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: 1131074
• 负责人: E Bruce Pitman
• 金额: $ 53.77万
• 依托单位: SUNY at Buffalo
• 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 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1131074&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.

该赠款提供了支持，以开发一种方法，以定量评估对火山喷发引起的灰云运动预测的信心。通过将统计建模，随机分析和计算科学的工具与广泛使用的火山粒子分散代码结合在一起，该赠款将提供工具来评估灰云运动的实时预测，这些预测是风险不同的风险和一系列模型变量。更具体地说，Puff计算机代码将用作Ash Cloud Simulation工具。吹气的泡芙繁殖烟灰，将与称为弯曲的火山喷发模型整合在一起。弯曲将灰分分布作为时间和高度的函数。这种集成替代了未知的粉扑输入参数，几个弯曲输入较好（但不是完全）受物理的约束。 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田模型的影响，对云位置的输出分布的影响。多种方面的混乱正交和随机整合技术将用于提供对这些预测的可靠性（误差）的定量度量。 了解分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很重要。在火山附近和爆发方面都有经济和社会学的影响。例如，航空行业决定基于分散模型预测的一部分飞行时间表，路线和燃油消耗估算。 这项研究的结果将为决策者和民用保护当局提供评估危险风险的框架。重要的是，这种方法论的发展与特定的粉扑代码无关，因此预测和可靠性分析的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险模型（例如放射学和化学羽流散布），以及从卡车或铁路事故中释放天然气。