High-impact Events Research Articles

I've booked you a place, good luck: Applying behavioral science to improve attendance at high-impact job recruitment events

Finding a job, especially in a recovering or uncertain economy, is challenging. Jobseekers Allowance (JSA) welfare benefit claimants in the United Kingdom have many competing options available to them in terms of how they direct their efforts in looking for work. Jobcentres, the organizations that support job seekers, have very strong links to the labor market and often run recruitment events in direct partnership with large employers seeking to hire in bulk. Attendance at these events, or any other specific job search activity, is typically low. This article reports the results of a randomized control trial designed to test the effectiveness of mobile phone text messaging in compelling jobseekers to attend such events. Tailored text messages are found to significantly increase the likelihood of attendance. We find text messages to be particularly effective when they seek to induce reciprocity and address low morale in the recipient.

Optimal allocation of limited resources to biosecurity surveillance using a portfolio theory methodology

Newly arrived pests and diseases pose a continuous threat to agriculture and the environment, and there are limited resources available for early detection programs. We formulate an optimal resource-allocation framework for early detection. The approach is based on the principles of portfolio theory although, unlike standard portfolio theory and its previous applications to biosecurity problems, return is modelled using a flexible mixed-distribution and an uncertainty measure is defined using the Chernoff bound. The Chernoff bound does not assume underlying symmetry and allows sensitivity to both stochasticity and parameter uncertainty to be incorporated systematically into the optimisation process. Optimisation is a trade-off between expected return values and uncertainty while, at the same time, hedging against low-probability high-impact events. In an application to the islands of Torres Strait (Australia), we demonstrate how uncertainty, stochasticity and linkages between surveillance locations interact to significantly alter relative allocation, when compared with allocation based on expected values alone. Locations of particular interest for biosecurity are identified and results lead to a more even spread of resources, or greater surveillance ‘diversification’.

Contingency Inventory Reservation for Low-Probability High-Impact Events

This article investigates reservation contracts for contingency inventory management between two buyers and a single supplier under a game theoretic framework. Two channel structures are considered in this context. In the first setting, the buyers simultaneously move to offer reservation fees to the supplier, who in turn, decides on the inventory amount she wants to carry for each buyer. In the second setting, the supplier moves first and offers nonrefundable-deductible reservation fees for the buyers, who respond with their respective reservation quantities. By reserving through a shared supplier, the buyers enable a contingency inventory pool which alleviates overage risk for the supplier and enables availability of products after low-probability high-impact events. Conditions for successful implementation of contingency reservation contracts are investigated. The results obtained for both channel structures were contrasted. It is shown that in a market where the buyers have more negotiation power, reservation contracts are more likely to achieve inventory buildup under relatively lower event probabilities.

Risk-based probabilistic-possibilistic self-scheduling considering high-impact low-probability events uncertainty

In recent years, examining the ruinous consequence of extreme weather events on the power system is one of the most challenging issues that researchers have confronted to. Considerable extreme conditions are generally the missing part of a realistic self-scheduling problem. Considering high-impact low-probability (HILP) events into the model have at least two benefits: first, generation companies (GenCos) can elude from financial disadvantages of upcoming HILP events and then the ISO can better clear energy and reserve markets with a preventive-oriented process to enhance power system resilience. This paper provides a pre-extreme condition self-scheduling for a price-taker generation company with renewable generation units which participates in the day-ahead energy and spinning reserve markets. Uncertainties associated with electricity prices and wind power production are characterized by multiple stochastic scenarios. The stochastic behavior of wind power is presented by using the Beta probability density function (PDF). In order to model the uncertainty of forced outages of generating units due to HILP events and the probability of being called for reserve deployment, a possibilistic approach is proposed. By comparing the generation scheduling under different risk factors and according to the financial disadvantages of HILP events, the conditional value-at-risk (CVaR) risk-averse strategy is considered into the model.

Managing Safety-Related Disruptions: Evidence from the U.S. Nuclear Power Industry.

Low-probability, high-impact events are difficult to manage. Firms may underinvest in risk assessments for low-probability, high-impact events because it is not easy to link the direct and indirect benefits of doing so. Scholarly research on the effectiveness of programs aimed at reducing such events faces the same challenge. In this article, we draw on comprehensive industry-wide data from the U.S. nuclear power industry to explore the impact of conducting probabilistic risk assessment (PRA) on preventing safety-related disruptions. We examine this using data from over 25,000 monthly event reports across 101 U.S. nuclear reactors from 1985 to 1998. Using Poisson fixed effects models with time trends, we find that the number of safety-related disruptions reduced between 8% and 27% per month in periods after operators submitted their PRA in response to the Nuclear Regulatory Commission's Generic Letter 88-20, which required all operators to conduct a PRA. One possible mechanism for this is that the adoption of PRA may have increased learning rates, lowering the rate of recurring events by 42%. We find that operators that completed their PRA before Generic Letter 88-20 continued to experience safety improvements during 1990-1995. This suggests that revisiting PRA or conducting it again can be beneficial. Our results suggest that even in a highly safety-conscious industry as nuclear utilities, a more formal approach to quantifying risk has itsbenefits.

