Collaborative Research: DC3--Integrating Probabilistic Forecasting and Optimization to Provide Decision Support to Meteorological Field Campaigns

合作研究：DC3——概率预报与优化相结合，为气象现场活动提供决策支持

• 批准号: 1063692
• 负责人: Johannes Verlinde
• 金额: $ 36.54万
• 依托单位: Pennsylvania State Univ University Park
• 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: 2011-09-15 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1063692&HistoricalAwards=false
• 关键词: Collaborative; Research; DC3; Integrating; Probabilistic

Efforts to deploy complex field data collection platforms (such as specially-instrumented research aircraft) in large meteorological experiments depend on specific weather conditions, equipment readiness etc. and thus involve both logistical and intellectual challenges to optimally allocate finite resources such as funded flight hours and available crew time to meet stated objectives. These challenges are magnified by uncertainties inherent in numerical weather prediction (NWP) guidance and factors such as multiple prioritized experimental objectives and geographically diverse study regions. This research will initially serve to guide deployment of multiple aircraft across multiple study regions as planned during DC3, the Deep Convective Clouds and Chemistry project. Previous work by this investigative team has integrated probabilistic forecasting methods with optimization techniques adapted from operations research to develop an automated weather-driven decision support system. This new effort will advance this work in three important ways. First, a novel statistical post-processing algorithm will be developed that converts NWP output into calibrated estimates of probability of occurrence for key events of interest to field experiment decision makers. Second, these techniques will be extended to a multi-objective decision model. Third, a system will be created that allows users (in this case, flight mission scientists) to enter their own studied judgments regarding selected variables in addition to more objective guidance gleaned from NWP models. This uniquely tailored algorithmic architecture will combine the best attributes of expert judgment and automation to optimize resource allocation decisions in rapidly-evolving field experiment setting.The intellectual merit of this project centers on extension of an existing body of theory and practice for algorithm-aided decision support to a multi-objective weather research problem subject to multiple resource constraints. This work will bridge methodologies developed in the arenas of finance and quantitative environmental decision analysis. Broader Impacts beyond desirable interdisciplinary student education will include development and application of a rigorous quantitative framework for determining those resources needed to achieve specified scientific outcomes during large meteorological field campaigns, and ultimately serve to provide guidance needed to achieve multiple research objectives by the most efficient means possible.

在大型气象实验中部署复杂的现场数据收集平台（如专用仪器研究飞机）的努力取决于特定的天气条件、设备准备情况等，因此涉及后勤和智力方面的挑战，以优化分配有限的资源，如资助的飞行时间和可用的机组人员时间，以实现既定目标。数值天气预报（NWP）指南固有的不确定性以及多个优先实验目标和地理上不同的研究区域等因素放大了这些挑战。这项研究最初将用于指导在DC3（深层对流云和化学项目）期间在多个研究区域部署多架飞机。该调查小组之前的工作是将概率预测方法与运筹学的优化技术相结合，开发一个自动天气驱动的决策支持系统。这项新的努力将在三个重要方面推进这项工作。首先，将开发一种新的统计后处理算法，将NWP输出转换为对现场实验决策者感兴趣的关键事件发生概率的校准估计。其次，将这些技术扩展到多目标决策模型。第三，将创建一个系统，允许用户（在这种情况下，飞行任务科学家）输入他们自己对选定变量的研究判断，以及从NWP模型中收集到的更客观的指导。这种独特的定制算法架构将结合专家判断和自动化的最佳属性，在快速发展的现场实验环境中优化资源分配决策。该项目的智力价值集中在将现有的理论和实践体系扩展为算法辅助决策支持，以解决受多种资源约束的多目标天气研究问题。这项工作将把金融和定量环境决策分析领域发展起来的方法联系起来。除了理想的跨学科学生教育之外，更广泛的影响将包括制定和应用严格的定量框架，以确定在大型气象野外活动中实现特定科学成果所需的资源，并最终提供以最有效的方式实现多个研究目标所需的指导。