Weather Events Research Articles

Impact of physical climate risks on agricultural firms’ bankruptcy: evidence from France, Italy, Portugal and Spain from 2016 to 2019

Abstract Frequent and long-lasting extreme climate events can impact business-related natural capital, severely affecting production and resulting in losses for agricultural firms. Physical climate effects are material dependencies for agricultural businesses and may severely affect their performance and compromise their survival. This study’s aim is to analyze the comprehensive effect of climate change factors on agricultural firm bankruptcies in a European geographical area that is especially susceptible to climate change. Employing a panel of 15 036 agricultural firms, we analyze the effects of adverse climatic conditions in the firm’s headquarters area using logit regressions, an instrumental variable ordinary least squares model, and the gradient-boosting ensemble method. We find that bankruptcy is conditioned upon extreme weather events, indicating that climate change’s physical impacts on firm resources already materially affect the agricultural sector’s resilience and survival. Specifically, abnormally high temperatures, precipitation, and the incidence of fires are significant factors that contribute to bankruptcy or insolvency. Furthermore, a “fire weather” index comprising high temperatures, drought, and wind, combined with fire occurrence, intensifies the bankruptcy risk of agricultural businesses.

Data Augmentation Strategies for Improved PM2.5 Forecasting Using Transformer Architectures

Breathing in fine particulate matter of diameter less than 2.5 µm (PM2.5) greatly increases an individual’s risk of cardiovascular and respiratory diseases. As climate change progresses, extreme weather events, including wildfires, are expected to increase, exacerbating air pollution. However, models often struggle to capture extreme pollution events due to the rarity of high PM2.5 levels in training datasets. To address this, we implemented cluster-based undersampling and trained Transformer models to improve extreme event prediction using various cutoff thresholds (12.1 µg/m3 and 35.5 µg/m3) and partial sampling ratios (10/90, 20/80, 30/70, 40/60, 50/50). Our results demonstrate that the 35.5 µg/m3 threshold, paired with a 20/80 partial sampling ratio, achieved the best performance, with an RMSE of 2.080, MAE of 1.386, and R2 of 0.914, particularly excelling in forecasting high PM2.5 events. Overall, models trained on augmented data significantly outperformed those trained on original data, highlighting the importance of resampling techniques in improving air quality forecasting accuracy, especially for high-pollution scenarios. These findings provide critical insights into optimizing air quality forecasting models, enabling more reliable predictions of extreme pollution events. By advancing the ability to forecast high PM2.5 levels, this study contributes to the development of more informed public health and environmental policies to mitigate the impacts of air pollution, and advanced the technology for building better air quality digital twins.

Spatiotemporal patterns of individual and multiple simultaneous severe weather events co-occurring with power outages in the United States, 2018–2020

In the United States, severe weather-driven power outages are increasing in frequency and duration, likely with health consequences. Previous studies examined individual severe weather events (e.g., heatwaves) and focused on large outages in metropolitan areas. Here, we described nationwide spatiotemporal patterns of individual (e.g., tropical cyclone alone) and simultaneous (e.g., tropical cyclone + anomalous heat) severe weather events co-occurring with 8+ hour outages. We used hourly county-level PowerOutage.us data from 2018–2020 to define 8+ hour outages as whenever the daily proportion of customers without power was ≥0.1% for ≥8 continuous hours. We conducted analyses at the daily and county (county-day) level and identified county-days with severe weather events, including anomalous cold, anomalous heat, anomalous precipitation, snowfall, tropical cyclones, and wildfire. Of 1,657 counties with reliable outage data, 1,205 (72.7%) experienced an 8+ hour outage co-occurring with an individual severe weather event, and 904 (54.6%) with multiple simultaneous severe weather events. Anomalous precipitation events co-occurring with outages were the most common, affecting 1,170 (70.6%) counties. These outages concentrated along the Gulf Coast, the Northeast, Michigan, and Southern California. Co-occurrence with anomalous heat happened the second most frequently, affecting 839 (50.6%) counties, mostly in Southeastern states. Among all county-days with a severe weather event, tropical cyclones–though rarer and primarily affecting the Eastern Seaboard–co-occurred with an 8+ hour outage 24% of the time. On the West Coast, wildfires were increasingly likely to co-occur with weather-related outages from 2018–2020. Among multiple simultaneous weather events, 8+ hour outages co-occurred with anomalous precipitation-anomalous heat on 1,155 county-days in 40 states, anomalous precipitation-tropical cyclone on 705 county-days in 24 states, and anomalous cold-snowfall on 259 county-days in 27 states. Our results can help guide efforts to strengthen the electricity grid, prepare communities for multi-hazard events, and allocate resources for adaptation and recovery.