A study of accidental impact scenarios for composite wing damage tolerance evaluation

ABSTRACTThe field data characterising aircraft accidental in-service damage was collected, sorted and processed. By means of probabilistic analysis, the wing damageability statistical parameters were determined. The scenarios of wing accidental impacts were described and the qualitative distribution of impact intensity over the wing surfaces was obtained. By means of original analytical method, the metal dent depth data were converted into impact energy data and energy probabilistic distributions were established. It was shown that the functional relationships generated on domestic data are generally consistent with similar foreign results obtained on other types of aircraft with serious differences in operating conditions. Along with realistic impact damage scenarios, the high energy impact events were considered. It was noted that in some cases severe damage events should not be addressed as extremely improbable and should be included into design and certification process.

Strategic planning of supply chains considering extreme events: Novel heuristic and application to the petrochemical industry

Chemical supply chains are a crucial component in the ongoing supply of large population centres. Unfortunately, episodes of extreme weather have, in recent years, revealed vulnerabilities in global supply networks to high-impact events. With a possible increase in both frequency and intensity of these events due to climate change, supply chains are at risk of disruption now more than ever, with potential dire economic, societal, and environmental consequences. Acknowledging that the direct application of stochastic programming in this context can quickly lead to very large CPU times, we propose an algorithm that combines the sample average approximation method with a selection heuristic for extreme event scenarios. Our method allows to analyse the tradeoff between economic performance and disruption risk, identifying supply chain configurations which are more resilient against extreme events. We demonstrate the effectiveness of this methodology in multiple case studies, showing how it identifies near optimal solutions in short CPU times.

THE SLOAN-C PILLARS AND BOUNDARY OBJECTS AS A FRAMEWORK FOR EVALUATING BLENDED LEARNING

The authors contend that blended learning represents a boundary object; a construct that brings together constituencies from a variety of backgrounds with each of these cohorts defining the object somewhat differently. The Sloan-C Pillars (learning effectiveness, access, cost effectiveness, student satisfaction, and faculty satisfaction) provide a foundation for the evaluation of asynchronous learning networks that works equally well for the evaluation of blended learning environments. The Pillars and a simplified model of a learning system, focus on inputs, processes, and outputs, and provide the framework for a case study of blended learning design and evaluation in a 500-student section of an Introductory Psychology course. Results of a multi-method evaluation of this course indicated very high levels of both learning effectiveness and student satisfaction. The article concludes with the suggestion that blended learning may represent a black swan, a high-impact, unpredicted, and rare event that highlights the limitations of our ability to reliably predict the future in any arena, including online learning environments.

A methodology for quantitative vulnerability assessment of coupled multi-hazard in Chemical Industrial Park

Natural disasters triggered technological (Na-Tech) events are low probability but high impact events. The cascading effects occur frequently under the impact of coupled natural disasters and industrial accidents in Chemical Industrial Park (CIP), which exacerbates the vulnerability of CIP. Therefore, it's essential to carry out the coupled multi-hazard vulnerability assessment in CIP. In the present study, a new methodology involving quantitative vulnerability assessment model and partition technology is proposed to assess the regional vulnerability. Considering the characteristics of multi-hazard, the vulnerability theory in CIP is explored, including sensitivity, resilience and exposure. Furthermore, the quantitative vulnerability assessment model for coupled multi-hazard is put forward based on cellular automata (CA) theory. Combined the model with GIS technology, the vulnerability partition technology is developed and the results of the vulnerability assessment can be visualized. The case study in a national CIP located at the coastal area of Southern China evidenced that the coupled multi-hazard vulnerability is much larger than the single-hazard and the validity of the result is verified by graph theory. Therefore, the methodology proposed can be applied to vulnerability assessment within an industrial park level and used to support the decision-making of regional planning, emergency management, disaster prevention and mitigation for CIP.

