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.
This research project was organized to advance scholarship and practice in the integration of weather forecasts into decision-making. There has been a great deal of public discussion about the potential value of advanced analytics to help people and organizations make better decisions. The promise is that predictive analytics and other types of modeling can help decision-makers gain useful insights into what the future might hold, and how their decisions may advance or retard the achievement of their goals. Yet although weather forecasting is arguably the original "Big Data" application, there has been comparatively little investigation of how to create systems that integrate numerical weather forecasts into automated decision applications. The project demonstrated the feasibility of using such algorithmic, data-driven approaches to improve productivity in one important application area: meteorological field campaigns in the atmospheric sciences. The techniques developed offer promise to improve outcomes in a wide range of application areas where weather plays an important factor. Intellectual merit: The project developed methodological innovations in two domains. First, an approach was perfected for converting general-purpose weather forecast products into probabilistic forecasts highly specific to particular decision settings. Second, an optimization scheme was developed for the entire class of decision problems characterized by three factors: a need to allocate scare resources across time; uncertainty that is resolved progressively via forecasts; and a need to navigate trade-offs between multiple competing objectives. Broader impacts: An enormous variety of societal challenges are substantially driven by weather. These include problems in agriculture, epidemiology, transport, energy, public safety, and many other fields. Across these diverse application fields arise numerous decision problems having structural commonalities with the problem analyzed in this project. By developing tools for making better decisions in application areas of this type, the project assists the creation of a society more resilient in the face of weather-related hazards.
