The statistical design of assessments of impacts from explosive volcanic eruptions

火山喷发影响评估的统计设计

• 批准号: 2437902
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2437902
• 关键词: statistical; design; assessments; impacts; explosive

Explosive volcanic eruptions generate far-reaching impacts, in particular the dispersion of volcanic ash in the atmosphere and its deposition on the ground which can have significant effects on agriculture, infrastructure, and human health. The dispersion of ash in the atmosphere is dependent on the magnitude of the eruption, and is driven by wind fields which are highly variable and difficult to forecast. Prediction of atmospheric ash dispersion is further complicated by the rarity of such eruption events, as well as the lack of direct observations of ash concentration at sufficiently high spatial and temporal resolution during such eruptions. This project will focus on the statistical design of operational assessments of volcanic ash dispersion, including:1. Statistically-coherent design of probabilistic volcanic ash hazard assessments. Current practice in producing a probabilistic ash hazard assessment uses an ensemble of simulations with sizes set by time or computing constraints, with the result that the variance of individual impact thresholds vary within and between assessments. This component of the research will use fundamental statistical approaches to standardise the design of probabilistic ash hazard assessments, and explore (i) variance reduction through stratification via the use of weather patterns as forcing data for ensembles, and (ii) appropriate statistical methods for larger (and consequently rarer) eruptions;2. Bayesian statistical methods to characterise uncertainty in estimation of the intensity of explosive eruptions and ensemble forecast design. Current practice in operational response to volcanic eruptions and in eruption forecast design uses observations of the eruption plume height to estimate the mass eruption rate through a semi-empirical relationship. This component of the research will use applied Bayesian regression methods to characterise the uncertainty in the height-eruption rate semi-empirical relationship, and thus in the design of ensemble forecasts and operational ash dispersion modelling.

火山爆发会产生深远的影响，特别是火山灰在大气中的扩散和在地面上的沉积，会对农业、基础设施和人类健康产生重大影响。火山灰在大气中的扩散取决于火山喷发的规模，并受到风场的影响，风场变化很大，难以预测。由于这种喷发事件的罕见性，以及在这种喷发期间缺乏足够高的空间和时间分辨率的火山灰浓度的直接观测，使大气火山灰扩散的预测更加复杂。该项目将侧重于火山灰扩散业务评估的统计设计，包括：1.概率性火山灰灾害评估的统计连贯设计。目前进行概率性灰害评估的做法是使用一套模拟，其规模由时间或计算限制因素决定，结果是，评估之内和评估之间的个别影响阈值的差异各不相同。研究的这一部分将使用基本的统计方法来验证概率性火山灰危害评估的设计，并探索（i）通过使用天气模式作为合奏的强迫数据分层来减少方差，以及（ii）更大（因此更罕见）的火山爆发的适当统计方法;2.消除爆炸性喷发强度估计和集合预报设计中不确定性的贝叶斯统计方法。目前对火山爆发的业务反应和火山爆发预测设计的做法是利用观测火山喷发羽流高度，通过半经验关系来估计大规模喷发率。研究的这一组成部分将使用应用贝叶斯回归方法来消除高度-喷发率半经验关系的不确定性，从而消除集合预报设计和火山灰扩散业务模型的不确定性。