The European Climate Research Alliance (ECRA): Collaboration from bottom-up

Abstract. The European Climate Research Alliance (ECRA) is an association of leading European research institutions in the field of climate research (http://www.ecra-climate.eu/, last access: 6 December 2018). ECRA is a bottom-up initiative and helps to facilitate the development of climate change research, combining the capacities of national research institutions, and inducing closer ties between existing national research initiatives, projects and infrastructures. ECRA works as an open platform to bring together climate researchers, providing excellent scientific expertise for policy makers and of societal relevance. The ECRA Board consists of representatives of ECRA partners and decides on governance, scientific priorities, and organisational matters. Currently organized into four Collaborative Programmes, climate scientists share their knowledge, experience and expertise to identify the most important research requirements for the future, thus developing a foresight approach. The CPs cover the topics: (1) Arctic variability and change, (2) Sea level changes and coastal impacts, (3) Changes in the hydrological cycle and (4) High impact events. The CP activities are planned in workshops and participation is open to all interested scientists from the relevant research fields. In particular, young researchers are actively encouraged to join the network. Each CP develops its joint research priorities for shaping European research into the future. Because scientific themes are interconnected, the four Collaborative Programmes interact with each other, e.g. through the organization of common workshops or joint applications. In addition, the Collaborative Programme leads attend the Board meetings. The different formats of ECRA meetings range from scientific workshops to briefing events and side events at conferences to involve different groups of interests. This facilitates the interaction of scientists, various stakeholder groups and politicians. A biennial open ECRA General Assembly that is organised in Brussels represents an umbrella event and acts as a platform for discussion and contact with stakeholders. This event is an excellent opportunity to jointly discuss research priorities of high societal relevance.

Assessments of Preparedness, Agri-food Impacts and Implications for Disaster Risk Management of Fukushima Nuclear Disaster

On March 11, 2011 the strongest ever recorded in Japan earthquake occurred which triggered a powerful tsunami and caused a nuclear accident in Fukushima nuclear plant. The later was a “man-made” disaster having immense impacts on people’s life, health and property, infrastructure, supply chains, economy, policies, natural and institutional environment, etc. This paper assesses preparedness for and agri-food impacts of the Fukushima nuclear disaster, identifies challenges in post-disaster recovery, and withdraws lessons for improving disaster risk management. Japan was not well prepared for such huge disaster while agri-food sector and consumption have been among the worst hit areas. The triple disaster was a rare but a high impact event, therefore, it is necessary to “prepare for unexpected”. Risk assessment is to include diverse hazards and multiple effects of a likely disaster, it is to be discussed with all stakeholders, and measures taken to educate and train all for complex disasters. It is necessary to modernize property rights, regulations, safety standards, and norms, enhance capability of responsible public authorities and improve coordination between divers actors. It is important to set up mechanisms for effective public resource allocation and reduction of agents’ costs. Different elements of agri-food chain have dissimilar capability requiring differential public support. There is a strong “regional” interdependency of agrarian, food and rural assets (and damages), and it is important to properly locate risk and take prevention and recover measures. Disaster response demonstrated the important role of small scale farms and food organizations, and high efficiency of private, market and collective governance. Before, during and after a disaster all available information from all sources is to be immediately publicized in understandable form through all possible means. Disaster provides opportunity to discuss, introduce and implement fundamental changes in agricultural, economic, regional, energy, disaster management, etc. policies. It is important to learn from the past experiences and make sure that “lessons learned” are not forgotten.

High-impact thunderstorms of the Brisbane metropolitan area

Accurate thunderstorm warnings in the hours-to-minutes preceding impact are often limited by the complex evolution of the mesoscale atmospheric environment. To accurately capture these complexities, analysis of observations remained central to operational short-term nowcasting predictions of thunderstorms. Over the past 40 years, multiple highimpact thunderstorm events have impacted the Brisbane Metropolitan Area (BMA) of South East Queensland resulting in significant insured losses. Four of these high-impact events were the focus of the following work. These cases included three events that resulted in the greatest insured losses for the BMA, exceeding AU$4 billion (2017) (18 January 1985, 16 November 2008 and 27 November 2014) and a fourth significant event (24 December 1989). Synthesis of previous work indicates that the four high-impact cases occurred during a south-easterly change with strengthening winds ahead of the change, suggesting commonalities may exist that can be exploited for forecasting. This paper provides a detailed observational analysis of the environment and convective storms from the four BMA events to explore discriminating characteristics that may improve the skill of nowcasting. For the four BMA events, significant deep convection was observed along the change for the hours prior to the change’s arrival at the Brisbane Airport, potentially acting as an early indicator of favourable conditions for high-impact thunderstorms. It was found that the timing of the south-easterly change through Brisbane was also highly correlated for all events, occurring within a 90-min window during the mid-afternoon convective heating maximum. Despite the destructive severe weather, upper air conditions were marginal for supporting organised thunderstorms, highlighting the importance of capturing mesoscale processes, such as the south-easterly change. To further understand possible discriminators of the four high-impact BMA cases, a 10-year climatology of the mesoscale and synoptic environment associated with south-easterly change events was developed for the warm season months of November to January. It is shown that although only a small number of events are associated with high-impact BMA thunderstorms, these events share a set of conditions relating to the prechange wind shift, timing of the south-easterly change and radar signatures.

