Assimilation of surface velocity data into a primitive equation coastal ocean model

Abstract

[1] A data assimilation system (DAS) of the wind-driven, mesoscale shelf circulation off the Oregon coast is developed. The DAS assimilates low-pass filtered surface velocity measurements, obtained from land-based high-frequency coastal radar arrays, into a primitive equation coastal ocean model using a sequential optimal interpolation scheme. Inhomogeneous and anisotropic estimates of the forecast error covariances required for the assimilation are assumed to be proportional to typical cross-correlations between modeled variables. These correlations are estimated from an ensemble of model simulations for 18 different summers. Similarly, the observation error covariances are assumed to be proportional to the actual covariances of the observations. A timedistributed averaging procedure (TDAP) that effectively low-pass filters the model forecast for comparison with the observations and introduces the corrections to the model state gradually over time is used in order to overcome problems of data compatibility and initialization. The correlations between direct subsurface current measurements and subsurface currents obtained from model-only and assimilation experiments for the summer of 1998 are 0.42 and 0.78, respectively, demonstrating the effectiveness of the DAS. Our estimates of the error covariances are shown to be appropriate through a series of objective statistical tests. Analysis of the term balances of the model equations show that the dominant modeled dynamical balances are preserved by the DAS and that uncertainties in the spatial variability of the wind forcing are likely to be one source of model error. By varying the relative magnitudes of the estimated forecast and observation error covariances the DAS is shown to be most effective when approximately 80% of the analysis is made up of the model solution.

Remote sensing of ocean surface currents: a review of what is being observed and what is being assimilated

“…Implicit implementation of localization is obtained by truncating the eigenvalue expansion of the term H P f H + R in Eq. (52) (Oke et al, 2002).…”

“…Since then, and driven by the continuous expansion of the network of HF radar systems, different data assimilation approaches have been used to assimilate HF radar currents into nonlinear, high-resolution ocean models: nudging (Lewis et al, 1998;Wilkin et al, 2005;Gopalakrishnan and Blumberg, 2012), sequential assimilation (Breivik and Saetra, 2001;Oke et al, 2002;Paduan and Shulman, 2004;Kurapov et al, 2005a;Oke et al, 2009) and 4DVAR assimilation schemes (Hoteit et al, 2009;Zhang et al, 2010;Yu et al, 2012).…”

An eddy resolving tidal-driven model of the South China Sea assimilating along-track SLA data using the EnOI

“…In this study, along-track altimetry data are assimilated using the Ensemble Optimal Interpolation (EnOI; Oke et al, 2002;Evensen, 2003) into a nested high-resolution configuration of the HYbrid Coordinate Ocean Model (HYCOM, Bleck, 2002) that includes tides. The EnOI is a multivariate data assimilation method derived from the Ensemble Kalman Filter.…”

Coastal observing and forecasting system for the German Bight – estimates of hydrophysical states

“…demanding problems. The assimilation techniques proposed in literature include optimal interpolation (e.g., Breivik and Saetra, 2001), more sophisticated statistical approaches (e.g., Oke et al, 2002;Paduan and Shulman, 2004;Barth et al, 2008Barth et al, , 2010, variational techniques (e.g., Sentchev et al, 2006;Yaremchuk and Sentchev, 2009;Scott et al, 2000;Kurapov et al, 2003) and adhoc methods (e.g., Lewis et al, 1998).…”