Merging Disparate Data and Numerical Model Results for Dynamically Constrained Nowcasts

Abstract

LONG-TERM GOALSThe long term goal of our research is to quantify submesoscale dynamical processes and understand their interactions with motions at larger scales. In particular, we focus on the following three areas:Use of high resolution disparate (HRD) data sets to develop dynamically consistent nowcasts of a flow field; Application of HRD observations to dynamical systems studies of the mixing properties of the surface flow field; Use of HRD surface observations to infer subsurface flow conditions. OBJECTIVESOur objective is to combine disparate surface current observations from sources like HF radar, Lagrangian drifters, passive remote sensing and ADCPs with open boundary flow information from any available source (numerical model, observations, climatology, etc.) to develop dynamically consistent nowcasts of the surface flow field. These nowcasts can then be analyzed using existing dynamical systems templates to study the mixing characteristics of the surface flow field. Also, because our formulation is exactly three-dimensionally incompressible, the nowcast can be used to infer some features of the subsurface flow field and may be readily assimilated into a numerical model. APPROACHOur nowcast approach uses normal mode analysis (NMA), a spectral technique that is a generalization of a method first described by Rao and Schwab (1981) in an analysis of currents in Lake Ontario. The