Enhanced Ocean Predictability Through Optimal Observing Strategies.

摘要

Long Term Goals. The long-term goal of this research is to develop the requisite technologies for effective observation strategies that provide the best possible now-casts and forecasts of oceanic conditions. This research contributes to the effort to predict mesoscale and submesoscale conditions and to understand the physical processes responsible for these conditions. OBJECTIVES. Three tightly integrated objectives form the goal of this research: integrating Lagrangian methods from dynamical systems into oceanographic applications, designing an optimal observing strategy based on these methods, and applying the results to data assimilating ocean models. Since the focus is Lagrangian data, the later two objectives are slanted towards optimal drifter deployment. The Gulf of Mexico primarily is used in this effort because both high-resolution numerical model results and drifter data were available. Ultimately, these three objectives will lead to a cumulative understanding of how drifter deployments may be used in conjunction with coherent features in the ocean, such rings and eddies, to understand how their Lagrangian boundaries effect advection. This Lagrangian data provides crucial information unavailable with Eulerian estimations of the ocean state. APPROACH. Objective Eulerian current reconstruction initiated by Rao and Schwab (1981), Eremeev et al (1992) , Chao et al (1998), and Lipphardt et al (2000) as well as Lagrangian methods using material curves to delineate coherent flow features presented in Poje and Haller (1999) form the core approach. The Eulerian reconstruction methods result from prior ONR support. The research here focuses on how the material curve analysis may be applied to ocean flow, specifically data assimilating GCMs. This analysis results in a Lagrangian flow template which is then combined with the objective reconstruction methods to optimally sample the ocean state.