Estimation and adaptive observation of environmental plumes
The estimation and forecasting environmental plume movement based on information from mobile sensors recently received renewed attention due to the Gulf coast oil and Icelandic ash problems, and remains of sustained interest today in homeland security settings (plant explosions, dirty bombs, etc.). The present work refines and tests the scientific algorithms at the heart of this problem. In particular, we combine the Ensemble Kalman Filter (EnKF), which provides a computationally feasible low-rank approximation of the un certainty of the estimate, with our recently developed Dynamic Adaptive Observation (DAO) algorithm for optimizing feasible sensor vehicle trajectories that minimize forecast uncertainty. A numerical experiment is performed which applies this combined EnKF/DAO algorithm to determine waypoints along optimized feasible sensor vehicle trajectories that improve the forecast of an environmental plume represented by a passive scalar convectively driven in a 2D fluid flow.