extreme events
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2022 ◽  
Vol 147 ◽  
pp. 105587
Alon Urlainis ◽  
David Ornai ◽  
Robert Levy ◽  
Oren Vilnay ◽  
Igal M. Shohet

2022 ◽  
Vol 85 ◽  
pp. 102401
Chien-fei Chen ◽  
Thomas Dietz ◽  
Nina H. Fefferman ◽  
Jamie Greig ◽  
Kristen Cetin ◽  

2022 ◽  
Vol 23 (2) ◽  
Navid Tahvildari ◽  
Mirla Abi Aad ◽  
Akash Sahu ◽  
Yawen Shen ◽  
Mohamed Morsy ◽  

2022 ◽  
Ionela Daniela Găitan-Botezatu ◽  

Globally, post-event funding needs are growing, while the material and human damage caused by extreme events is constantly growing. The 2015 United Nations (UN) Global Assessment Report on Disaster Risk Reduction estimated that worldwide, these extreme events cause losses of approximately $ 250-300 billion annually. Although there are now various post-event financing options (insurance, grants, loans, donations, etc.) for the population, companies or public institutions, these instruments are often not sufficient for post-event recovery and reconstruction, so many challenges remain for post-event recovery. Thus, there is often a gap between the financing needs of companies or the population and the existing financing instruments, most often the amounts needed for financing being higher than the amounts that are available through the various existing financing mechanisms. In this article we addressed the topic of post-event funding sources such as donations and highlighted that these, although they are one of the cheapest sources of funding, the support of post-event donors is often uncertain. Also, in the elaboration of this paper I used qualitative and quantitative research based on the use of methods such as Spearman correlation indicator, data processing and analysis, documenting reports, studying reference works and other studies.

Atmosphere ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 117
Philippe Quevauviller

The increasing severity and frequency of extreme weather and climate events (e.g., floods, heat and cold waves, storms, forest fires) resulting from climate change-compounded vulnerabilities and exposure require a specific research focus. Climate-related extreme events are part of disaster risk reduction policies ruled at international, EU, and national levels, covering various sectors and features such as awareness-raising, prevention, mitigation, preparedness, monitoring and detection, response, and recovery. A wide range of research and technological developments, as well as capacity-building and training projects, has supported the development and implementation of these policies and strategies. In particular, research and innovation actions support the paradigm shift from managing “disasters” to managing “risks” and enhancing resilience needs. In this respect, a huge body of knowledge and technology has been developed in the EU-funded Seventh Framework Programme (2007–2013) and Horizon 2020 (2014–2020), for example in the area of measures and technologies needed to enhance the response capacity to extreme weather and climate events affecting the security of people and assets. In addition, networking initiatives have been developed to connect scientists, policy-makers, practitioners, and industry and civil society representatives in order to boost research uptake, identify gaps, and elaborate research programs at EU level. Research and networking efforts are pursued within the newly starting framework program Horizon Europe (2021–2027), with a focus on supporting civil protection operations. This paper provides a general overview of relevant EU policies and examples of past and developing research in the area of weather and climate extreme events and highlights current networking efforts in this area.

2022 ◽  
Vol 9 ◽  
Fan Fang ◽  
Tong Wang ◽  
Suoyi Tan ◽  
Saran Chen ◽  
Tao Zhou ◽  

Background: The measurement and identification of changes in the social structure in response to an exceptional event like COVID-19 can facilitate a more informed public response to the pandemic and provide fundamental insights on how collective social processes respond to extreme events.Objective: In this study, we built a generalized framework for applying social media data to understand public behavioral and emotional changes in response to COVID-19.Methods: Utilizing a complete dataset of Sina Weibo posts published by users in Wuhan from December 2019 to March 2020, we constructed a time-varying social network of 3.5 million users. In combination with community detection, text analysis, and sentiment analysis, we comprehensively analyzed the evolution of the social network structure, as well as the behavioral and emotional changes across four main stages of Wuhan's experience with the epidemic.Results: The empirical results indicate that almost all network indicators related to the network's size and the frequency of social interactions increased during the outbreak. The number of unique recipients, average degree, and transitivity increased by 24, 23, and 19% during the severe stage than before the outbreak, respectively. Additionally, the similarity of topics discussed on Weibo increased during the local peak of the epidemic. Most people began discussing the epidemic instead of the more varied cultural topics that dominated early conversations. The number of communities focused on COVID-19 increased by nearly 40 percent of the total number of communities. Finally, we find a statistically significant “rebound effect” by exploring the emotional content of the users' posts through paired sample t-test (P = 0.003).Conclusions: Following the evolution of the network and community structure can explain how collective social processes changed during the pandemic. These results can provide data-driven insights into the development of public attention during extreme events.

2022 ◽  
Rachel Wai-Ying Wu ◽  
Zheng Wu ◽  
Daniela I. V. Domeisen

Abstract. Extreme stratospheric events such as sudden stratospheric warming and strong vortex events associated with an anomalously weak or strong polar vortex can have downward impacts on surface weather that can last for several weeks to months. Hence, successful predictions of these stratospheric events would be beneficial for extended range weather prediction. However, the predictability limit of extreme stratospheric events is most often limited to around 2 weeks or less. The predictability also strongly differs between events, and between event types. The reasons for the observed differences in the predictability, however, are not resolved. To better understand the predictability differences between events, we expand the definitions of extreme stratospheric events to wind deceleration and acceleration events, and conduct a systematic comparison of predictability between event types in the European Centre for Medium-Range Weather Forecasts (ECMWF) prediction system for the sub-seasonal predictions. We find that wind deceleration and acceleration events follow the same predictability behaviour, that is, events of stronger magnitude are less predictable in a close to linear relationship, to the same extent for both types of events. There are however deviations from this linear behaviour for very extreme events. The difficulties of the prediction system in predicting extremely strong anomalies can be traced to a poor predictability of extreme wave activity pulses in the lower stratosphere, which impacts the prediction of deceleration events, and interestingly, also acceleration events. Improvements in the understanding of the wave amplification that is associated with extremely strong wave activity pulses and accurately representing these processes in the model is expected to enhance the predictability of stratospheric extreme events and, by extension, their impacts on surface weather and climate.

A. Frifra ◽  
M. Maanan ◽  
H. Rhinane ◽  
M. Maanan

Abstract. Storms represent an increased source of risk that affects human life, property, and the environment. Prediction of these events, however, is challenging due to their low frequency of occurrence. This paper proposed an artificial intelligence approach to address this challenge and predict storm characteristics and occurrence using a gated recurrent unit (GRU) neural network and a support vector machine (SVM). Historical weather and marine measurements collected from buoy data, as well as a database of storms containing all the extreme events that occurred in Brittany and Pays de la Loire regions, Western France, since 1996, were used. Firstly, GRU was used to predict the characteristics of storms (wind speed, pressure, humidity, temperature, and wave height). Then, SVM was introduced to identify storm-specific patterns and predict storm occurrence. The approach adopted leads to the prediction of storms and their characteristics, which could be used widely to reduce the awful consequences of these natural disasters by taking preventive measures.

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