Evaluation of the Hf-Radar Network System around Taiwan using Normalized Cumulative Lagrangian Separation

The Lagrangian separation distance between the endpoints of simulated and observed drifter trajectories is often used to assess the performance of numerical particle trajectory models. However, the separation distance fails to indicate relative model performance in weak and strong current regions, such as over continental shelves and the adjacent deep ocean. A skill score described in detail by Lui, et al. was applied to estimate the cumulative Lagrangian separation distances normalized by the associated cumulative trajectory lengths [1]. The proposed dimensionless skill score is particularly useful when the number of drifter trajectories is limited and neither a conventional Eulerian‐based velocity nor a Lagrangian based probability density function may be estimated. The skill score assesses The Taiwan Ocean Radar Observing System (TOROS) performance. TOROS consists of 17 SeaSonde type radars around the Taiwan Island. The currents off Taiwan are significantly influenced by the nearby Kuroshio Current. The TOROS High Frequency radars network product is useful for providing essential information on ocean surface currents of use in water property transports, fishing, offshore oil and gas operations, hazardous spill mitigation, search and rescue, etc.