Climate change risk trap: Low-carbon spatial restructuring and disaster risk in petroleum-based economies

Abstract The economic risks from climate change can be separated into two categories: physical risks from increased frequency and magnitude of extreme weather and climate events, and transition risks due to rapid decarbonisation leading to stranded assets and shifts in production and the labour market. On the global level, the two risks are widely recognized as trade-offs by the academic and policy literature: rapid decarbonisation increases transition risks while reducing physical risks, and vice versa for a delayed reduction of greenhouse gas emissions. In contrast, little is known about the interaction between the two risks on smaller geographical scales. This paper investigates the interactions between physical and transition risks on the sub-national level for petroleum-based economies. Their strong dependency on the export of fossil fuels requires a more far-reaching economic restructuring to meet both local and global emission targets than most other economies, exposing them to high transition risks. At the same time, the profound economic changes resulting from a low-carbon transition lead to a spatial redistribution of assets and labour, when brown assets and jobs get stranded and new green assets and jobs are created elsewhere. Building on the literature on spatial economic restructuring and climate disaster risk, we show that, unlike the common conceptualisation as a trade-off, physical and transition risk can increase simultaneously on the sub-national level. We call this dynamic the climate change risk trap. The paper provides an empirical illustration of this trap using the example of flash flood risk in Kuwait, a wealthy petroleum-based economy in the Gulf region. It shows how decisions on urban planning and low-carbon economic restructuring have increased flash flood risk. The analysis highlights the importance of considering climate disaster risk and environmental impact assessments in low-carbon transition planning to avoid falling into the climate change risk trap.

Investigation of a transformer-based hybrid artificial neural networks for climate data prediction and analysis

IntroductionClimate change isone of the major challenges facing the world today, causing frequent extreme weather events that significantly impact human production, life, and the ecological environment. Traditional climate prediction models largely rely on the simulation of physical processes. While they have achieved some success, these models still face issues such as complexity, high computational cost, and insufficient handling of multivariable nonlinear relationships.MethodsIn light of this, this paper proposes a hybrid deep learning model based on Transformer-Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) to improve the accuracy of climate predictions. Firstly, the Transformer model is introduced to capture the complex patterns in cimate data time series through its powerful sequence modeling capabilities. Secondly, CNN is utilized to extract local features and capture short-term changes. Lastly, LSTM is adept at handling long-term dependencies, ensuring the model can remember and utilize information over extended time spans.Results and DiscussionExperiments conducted on temperature data from Guangdong Province in China validate the performance of the proposed model. Compared to four different climate prediction decomposition methods, the proposed hybrid model with the Transformer method performs the best. The resuts also show that the Transformer-CNN-LSTM hybrid model outperforms other hybrid models on five evaluation metrics, indicating that the proposed model provides more accurate predictions and more stable fitting results.

