Observing system simulation experiments for unmanned aircraft in an idealized vortex model framework

The accuracy of tropical cyclone intensity forecasts is limited by poor coverage of in-situ observations, especially in the boundary layer. Unmanned aircraft systems (UAS), such as the Aerosonde, can sample regions that are currently not observed well by manned aircraft and satellites. In order to enable future deployment of UAS for routine tropical cyclone monitoring, an integrated adaptive sampling framework must be developed to collect and assimilate UAS observations on a nearly continuous basis. Our project involves a unique collaboration between two groups: novel motion coordination strategies are developed at the University of Maryland, while atmospheric data assimilation is performed at the University of Miami.

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).