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.
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.
- 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.
- 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.
- 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.