Objective design of coastal HF Radar networks using stochastic array modes (ArM toolbox)

In the framework of the European projects FP7 JERICO (http://www.jerico-fp7.eu/) and H2020 JERICO NEXT (http://www.jerico-ri.eu/), this study consisted in carrying out an objective design analysis of coastal HF radar networks with the ArM (Array Modes) method (Le Hénaff et al., 2009; Lamouroux et al., 2016; Charria et al., 2016). The ArM approach is a non-assimilative, data-model synergistic approach: it uses ensembles in response to known error sources to describe prior (model) uncertainties, and aims at quantitatively evaluating the performance of the observation network at detecting those uncertainties amidst observational noise.  The ArM analysis consists in calculating and interpreting spectra of the representer matrix, as well as modal representers, making it possible to visualize the model error structures which are detectable by the radar observations and, in a second step, potentially controllable through data assimilation. The performances of two existing Bay of Biscay HF radar observation sites deployed on the Spanish Basque coast as well as a projected third site on the French Landes coast were evaluated with the ArM method, separately or by combinations of two and three sites.  We considered only radial velocities from the radars.  The ensembles were composed of two sets of 50 members from two different 3D oceanic models with differing resolutions: MARS-3D (4km) and Symphonie (500m), respectively. Tests showed that adding radars improves the detection of model errors by increasing the quantity and location of observations that lead to efficient sampling of model error structures.  In particular, the third projected radar site would bring a clear improvement at sampling zonal surface velocity errors in the model, because of its location.  Using higher-resolution ensembles (approximating higher-resolution model errors) leads to similar results, qualitatively, but differences appear when examining the spatial structures of errors detected by the arrays (in the form of modal representers). Finally, we studied the impact of correlated measurement errors, e.g. via sea state which can contaminate radar-derived velocities via Stokes drift.  We found that our previous conclusions regarding the existing array performance and the positive impact of a third site were not significantly modified by such correlated noise contamination

Assessing impacts of observations on ocean circulation models with examples from coastal, shelf, and marginal seas

Ocean observing systems in coastal, shelf and marginal seas collect diverse oceanographic information supporting a wide range of socioeconomic needs, but observations are necessarily sparse in space and/or time due to practical limitations. Ocean analysis and forecast systems capitalize on such observations, producing data-constrained, four-dimensional oceanographic fields. Here we review efforts to quantify the impact of ocean observations, observing platforms, and networks of platforms on model products of the physical ocean state in coastal regions. Quantitative assessment must consider a variety of issues including observation operators that sample models, error of representativeness, and correlated uncertainty in observations. Observing System Experiments, Observing System Simulation Experiments, representer functions and array modes, observation impacts, and algorithms based on artificial intelligence all offer methods to evaluate data-based model performance improvements according to metrics that characterize oceanographic features of local interest. Applications from globally distributed coastal ocean modeling systems document broad adoption of quantitative methods, generally meaningful reductions in model-data discrepancies from observation assimilation, and support for assimilation of complementary data sets, including subsurface in situ observation platforms, across diverse coastal environments