Proactive Resilience of Power Systems Against Natural Disasters: A Literature Review

The increase in power outages caused by high-impact low-probability events, such as extreme weather-related climate variation events, is the main reason behind studying power system resilience. However, the concepts of resilience as well as the proactive procedures that can be carried out to address these events are such have not so far been satisfactorily investigated notwithstanding their growing benefits. This paper exhibits a review of the current research on power system resilience; which predominantly focuses on proactive resilience against natural disasters. Firstly, it gives a theoretical framework to acquire insights into power system resilience and its key features. Secondly, it presents frameworks for proactive resilience of power systems with a spotlight on the extreme weather events and their effect. Finally, various strategies for preparing, hardening and enhancing proactive resilience with a focus on microgrids for improving power system resilience are reviewed.

MODELLING PARAMETERS AND IMPACTS OF FOUR EXTRATROPICAL CYCLONES UNDER FUTURE CLIMATE SCENARIOS

Coastal zone is among the most vulnerable areas of the Earth to global warming, firstly, due to anticipated mean sea level rise, and secondly, regarding changes in storm activity. In Europe, much of the high-impact weather events are associated with extratropical cyclones (ETC). It is highly possible that tropical cyclones will get stronger under future climate conditions, however projections for ETC-s are still far more mixed. The study at hand is an improved extension to that of Mäll et al. (2017) where the authors aimed to study how an individual extreme storm (2005 Gudrun) would change under future climate conditions. The main aim is to learn whether the similar results are found for four different (tracks, thermal conditions) ETC-s under improved methodology and homogenous initial and boundary conditions in order to decrease uncertainty and draw stronger climatological generalizations. The study area is the Baltic Sea region and more specifically Estonia.

No wink of sleep: Population sleep characteristics in response to the brexit poll and the 2016 U.S. presidential election

IntroductionPersonal well-being, including people's sleep characteristics, is affected by a variety of factors, one example of which is wide-ranging high-impact public events. In this study, we use a large sleep database obtained through a smartphone application for sleep tracking via anonymized time-sampled data to study the effect of two political events with a wide-ranging impact on people's sleep characteristics: the Brexit referendum in June 2016, and the presidential election of Donald Trump in November 2016 MethodUsing Sleep as Android — an actigraphy-based sleep monitoring smartphone application — we collected 10.5 million geo-located sleep records from more than 69,000 users in Europe and North America. Population-based changes in sleep around each of these two events, in the United Kingdom and in the United States of America, were assessed using a non-parametric bootstrap test ResultsThe analysis revealed a significant reduction by 16 min and 21 s in the mean sleep duration of British people in the night after the Brexit poll (p < 0.001). Similarly, the analysis of the US presidential election revealed a significant 12 min 49 s drop in the mean sleep duration during the night following the event, in comparison with the whole studied region (p < 0.001), and an increase by 5 min and 9 s in the subsequent night (p = 0.0328). Additional analysis comparing the election night to comparable days in preceding years revealed that the actual reduction in sleep length may have been even greater. There is also an increase in the proportion of subjects with very short sleep ConclusionsThe results demonstrate a significant impact of two specific major political events on population sleep characteristics. Our results further underline the potential of mobile applications and informatics approaches in general to provide data that enable us to investigate fundamental physiological variables over time and location.

High-Fidelity Simulation Training for Nurse Anesthetists Managing Malignant Hyperthermia: A Quality Improvement Project

BackgroundMalignant hyperthermia (MH) is a low-frequency, high-impact event that many certified registered nurse anesthetists may be unprepared to manage due to limited clinical experience. MethodA pretest, posttest, repeated-measures design was used to evaluate whether certified registered nurse anesthetists' MH knowledge improved immediately after and three months after high-fidelity simulation training. Technical and nontechnical skills were assessed during simulation training. ResultsAfter implementation, mean MH knowledge scores and self-confidence levels remained higher at three-month postimplementation than pretest scores. ConclusionMH simulation training increased participant's knowledge and self-confidence needed to manage an MH crisis.

