The research will construct an analytical framework to reduce uncertainty in forecasts of hurricane intensity by optimally targeting a coordinated observing network of unmanned aircraft using ensemble-based adaptive sampling and coordination of sampling trajectories. An ensemble-based theory combined with serial adaptive sampling and rapid assimilation updates will be employed for the first time to yield probabilistic flow estimates and optimal sampling configurations. A new theory in decentralized motion coordination will be developed to account for spatially and temporally variable flow fields that exceed the platform speed relative to the flow. The framework will be evaluated using a hierarchy of hurricane models to assess improvements in probabilistic forecasts of the flow. The proposed research will achieve theoretical advances broadly applicable to environmental sampling, including ensemble-based assimilation of near-continuous data, ensemble-based adaptive sampling, and decentralized coordination of unmanned platforms in dynamic flow fields.
The broader significance of this research project lies in its potential to improve hurricane forecasts by integrating next-generation weather prediction models with novel strategies for adaptive motion coordination of multiple unmanned aircraft.
Targeting Observations of Tropical Cyclones using Cooperative Control of Unmanned Aircraft is a four-year, $275K grant.