Digital Twin-Based Management of Sewer Systems: Research Strategy for KaSyTwin Project

Sewer infrastructure is vital for flood prevention, environmental protection, and public health. As part of sewer infrastructure, sewer systems are prone to degradation. Traditional maintenance methods for sewer systems are largely manual and reactive and rely on inconsistent data, leading to inefficient maintenance. The KaSyTwin research project addresses the urgent need for efficient and resilient sewer system management methods in Germany, aiming to develop a methodology for the semi-automated development and utilization of digital twins of sewer systems to enhance data availability and operational resilience. Using advanced multi-sensor robotic platforms equipped with scanning and imaging systems, i.e., laser scanners and cameras, as well as artificial intelligence (AI), the KaSyTwin research project focuses on generating digital twin-enabled representations of sewer systems in real time. As a project report, this work outlines the research framework and proposed methodologies in the KaSyTwin research project. Digital twins of sewer systems integrated with AI technologies are expected to facilitate proactive maintenance, resilience forecasting against extreme weather events, and real-time damage detection. Furthermore, the KaSyTwin research project aspires to advance the digital management of sewer systems, ensuring long-term functionality and public welfare via on-demand structural health monitoring and non-destructive testing.

CPS-RAUnet++: A Jet Axis Detection Method Based on Cross-Pseudo Supervision and Extended Unet++ Model

Atmospheric jets are pivotal components of atmospheric circulation, profoundly influencing surface weather patterns and the development of extreme weather events such as storms and cold waves. Accurate detection of the jet stream axis is indispensable for enhancing weather forecasting, monitoring climate change, and mitigating disasters. However, traditional methods for delineating atmospheric jets are plagued by inefficiency, substantial errors, and pronounced subjectivity, limiting their applicability in complex atmospheric scenarios. Current research on semi-supervised methods for extracting atmospheric jets remains scarce, with most approaches dependent on traditional techniques that struggle with stability and generalization. To address these limitations, this study proposes a semi-supervised jet stream axis extraction method leveraging an enhanced U-Net++ model. The approach incorporates improved residual blocks and enhanced attention gate mechanisms, seamlessly integrating these enhanced attention gates into the dense skip connections of U-Net++. Furthermore, it optimizes the consistency learning phase within semi-supervised frameworks, effectively addressing data scarcity challenges while significantly enhancing the precision of jet stream axis detection. Experimental results reveal the following: (1) With only 30% of labeled data, the proposed method achieves a precision exceeding 80% on the test set, surpassing state-of-the-art (SOTA) baselines. Compared to fully supervised U-Net and U-Net++ methods, the precision improves by 17.02% and 9.91%. (2) With labeled data proportions of 10%, 20%, and 30%, the proposed method outperforms the MT semi-supervised method, achieving precision gains of 9.44%, 15.58%, and 19.50%, while surpassing the DCT semi-supervised method with improvements of 10.24%, 16.64%, and 14.15%, respectively. Ablation studies further validate the effectiveness of the proposed method in accurately identifying the jet stream axis. The proposed method exhibits remarkable consistency, stability, and generalization capabilities, producing jet stream axis extractions closely aligned with wind field data.

Criticality disaster risk assessment for urban rail transport in Indonesia

The increasing frequency of extreme weather events due to climate change poses significant risks to urban rail systems, especially in disaster-prone regions like Indonesia. Urban railways, with limited rerouting options, are highly vulnerable to natural disasters such as floods, landslides, and earthquakes. These disruptions can cause cascading economic impacts, including infrastructure damage, productivity loss, and prolonged travel times. This study develops a methodological framework to assess disaster risk and climate vulnerability in urban rail systems, with Bandung, Indonesia, as the case study. The framework integrates Climate Vulnerability and Risk Assessment (CVRA) with the Movement and Place (M&P) framework, leveraging Geographic Information System (GIS) technology to identify and prioritize high-risk areas. CVRA evaluates hazards, exposure, and vulnerabilities, while M&P assesses the functional significance of transport nodes based on movement patterns and land use density. The combined analysis produces a risk matrix, enabling targeted mitigation strategies that integrate engineering solutions, urban planning, and policy interventions. Findings highlight critical vulnerabilities in Bandung’s proposed LRT system, particularly at stations susceptible to earthquakes and landslides. Recommended mitigation measures include resilient infrastructure designs, strategic planning for high-risk zones, and stakeholder engagement for prioritization. This framework offers practical guidance for policymakers to enhance urban rail resilience, reduce climate-related risks, and ensure sustainable urban mobility. It serves as a scalable model for other cities in Indonesia and globally, supporting adaptive and sustainable transport systems.

