Communicating optimized decision input from stochastic turbulence forecasts
Szczes, Jeanne R.
Eckel, F. Anthony
Harr, Patrick A.
MetadataShow full item record
The uncertainty of weather forecasts contributes to mission risk. Ensemble data can improve combat capability by incorporating forecast uncertainty into the warfighter decision process. The study transforms raw ensemble data into optimized decision inputs for upper level turbulence using ORM principles and decision science. It demonstrates the methodology and importance of incorporating ambiguity, the uncertainty in forecast uncertainty, into the decision making process using the Taijitu method to estimate ambiguity. Comparing ambiguity and risk tolerance uncertainty intervals produces a more appropriate decision input compared to currently existing methods. Significant differences between the current and research derived decision input products demonstrate potential value added to decision making by incorporating ambiguity information. An effective visualization is devised for varying levels of risk tolerance and mission thresholds that is educational and practical for users. Research procedures and results can serve as an example to further education and development of stochastic methods in the Air Force and Department of Defense.
Showing items related by title, author, creator and subject.
Allen, Mark S. (Monterey, California: Naval Postgraduate School, 2009-09);An ensemble prediction system (EPS) generates flow-dependent estimates of uncertainty (i.e., random error due to analysis and model errors) associated with a numerical weather prediction model to provide information ...
Mortlock, Robert (Monterey, California. Naval Postgraduate School, 2017-07); NPS-AM-17-211This Enhanced Combat Helmet (ECH) case study encourages critical analysis of a U.S. Defense Department project at two key decision points: project start and production. The case centers on the development, testing, and ...
Royset, Johannes O.; Wets, Roger J-B (2016-02-08);Stochastic ambiguity provides a rich class of uncertainty models that includes those in stochastic, robust, risk-based, and semi-in nite optimization, and that accounts for both uncertainty about parameter values as well ...