"This proposal will be awarded using funds made available by the American Recovery and Reinvestment Act of 2009 (Public Law 111-5), and meets the requirements established in Section 2 of the White House Memorandum entitled, Ensuring Responsible Spending of Recovery Act Funds, dated March 20, 2009. I also affirm, as the cognizant Program Officer, that the proposal does not support projects described in Section 1604 of Division A of the Recovery Act."
The research objective of this project is to 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. Better forecasts and reduced uncertainty in hurricane intensity prediction lead to reduced loss of life and property via improved emergency management decisions. Intensity forecasts are dependent on the assimilation of observations of pressure, wind velocity, and temperature at altitudes lower than manned aircraft can safely fly. Small, unmanned aircraft can fill this important gap in observation data by flying at altitudes as low as one hundred meters. A coordinated team of unmanned aircraft can further reduce the uncertainty in intensity forecasts by collecting low-altitude observations in a prescribed spatial distribution. The project will also provide undergraduate and graduate students with a unique training opportunity at the intersection of atmospheric science and dynamical control systems.