Arctic sea ice melting has produced distinct sea ice-atmosphere coupled patterns

Abstract The possible influence of Arctic sea ice on weather events and climate variations has received considerable attention, while the evolution of its relationship with interannual variations of winter temperature across much of Eurasia remains poorly understood. This study quantitatively describes two distinct coupled patterns between autumn Arctic sea ice concentration (SIC) in the Barents-Kara Seas and the subsequent winter regional temperature over Eurasia. The leading coupled pattern dominantly depicts interannual variations of the cold Arctic – warm Eurasia or warm Arctic – cold Eurasia configuration, differing from the background warming pattern. While the second coupled pattern describes in-phase interannual variations of winter temperature over the Arctic and across much of Eurasia. This second pattern displayed an interdecadal transition in the early 2010s, from frequent occurrences of the cold Arctic – cold Eurasia pattern to the dominant warm Arctic – warm Eurasia configuration. Through this interdecadal transition, the warm Arctic – warm Eurasia configuration replaced the leading coupled pattern. The previously proposed the tropospheric and stratospheric processes cannot fully explain this Arctic-Eurasia temperature configuration. These findings underscore the pivotal role of Arctic sea ice melting in influencing the interannual and interdecadal variations of winter atmospheric circulation patterns and have significant implications for predicting short-term winter temperature trends over Eurasia.

Extreme weather events and economic growth: empirical evidence from Africa

Extreme weather events and economic growth: empirical evidence from Africa

Remote Sensing Technology for Observing Tree Mortality and Its Influences on Carbon–Water Dynamics

Trees are indispensable to ecosystems, yet mortality rates have been increasing due to the abnormal changes in forest growth environments caused by frequent extreme weather events associated with global climate warming. Consequently, the need to monitor, assess, and predict tree mortality has become increasingly urgent to better address climate change and protect forest ecosystems. Over the past few decades, remote sensing has been widely applied to vegetation mortality observation due to its significant advantages. Here, we reviewed and analyzed the major research advancements in the application of remote sensing for tree mortality monitoring, using the Web of Science Core Collection database, covering the period from 1998 to the first half of 2024. We comprehensively summarized the use of different platforms (satellite and UAV) for data acquisition, the application of various sensors (multispectral, hyperspectral, and radar) as image data sources, the primary indicators, the classification models used in monitoring tree mortality, and the influence of tree mortality. Our findings indicated that satellite-based optical remote sensing data were the primary data source for tree mortality monitoring, accounting for 80% of existing studies. Time-series optical remote sensing data have emerged as a crucial direction for enhancing the accuracy of vegetation mortality monitoring. In recent years, studies utilizing airborne LiDAR have shown an increasing trend, accounting for 48% of UAV-based research. NDVI was the most commonly used remote sensing indicator, and most studies incorporated meteorological and climatic factors as environmental variables. Machine learning was increasingly favored for remote sensing data analysis, with Random Forest being the most widely used classification model. People are more focused on the impacts of tree mortality on water and carbon. Finally, we discussed the challenges in monitoring and evaluating tree mortality through remote sensing and offered perspectives for future developments.

Advancing forecasting capabilities: A contrastive learning model for forecasting tropical cyclone rapid intensification

Tropical cyclones (TCs), particularly those that rapidly intensify (RI), pose a significant threat due to the uncertainty in forecasting them. RI TC periods, which intensify by at least 13 m/s within 24 h, remain challenging to forecast accurately. Existing models achieve a probability of detection (POD) of 82.6% and a false alarm rate (FARate) of 27.2%. To address this, we developed a contrastive-based RI TC forecasting (RITCF-contrastive) model, utilizing satellite infrared imagery alongside atmospheric and oceanic data. The RITCF-contrastive model was tested on 1,149 TC periods in the Northwest Pacific from 2020 to 2021, achieving a POD of 92.3% and a FARate of 8.9%. RITCF-contrastive improves on previous models by addressing sample imbalance and incorporating TC structural features, leading to a 11.7% improvement in POD and a 3 times reduction in FARate compared to existing deep learning methods. The RITCF-contrastive model not only enhances RI TC forecasting but also offers a unique approach to forecasting these dangerous weather events.

