surface sediment through surface runoff and wind transport, particle redistribution caused by agitated waves, and fragmentation under intense abrasion forces. Severe weather conditions have the potential to disperse larger and more varied kinds of microplastics.

Daily Lifestyle Managed by Natural Disasters in Anatolia

The heart of Anatolia was devastated by two major droughts between 1800 and 1880. The severe winter conditions during this period, followed by subsequent floods, once again destroyed farmers' crops. The great earthquake in Bursa further burdened the already exhausted society with pain and sorrow. In this study, data will be evaluated to understand how people in Anatolia, exposed to successive disasters, had to adapt their livelihoods. The focus will be on how workshop-style collective production, which had begun to emerge in Anatolia on the brink of the Industrial Revolution, was disrupted by natural disasters, and why the agriculture-based economy remained predominant. The study includes observations on the changes in Bursa and Adana, where disaster-affected populations clustered after being displaced. The contribution of cheap labor, provided by victims of natural disasters, to the manufacturing sector in Bursa, Adana, Aydın, and Izmir is analyzed in relation to the small-scale industrial transformations in these cities. A comparison is made between the livelihoods of the local population before the disasters and their income sources afterward. Based on this comparison, the impact of disasters on livelihoods is discussed. Additionally, the types of disasters and their varying degrees of impact in Ankara and Konya are examined.

Enhancing Bright Band Prediction Through RadioSonde RadioWind for International Telecommunication Union- Radio Communications (ITU-R)

As a vital source of information on atmospheric conditions at different altitudes, upper air observations are critical for precise weather forecasting, climate monitoring, and atmospheric research. In coastal areas like Vishakhapatnam that are subject to tropical cyclones and monsoonal fluctuations, radiosondes provide precise vertical profiles that are essential for weather forecasting. They also measure temperature, humidity, and pressure. The addition of wind speed and direction readings via radiowind devices, often known as "rawinsondes," improves severe weather forecasts and our understanding of atmospheric dynamics. Forecasting floods, maintaining aviation safety, and managing water resources are all aided by the ability to predict the bright band, a radar indication of the precipitation layer melting. Weather models and readiness for disasters are much improved in Vishakhapatnam when radiosondes, rawinsondes, and bright band predictions are used in tandem.

AdaptoMixNet: detection of foreign objects on power transmission lines under severe weather conditions

AdaptoMixNet: detection of foreign objects on power transmission lines under severe weather conditions

Morphological Features of Severe Ionospheric Weather Associated with Typhoon Doksuri in 2023

Morphological Features of Severe Ionospheric Weather Associated with Typhoon Doksuri in 2023

Study concerning the design and functionality of individual emergency shelters

The divide between the rich and poor in the European housing market is fast rising. Latest research indicates that Europe is dealing with an increasing number of homeless people. Every city in Europe has them—homeless people compelled to live on street corners, frequently hiding themselves with cardboard. Rain, snow, and temperatures below zero pose a threat to their lives on a daily basis. There are many varied kinds of services that have been discovered, but it is difficult to keep track of everyone and guarantee that they have a warm night's sleep in the winter. The current article suggests accommodation as a workaround until they can receive high-intensity support, a way to keep a single person warm and safe during the winter. The focus is on devising a strategy that not only ensures the warmth and safety of individuals during the harsh winter months but also seeks to industrialize the construction of shelters, ensuring affordability below the cost of winter hospitalization for a homeless person. Crucially, the article introduces an additional layer to this initiative by highlighting the dual purpose of these individual shelters. Beyond being a means to provide respite for the homeless during severe weather, these shelters are envisioned as immediate response units in the event of emergencies such as earthquakes in urban areas. The article explores the potential impact of this multi-layered approach on transforming urban landscapes and fostering resilient communities.