This research investigates a new planning strategy for a team of mobile sensors that coordinate measurements to maximize the accuracy of weather forecasts.
We combine technologies from the nonlinear weather prediction and planning/control communities to create a close link between model predictions and observed measurements, choosing future measurements that minimize the forecast error under time-varying conditions.

The main result will be a new framework for coordinating a team of mobile observing assets that provides more efficient measurement strategies and a more accurate means of capturing spatial correlations in the system dynamics, which will have broad applicability to measurement and prediction in other domains.

We have approached the problem on three fronts.
  1. We have developed an information-theoretic algorithm for selecting environment measurements in a computationally effective way. This algorithm determines the best discrete locations and times to take additional measurement for reducing the forecast uncertainty in the region of interest while considering the mobility of the sensor platforms.
  2. We have developed a second algorithm for planning trajectories between target measurement points that further decrease the predicted forecast error. The system learns to use past experience in predicting good routes to travel between measurements. Experiments show that these approaches work well on simplified Lorenz models of weather patterns.
  3. More realistic experiments will be conducted using the Navy's Coupled Ocean Atmosphere Prediction System (COAMPS), a fullscale regional weather research and forecasting model.  COAMPS data will be assimilated using the Data Assimilation Research Testbed (DART) ensemble square root filter.