In cooperative motion theory, advanced methodologies for coordination of vehicles have been developed for scenarios where the speed of the flow is slower than the speed of the platform. However, when the flow speed is fast (such as in a hurricane), it is much more difficult to stabilize the vehicle motion. We are establishing new dynamic control laws for spatiotemporally-varying fast flow fields.
In this study, we conduct an observation system simulation experiment (OSSE) to evaluate the forecast impact of synthetic observations drawn from a minimally parameterized family of single-vehicle trajectories. We first consider a perfect model framework: both the nature run and the forecast model are based on a modified version of the well-known Rankine vortex. An ensemble Kalman filter (EnKF), in a rapidly-updating cycle, is run numerous independent times in order to cover the entire parameter space of possible trajectories. Preliminary results presented here indicate the relative effectiveness of different sampling strategies. In the future, we will extend our OSSE work to an imperfect model with multiple coordinated vehicles. Once the adaptive sampling framework is mature, we will move from 2-D idealized models to full 3-D simulations with the Weather Research and Forecasting model (WRF).