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

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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.