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

在大型气象实验中部署复杂的现场数据收集平台（例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挑战，以优化分配有限的资源，例如资助飞行小时和可用机组人员时间以实现既定目标。 数值天气预报（NWP）指导中固有的不确定性以及多个优先实验目标和地理上多样化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（深对流云和化学项目）期间计划在多个研究区域部署多架飞机。 这个调查小组以前的工作已经整合了概率预测方法与优化技术，从运筹学适应开发一个自动化的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动这项工作。 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，这些技术将被扩展到一个多目标决策模型。 第三，将创建一个系统，允许用户（在本例中，飞行使命科学家）输入他们自己对选定变量的研究判断，以及从数值预报模型收集的更客观的指导。 这种独特的定制算法架构将联合收割机的专家判断和自动化的最佳属性，以优化资源分配的决策，在快速发展的现场实验setting.The智力的优点，这个项目的中心扩展现有的理论和实践的算法辅助决策支持的多目标天气研究问题受到多个资源的限制。 这项工作将把在金融和定量环境决策分析领域制定的方法联系起来。 超越理想的跨学科学生教育的更广泛的影响将包括制定和应用一个严格的定量框架，以确定在大型气象实地活动中实现特定科学成果所需的资源，并最终为通过最有效的手段实现多个研究目标提供指导。

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.