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A glider network design study for a synoptic view of the oceanic mesoscale variability
L'Heveder, B.; Mortier, L.; Testor, P.; Lekien, F. (2013). A glider network design study for a synoptic view of the oceanic mesoscale variability. J. Atmos. Oceanic. Technol. 30(7): 1472-1493. dx.doi.org/10.1175/JTECH-D-12-00053.1
In: Journal of Atmospheric and Oceanic Technology. American Meteorological Society: Boston, MA. ISSN 0739-0572; e-ISSN 1520-0426, more
Peer reviewed article  
Available in  Authors 
Author keywords
    Synoptic climatology; Mesoscale systems; In situ atmosphericobservations; Sampling; Statistics
Authors  Top 
  • L'Heveder, B.
  • Mortier, L.
  • Testor, P.
  • Lekien, F., more
Abstract
    This study presents an Observing System Simulation Experiment (OSSE) with a network of gliders in a realistic mesoscale field of eddies and filaments. The main objective is to demonstrate that the analysis skill evaluation, performed with different statistics, determines the optimal number of gliders needed to survey a glider observatory with a given simple topology of the glider array, in the shape of a double comb. Metrics, based on a spatial interpolation of the sampled data with a multiscale objective analysis method, are elaborated to evaluate the reconstruction of the three-dimensional temperature field with several glider networks, at a weekly time scale. The mesoscale structures obtained by the optimal network (front, eddies, eddies detachment) are also compared with the structures of the original simulation. This comparison demonstrates the efficiency of a glider fleet to sample a well-defined area at a given spatiotemporal scale. In this particular situation (midlatitude region, domain of 400 km x 600 km, reconstruction of weekly snapshots), the optimum network is composed of 10 gliders. A relationship is highlighted between the spatial scales of the sampled area, the physical characteristics of the studied region, the reconstruction time scale, and the optimum number of gliders. The results presented here can be applied to design an actual in situ experiment.
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