Abstract
Lagrangian sensing for tracing hydrodynamic trajectories is an innovative approach for studying estuarial environments. Actuated Lagrangian sensors are capable of avoiding obstacles and navigating when active and retain a passive hydrodynamic profile that is suited for Lagrangian sensing when passive. A heterogeneous fleet of actuated and passive drifting sensors is presented. Data assimilation using a high-performance computing HPC cluster that runs the ensemble Kalman filter EnKF is an essential component of the estuarial state estimation system. The performance of the mixed capability fleet and the data assimilation backend is evaluated in the context of a landmark 96-unit river study in the Sacramento-San Joaquin Delta region of California.
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