CAREER: Uncertainty Propagation and Data Assimilation for Toxic Cloud Prediction

职业：有毒云预测的不确定性传播和数据同化

• 批准号: 1054759
• 负责人: Puneet Singla
• 金额: $ 41.2万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1054759&HistoricalAwards=false
• 关键词: CAREER; Uncertainty; Propagation; Data; Assimilation

This Faculty Early Career Development (CAREER) award focuses on developing mathematical tools for accurate characterization and propagation of uncertainty in mathematical models, and fusion of model output with sparse, noisy data to determine estimates of the actual physical phenomenon and statistical measure of confidence in those estimates. The principal goals of this CAREER award are twofold: (i) to understand how the uncertainty of input variables and the random forcing of winds affect the output of the dispersion model, and (ii) to provide a prediction of toxic cloud movement, together with quantitative measures of confidence in that prediction. Uncertainty analysis of dispersion of toxic clouds is becoming one of the most important components for timely and accurate threat assessment from natural or man-made incidents (such as Chernobyl or Eyjafjallajökull incidents). Consequent to the toxic material release, response organizations and industries make decisions based on predictions of the cloud motion, with little knowledge or appreciation of the reliability of those predictions. The quantitative understanding of uncertainty is essential when predictions are to be used to inform policy making or mitigation solutions where significant resources are at stake. For example, an understanding of uncertainty in model predictions can play an essential role in the acceptance of the need to vacate a city in case of toxic material release where the cost of different choices varies by millions of dollars and human lives. If successful, this research work will provide means for evaluating hazard risks in space and time. An important practical outcome will be the ability to generate toxic material hazard maps. This CAREER project also integrates educational outreach efforts into the research plan with the goal to increase the diversity and the number of students from minority groups in science and engineering in association with existing programs on campus. In addition, the PI will design a ``Self-Help'' tutorial web-site to inform and educate both students and researchers about nonlinear filtering and uncertainty characterization concepts. Research and education accomplishments completed, as part of the plan will be described in journal publications, and conference presentations.

该学院早期职业发展（CAREER）奖的重点是开发数学工具，以准确表征和传播数学模型中的不确定性，并将模型输出与稀疏、嘈杂的数据融合，以确定对实际物理现象的估计以及对这些估计的置信度的统计测量。 该职业奖的主要目标有两个：（i）了解输入变量的不确定性和风的随机强迫如何影响扩散模型的输出，以及（ii）提供有毒云运动的预测，以及对该预测的置信度的定量测量。 有毒云扩散的不确定性分析正在成为及时、准确地评估自然或人为事件（例如切尔诺贝利或埃亚菲亚德拉冰盖事件）威胁的最重要组成部分之一。由于有毒物质的释放，响应组织和行业根据云运动的预测做出决策，但对这些预测的可靠性知之甚少。当预测用于为涉及重要资源的决策或缓解解决方案提供信息时，对不确定性的定量理解至关重要。例如，对模型预测中的不确定性的理解可以在接受有毒物质泄漏时撤离城市的必要性方面发挥重要作用，其中不同选择的成本相差数百万美元和人的生命。如果成功，这项研究工作将为评估空间和时间上的灾害风险提供手段。一个重要的实际成果将是生成有毒物质危险地图的能力。 该职业项目还将教育推广工作纳入研究计划，目的是增加科学和工程领域少数群体学生的多样性和数量，并与校园现有项目相结合。此外，PI 将设计一个“自助”教程网站，向学生和研究人员提供有关非线性滤波和不确定性表征概念的信息和教育。作为计划的一部分，已完成的研究和教育成就将在期刊出版物和会议演讲中进行描述。