Review of Lidar-Based Applications for Aviation Weather

Measurements collected by Leosphere Doppler lidars were reviewed to study meteorological processes such as wind shear, wind profiles, gust fronts, and wake vortices over airports. First, the basic concepts of lidar are discussed, then its use for wind environments with respect to high-impact weather events is presented. Issues related to previous definitions of wind-related algorithms and criteria are summarized to validate the use of Doppler lidar for clear-air environmental conditions. Based on International Civil Aviation Organization (ICAO) criteria that use a 500-m height threshold in the vertical for wind warning conditions, this work suggests that use of Doppler lidars can significantly improve the safety of flight environments along landing and takeoff corridors at airports by providing warnings to pilots and ground crew and optimizing air-traffic management. The wind measurements from the lidars are found to be accurate to 0.1 m s−1, and use of Doppler lidars can increase the probability of detection of wind-related severe weather conditions by up to 50% beyond the 500 m of the atmospheric boundary layer (ABL).

Trial Implementation of a Multihazard Risk Assessment Framework for High-Impact Low-Frequency Power Grid Events

The Pacific Northwest National Laboratory developed a risk framework for modeling high-impact, low-frequency power grid events to support risk-informed decisions. In this paper, we briefly recap the framework and demonstrate its implementation for seismic and geomagnetic hazards using a benchmark reliability test system. We describe integration of a collection of models implemented to perform hazard analysis, fragility evaluation, consequence estimation, and postevent restoration. We demonstrate the value of the framework as a multihazard power grid risk assessment and management tool. The research will benefit transmission planners and emergency planners by improving their ability to maintain a resilient grid infrastructure against impacts from major events.

Potential of an EnKF Storm-Scale Data Assimilation System Over Sparse Observation Regions with Complex Orography

High-impact weather events over sparse data regions with complex orography, such as the Mediterranean region, remain a challenge for numerical weather prediction. This study evaluates, for the first time, the ability of a multiscale ensemble-based data assimilation system to reproduce a heavy precipitation episode that occurred during the first Special Observation Period (SOP1) of the Hydrological cycle in the Mediterranean Experiment (HyMeX). During the Intense Observation Period (IOP13) from 14 to 15 October 2012, convective maritime activity associated with an advancing cold front affected coastal areas of southern France, Corsica and Italy. With the main objective of improving forecasts of this weather event, a data assimilation (DA) system using the Ensemble Kalman Filter (EnKF) algorithm is implemented. The potential impact of assimilating conventional in-situ observations (METAR, aircrafts, buoys and rawinsondes) and single-Doppler reflectivity data to improve numerical representation of growing convective maritime structures that will evolve towards coastal populated areas is evaluated. Results indicate that information provided by both observation sources contribute to initiation and subsequent evolution of convective structures not captured by the conventional runs. Notably, data assimilation experiments produce the best quantitative verification scores for the short range (6–8 h) forecasts of accumulated precipitation. Beyond 6–8 h, data assimilation experiments and those without data assimilation are indistinguishable. Sensitivity experiments, evaluating the impact of increasing the length of the radar data assimilation period, reveal the importance of assimilating high-frequency reflectivity data during a mid-term period (6 h approx.) to better depict deep convective structures initiated over the sea that evolve towards populated coastal areas.

NowCastMIX: Automatic Integrated Warnings for Severe Convection on Nowcasting Time Scales at the German Weather Service

AbstractNowCastMIX is the core nowcasting guidance system at the German Weather Service. It automatically monitors several systems to capture rapidly developing high-impact mesoscale convective events, including 3D radar volume scanning, radar-based cell tracking and extrapolation, lightning detection, calibrated precipitation extrapolations, NWP, and live surface station reports. Within the context of the larger warning decision support process AutoWARN, NowCastMIX integrates the input data into a high-resolution analysis, based on a fuzzy logic approach for thunderstorm categorization and extrapolation, to provide an optimized warning solution with a 5-min update cycle for lead times of up to 1 h. Feature tracking is undertaken to optimize the direction of warning polygons, allowing individual, tangentially moving cells or cell clusters to be tracked explicitly. An adaptive ensemble clustering is deployed to reduce the spatial complexity of the resulting warning fields and smooth noisy temporal variations to a manageable level for duty forecasters. Further specialized outputs for civil aviation and for a public mobile phone warning app are generated. Now in its eighth year of operation, a comprehensive and complete set of thunderstorm analyses and nowcasts over Germany has been created, which is of unique value for ongoing research and development efforts for improving the system, as well as for addressing climatological aspects of severe convection. Verification has shown that NowCastMIX has helped to significantly improve the quality of the official warnings for severe convective weather events when used within the AutoWARN process.