The role of intersectionality in shaping adaptive capacity of smallholder farmers in the Talensi district of Ghana

ABSTRACT In the Talensi district of Ghana, smallholder farmers adapt to climate change—including variability, extreme weather events, and long-term temperature shifts—in varied and unequal ways. This study delves into the intersectional nature of this inequality. Specifically, it explores how the socio-demographics of these smallholder farmers intersect with their livelihood capitals, shaping their capacity for climate adaptation. Drawing from a systematic random sample of 130 smallholder farmers and six focus group discussions and employing descriptive and inferential statistics and narrative analysis, the research unpacks how livelihood capital indicators intersect with these farmers’ socio-demographics, either supporting or constraining their climate adaptation efforts. The findings underscore how socio-demographic identities and livelihood resources result in unequal adaptive capacities and opportunities within heterogeneous communities. This research enriches the intersectionality literature, advocating for an intersectional approach to vulnerability studies. Such an approach can yield a deeper insight into vulnerability, allowing researchers to identify and understand vulnerable groups more precisely. An intersectionality approach is essential to avoid overshadowing complex local realities, which could inadvertently neglect the needs of many individuals and exacerbate existing vulnerabilities.

Dramatic effect of extreme rainfall event and storm on microbial community dynamics in a subtropical coastal region.

Extreme weather events, such as heavy rainfall and typhoons, are becoming more frequent due to climate change and can significantly impact coastal microbial communities. This study examines the short-term alterations in microbial food webs-viruses, bacteria, picophytoplankton, nanoflagellates, ciliates, and diatom-following Typhoon Krathon in Taiwan's coastal waters in October 2024. Daily in situ sampling revealed a significant post-typhoon increased in viral, nanoflagellate, and Synechococcus spp. abundance. Furthermore, viral, Synechococcus spp., and nanoflagellate abundance increased by approximately 4.2 to 12.8-fold, 33.3 to 160 fold, and 0.5 to 9.4 fold in response to these weather events, compared to periods pre-typhoon, respectively. Modified dilution experiments showed that, before the typhoon, nanoflagellate grazing was the major cause of bacterial mortality, while viral lysis was the main cause of Synechococcus spp. mortality. Post-typhoon, there was a notable shift, with nanoflagellate grazing increasing mortality rates of bacteria and Synechococcus spp., suggesting that grazing became the dominant top-down control mechanism after the disturbance. Our findings suggest that extreme weather events shift microbial mortality dynamics, increasing the role of grazing in controlling microbial populations, with potential implications for carbon cycling in coastal ecosystems.

Public Support for Flood Risk Management: Insights from an Italian Alpine Survey Using Systems Thinking

This study presents the results of a survey on flood risk awareness conducted in the Italian Alps, examining the impacts of a major weather event on public perception and trust. It develops a systems-thinking framework to analyse dynamic feedback loops influencing flood risk management support over time. The survey data collection overlapped with a severe storm event in Central Europe, the storm “Adrian” (also known as “Vaia”). This provided a unique pre- and post-event perspective. Results highlight the critical role of individual knowledge, trust in authorities, and social group dynamics in shaping risk perception processes. The study shows how major weather events can change perceptions, sense of safety, and institutional trust within local communities, and more interestingly, these changes can vary spatially. The findings are summarised using a systems-thinking framework, which helps to identify possible feedback loops between flood risk management interventions and long-term public support. The study emphasizes the importance of forward-looking, systems-thinking approaches in the design, monitoring, and evaluation of flood risk management plans. These approaches allow one to account for often-overlooked dynamics, such as spatially varying feedback loops and counter-intuitive effects, ultimately improving the long-term effectiveness of flood risk management.

