Ocean Dynamics

, Volume 66, Issue 4, pp 567–588 | Cite as

Objective assessment of the contribution of the RECOPESCA network to the monitoring of 3D coastal ocean variables in the Bay of Biscay and the English Channel

  • Julien Lamouroux
  • Guillaume Charria
  • Pierre De Mey
  • Stéphane Raynaud
  • Catherine Heyraud
  • Philippe Craneguy
  • Franck Dumas
  • Matthieu Le Hénaff
Part of the following topical collections:
  1. Topical Collection on Coastal Ocean Forecasting Science supported by the GODAE OceanView Coastal Oceans and Shelf Seas Task Team (COSS-TT)


In the Bay of Biscay and the English Channel, in situ observations represent a key element to monitor and to understand the wide range of processes in the coastal ocean and their direct impacts on human activities. An efficient way to measure the hydrological content of the water column over the main part of the continental shelf is to consider ships of opportunity as the surface to cover is wide and could be far from the coast. In the French observation strategy, the RECOPESCA programme, as a component of the High frequency Observation network for the environment in coastal SEAs (HOSEA), aims to collect environmental observations from sensors attached to fishing nets. In the present study, we assess that network using the Array Modes (ArM) method (a stochastic implementation of Le Hénaff et al. Ocean Dyn 59: 3–20. doi:  10.1007/s10236-008-0144-7, 2009). That model ensemble-based method is used here to compare model and observation errors and to quantitatively evaluate the performance of the observation network at detecting prior (model) uncertainties, based on hypotheses on error sources. A reference network, based on fishing vessel observations in 2008, is assessed using that method. Considering the various seasons, we show the efficiency of the network at detecting the main model uncertainties. Moreover, three scenarios, based on the reference network, a denser network in 2010 and a fictive network aggregated from a pluri-annual collection of profiles, are also analysed. Our sensitivity study shows the importance of the profile positions with respect to the sheer number of profiles for ensuring the ability of the network to describe the main error modes. More generally, we demonstrate the capacity of this method, with a low computational cost, to assess and to design new in situ observation networks.


Design of in situ observation network Bay of Biscay English Channel Performance assessment Model errors 



This study has been undertaken in the frame and with the financial support of the PREVIMER project, the FP7 JERICO (WP9) project, and the FP7 SANGOMA project (FP7-SPACE-2011-283580). M. Le Hénaff received partial support for this work from the base funds of the NOAA Atlantic Oceanographic and Meteorological Laboratory. It has also been conducted as a contribution to the GODAE OceanView Coastal Ocean and Shelf Seas Task Team (COSS-TT). We would like to thank Emilie Leblond and Patrick Berthou for leading the RECOPESCA programme, Loic Quemener and Michel Repecaud for operating the network, and the voluntary fishermen who have accepted to join the RECOPESCA network. We also thank the technical team of RECOPESCA, especially Matthieu Bourbigot, and the observers of the Ifremer Fisheries Information System. The data from the EUMETSAT Satellite Application Facility on Ocean and Sea Ice is accessible through the SAF’s homepage: We are grateful to the two anonymous referees and the Associate Editor Emil Vassilev Stanev for their very fruitful and constructive comments on this manuscript.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Julien Lamouroux
    • 1
    • 2
  • Guillaume Charria
    • 3
  • Pierre De Mey
    • 4
  • Stéphane Raynaud
    • 5
  • Catherine Heyraud
    • 5
  • Philippe Craneguy
    • 5
  • Franck Dumas
    • 6
  • Matthieu Le Hénaff
    • 7
    • 8
  1. 1.NOVELTISLabègeFrance
  2. 2.Mercator-OcéanRamonville Saint-AgneFrance
  3. 3.Ifremer, Univ. Brest, CNRS, IRDLaboratoire d’Océanographie Physique et Spatiale (LOPS), IUEMBrestFrance
  4. 4.CNRS, LEGOS/UMR 5566ToulouseFrance
  5. 5.ACTIMARBrestFrance
  6. 6.SHOMHOMBrestFrance
  7. 7.University of Miami/Cooperative Institute for Marine and Atmospheric Studies (CIMAS)MiamiUSA
  8. 8.NOAA Atlantic Oceanographic and Meteorological Laboratory (AOML)MiamiUSA

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