Objectively mapping HF radar-derived surface current data using measured and idealized data covariance matrices

Abstract

Surface currents measured by high-frequency radars are objectively mapped using covariance matrices computed from hourly surface current vectors spanning two years. Since retrievals of surface radial velocities are inherently gappy in space and time, the irregular density of surface current data leads to negative eigenvalues in the sample covariance matrix. The number and the magnitude of the negative eigenvalues depend on the degree of data continuity used in the matrix computation. In a region of 90% data coverage, the negative eigenvalues of the sample covariance matrix are small enough to be removed by adding a noise term to the diagonal of the matrix. The mapping is extended to regions of poorer data coverage by applying a smoothed covariance matrix obtained by spatially averaging the sample covariance matrix. This approach estimates a stable covariance matrix of surface currents for regions with the intermittent radar coverage. An additional benefit is the removal of baseline errors that often exist between two radar sites. The covariance matrices and the correlation functions of the surface currents are exponential in space rather than Gaussian, as is often assumed in the objective mapping of oceanographic data sets. Patterns in the decorrelation length scale provide the variabilities of surface currents and the insights on the influence of topographic features (bathymetry and headlands). The objective mapping approach presented herein lends itself to various applications, including the Lagrangian transport estimates, dynamic analysis through divergence and vorticity of current vectors, and statistical models of surface currents.