Frontline Clinic Administrator Perspectives on Extreme Weather Events, Clinic Operations, and Climate Resilience.

Climate-sensitive extreme weather events are increasingly impacting frontline clinic operations. We conducted a national, cross-sectional survey of 284 self-identified administrators and other staff at frontline clinics determining their attitudes toward climate change and the impacts, resilience, and preparedness of clinics for extreme weather events. Most respondents (80.2%) reported that humans are causing climate change, and nearly half (45.9%) reported concerns about extreme weather impacting their job, with barriers to preparedness including financing and knowledge and resources. Over a third (41.8%) of respondents reported taking steps to protect their clinic infrastructure from extreme weather, including by addressing power and improving ventilation.

Impact of Extreme Weather Events on the Surface Energy Balance of the Low-Elevation Svalbard Glacier Aldegondabreen

Impact of Extreme Weather Events on the Surface Energy Balance of the Low-Elevation Svalbard Glacier Aldegondabreen

Long-Term Statistical Analysis of Severe Weather and Climate Events in Greece

Long-Term Statistical Analysis of Severe Weather and Climate Events in Greece

Multiscale wildfire and smoke detection in complex drone forest environments based on YOLOv8

Global climate change has triggered frequent extreme weather events, leading to a significant increase in the frequency and intensity of forest fires. Traditional fire monitoring methods such as manual inspections, sensor technologies, and remote sensing satellites have limitations. With the advancement of drone technology and deep learning, using drones combined with artificial intelligence for fire monitoring has become mainstream. This paper proposes an improved YOLOv8-based model that incorporates local convolution instead of full convolution in the C2F module and integrates the EMA module to enhance the feature channel interaction modeling capability and contextual information utilization, thereby reducing model complexity and increasing efficiency. Additionally, in order to address the risk of false positives and missed detections caused by vegetation, terrain, and lighting changes in forests, we have introduced the AgentAttention module in the Backbone. This module combines Softmax and linear attention to optimize feature extraction, improving the model’s accuracy and robustness. Furthermore, in order to tackle the challenges of detecting flames and smoke at different scales and angles, we have designed the BiFormer module, which adaptively fuses global and local features, significantly enhancing the model’s multi-scale and multi-angle detection capability. Experimental results show that the improved model achieves Precision and Recall of 93.57% and 88.51%, respectively, representing improvements of 5.05% and 2.72% over the original model. It also optimizes FPS, GFLOPs, and Params by 14.3%, 25%, and 19.7%, respectively. This research has significant application prospects in forest fire early warning, emergency response, and loss reduction, while also providing strong technical support for forest resource protection and public safety.

A Framework for Resilient Community Microgrids: Review of Operational Strategies and Performance Metrics

The growing frequency of extreme weather events and grid disruptions highlights the urgent need for resilient energy systems. Decentralized and autonomous, community microgrids offer reliable and adaptable solutions. However, existing research often isolates components or control methods, lacking a comprehensive synthesis of their interdependencies and resilience strategies. To address this gap, this study conducts a comprehensive scoping review to synthesize the current state of knowledge on community microgrids, focusing on their types, components, operational strategies, control methods, and performance indicators. The research identifies key microgrid subtypes, such as islanded, hybrid, multi-energy, and autonomous systems, and evaluates the role of critical components like energy storage systems and distributed energy resources in enhancing resilience. It also highlights performance indicators, including reliability, stability, and flexibility, that serve as benchmarks for resilience. A novel framework is proposed, integrating microgrid design and operational aspects into a cohesive model for resilience enhancement. This framework provides actionable insights for practitioners to optimize microgrid design, for policymakers to create adaptive regulations, and for researchers to address knowledge gaps in the field. The findings underscore the critical role of advanced control methodologies in improving adaptability and efficiency under diverse operational conditions. By addressing the lack of an integrated approach in the existing literature, this study contributes to advancing resilient energy systems, supporting the energy transition, and promoting energy security. Future research should validate the proposed framework through empirical studies and explore scalable, cost-effective solutions to enable widespread adoption.