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

Type Poster
Date 2019
Language English
Other localization https://www.godae-oceanview.org/outreach/meetings-workshops/symposia-summer-schools/gov-symposium-2019/
Author(s) Skandrani Chafih1, de Mey Pierre2, Charria GuillaumeORCID3
Affiliation(s) 1 : NOVELTIS, Toulouse, France
2 : LEGOS, Toulouse, France
3 : Ifremer, LOPS, france
Meeting OceanPredict '19 - GODAE OceanView Symposium. May, 6-10, 2019 in Halifax, Nova Scotia, Canada

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.

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Skandrani Chafih, de Mey Pierre, Charria Guillaume (2019). Objective design of coastal HF Radar networks using stochastic array modes (ArM toolbox). OceanPredict '19 - GODAE OceanView Symposium. May, 6-10, 2019 in Halifax, Nova Scotia, Canada. https://archimer.ifremer.fr/doc/00635/74726/