IDR/Collaborative Research: Characterizing Uncertainty in the Motion of Volcanic Plumes Advected by Wind Fields

IDR/合作研究：表征风场平流火山羽流运动的不确定性

• 批准号: 1131799
• 负责人: Peter Webley
• 金额: $ 45.23万
• 依托单位: University of Alaska Fairbanks Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1131799&HistoricalAwards=false
• 关键词: IDR; Collaborative; Research; Characterizing; Uncertainty

This grant provides support to develop a methodology for the quantitative assessment of confidence in predictions of the motion of ash clouds caused by volcanic eruptions. By combining statistical modeling, stochastic analysis, and the tools of computational science together with a widely-used volcanic particle dispersion code, this grant will provide tools to assess real time predictions of ash cloud motion that accounts for varying wind conditions and a range of model variables. More specifically, the PUFF computer code will be used as the ash cloud simulation tool. PUFF, which propagates an airborne ash plume, will be integrated with a volcanic eruption model called BENT. BENT outputs ash distribution as a function of time and height. This integration substitutes for the unknown PUFF input parameters several BENT inputs that are better (but not entirely) constrained by physics. This integrated model will be used to develop a framework for analyzing the effects of uncertain parameters and of differing windfield models, on the output distribution for the cloud location. Polynomical chaos quadrature and stochastic integration techniques will be used to provide a quantitative measure of the reliability (i.e. error) of those predictions. Understanding the effects of uncertainty in dispersion model predictions are important to assess and mitigate potential hazards associated with volcanic ash clouds. There are economic and sociologic impacts both near the volcano and far downwind from the eruption. For example, the airline industry decides flight schedules, routes, and fuel consumption estimates, based in part on dispersion model forecasts. The results of this research will provide decision makers and civil protection authorities with a framework for evaluating hazard risk. Importantly, this methodological development is independent of the specific PUFF code, so the framework for prediction and reliability analysis can be directly applied to other ash cloud codes, and more generally to other hazard models -- such as radiological and chemical plume dispersion, and gas release from truck or rail accidents.

这笔赠款支持开发一种方法，用于定量评估火山喷发引起的火山灰云运动预测的置信度。通过将统计建模、随机分析和计算科学工具与广泛使用的火山颗粒分散代码相结合，这笔赠款将提供评估火山灰云运动实时预测的工具，该预测考虑了不同的风力条件和一系列模型变量。更具体地说，PUFF计算机代码将用作灰云模拟工具。 PUFF 会传播空气中的火山灰羽流，它将与称为 BENT 的火山喷发模型集成。 BENT 输出灰分分布作为时间和高度的函数。这种积分用几个更好（但不完全）受物理约束的 BENT 输入代替了未知的 PUFF 输入参数。 该集成模型将用于开发一个框架，用于分析不确定参数和不同风场模型对云位置输出分布的影响。多项式混沌求积和随机积分技术将用于提供这些预测的可靠性（即误差）的定量测​​量。 了解扩散模型预测中不确定性的影响对于评估和减轻与火山灰云相关的潜在危害非常重要。火山附近和远离喷发的下风处都会产生经济和社会影响。例如，航空业部分根据离散模型预测来决定航班时刻表、航线和燃油消耗估算。 这项研究的结果将为决策者和民防当局提供评估灾害风险的框架。重要的是，这种方法的开发独立于特定的 PUFF 代码，因此预测和可靠性分析框架可以直接应用于其他灰云代码，更普遍地应用于其他危害模型——例如放射性和化学羽流扩散以及气体从卡车或铁路事故中释放。