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
Article
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)

Abstract

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.

Keywords

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

References

  1. Alvarez A, Mourre B (2014) Cooperation or coordination of underwater glider networks? An assessment from Observing System Simulation Experiments in the Ligurian Sea. J Atmos Oceanic Tech 31:2268–2277. doi:10.1175/JTECH-D-13-00214.1 CrossRefGoogle Scholar
  2. Auclair F, Marsaleix P, de Mey P (2003) Space-time structure and dynamics of the forecast error in a coastal circulation model in the Gulf of Lions. Dyn Atmos Oceans 36(4):309–346CrossRefGoogle Scholar
  3. Bennett AF (1985) Array design by inverse methods. Prog Oceanogr 15:129–151CrossRefGoogle Scholar
  4. Bennett AF (1990) Inverse methods for assessing ship-of-opportunity networks and estimating circulation and winds from tropical expendable bathythermograph data. J Geophys Res 95:16111–16148CrossRefGoogle Scholar
  5. Buizza R (2006) The ECMWF ensemble prediction system. In: Predictability of weather and climate, 17. Cambridge University Press, p. 459–488Google Scholar
  6. Charria G, Lazure P, Le Cann B, Serpette A, Reverdin G, Louazel S, Batifoulier F, Dumas F, Pichon A, Morel Y (2013) Surface layer circulation derived from Lagrangian drifters in the Bay of Biscay. J Mar Syst. 109–110:S60-S76. ISSN 0924-7963. 10.1016/j.jmarsys.2011.09.015
  7. Charria G, Repecaud M, Quemener L, Ménesguen A, Rimmelin-Maury P, L’Helguen S, Beaumont L, Jolivet A, Morin P, Macé E, Lazure P, Le Gendre R, Jacqueline F, Verney R, Marié L, Jegou P, Le Reste S, André X, Dutreuil V, Regnault JP, Jestin H, Lintanf H, Pichavant P, Retho M, Allenou JA, Stanisière JY, Bonnat A, Nonnotte L, Duros W, Tarot S, Carval T, Le Hir P, Dumas F, Vandermeirsch F, Lecornu F (2014) PREVIMER: a contribution to in situ coastal observing systems. Q Newslet MERCATOR Ocean 49Google Scholar
  8. Charria G, Lamouroux J, De Mey P (2015) Optimizing observation networks using gliders, moored buoys and FerryBox in the Bay of Biscay and English Channel. Submitted to Journal of Marine SystemsGoogle Scholar
  9. Cuypers Y, Bouruet Aubertot P, Lazure P, Lourenço A, Lunven M, Sourisseau M, Velo-Suarez L (2011) Non linear internal tides, turbulent mixing in the continental shelf of South Brittany. EPIGRAM Annual Meeting. Ile de RéGoogle Scholar
  10. Duhaut T, Honnorat M, Debreu L (2008) Développements numériques pour le modèle MARS. Rapport PREVIMER contrat N06/2 210 290Google Scholar
  11. EUMETSAT (2006) Atlantic sea surface temperature product manual. http://www.osi-saf.org/biblio/docs/ss1_pmatlsst_1_6.pdf
  12. Ferrer L, Fontán A, Mader J, Chust G, González M, Valencia V, Ad U, Collins MB (2009) Low-salinity plumes in the oceanic region of the Basque Country. Cont Shelf Res 29(8):970–984CrossRefGoogle Scholar
  13. Friedrichs MAM (2001) A data assimilative marine ecosystem model of the central equatorial Pacific: numerical twin experiments. J Mar Res 59:859–894CrossRefGoogle Scholar
  14. Halliwell GR, Srinivasan A, Kourafalou V, Yang H, Willey D, Le Hénaff M, Atlas R (2014) Rigorous evaluation of a fraternal twin ocean OSSE system in the open Gulf of Mexico. J Atmos Ocean Technol 31(1):105–130. doi:10.1175/JTECH-D-13-00011.1 CrossRefGoogle Scholar
  15. Heyraud C (2011) Assimilation de Données dans les modèles de façade. Ensemble de Prévisions. Années 2006, 2008. Report 11.20. Actimar.Google Scholar
  16. Huret M, Gohin F, Delmas D, Lunven M, Garçon V (2007) Use of SeaWIFS data for light availability and parameter estimation of a phytoplankton production model of the Bay of Biscay. J Mar Syst 65:509–531CrossRefGoogle Scholar
  17. Ide K, Courtier P, Ghil M, Lorenc A (1997) Unified notations for data assimilation: operational, sequential and variational. J Meteorol Soc Jpn 75(1B):181–189Google Scholar
  18. Isemer HJ, Hasse L (1985) The Bunker climate atlas of the North Atlantic Ocean, vol 2. Springer, Berlin, pp 218–252CrossRefGoogle Scholar
  19. Lazure P, Dumas F (2008) An external-internal mode coupling for a 3D hydrodynamical model for applications at regional scales (MARS). Adv Water Resour 31(2):233–250. doi:10.1016/j.advwatres.2007.06.010 CrossRefGoogle Scholar
  20. Lazure P, Jegou AM (1998) 3D modelling of seasonal evolution of Loire and Gironde plumes on Biscay Bay continental shelf. Oceanol Acta 21(2):165–177CrossRefGoogle Scholar
  21. Lazure P, Jegou AM, Kerdreux M (2006) Analysis of salinity measurements near islands on the French continental shelf of the Bay of Biscay. Sci Mar 70(S1):7–14CrossRefGoogle Scholar
  22. Le Boyer A, Cambon G, Daniault N, Herbette S, Le Cann B, Marié L, Morin P (2009) Observations of the Ushant tidal front in September 2007. Cont Shelf Res 29(8):1026–1037. doi:10.1016/j.csr.2008.12.020 CrossRefGoogle Scholar
  23. Le Hénaff M, De Mey P, Marsaleix P (2009) Assessment of observational networks with the representer matrix spectra method—application to a 3d coastal model of the Bay of Biscay. Ocean Dyn 59:3–20. doi:10.1007/s10236-008-0144-7 CrossRefGoogle Scholar
  24. Lea DJ, Martin MJ, Oke PR (2014) Demonstrating the complementarity of observations in an operational ocean forecasting system. Q J Roy Meteorol Soc 140:2037–2049. doi:10.1002/qj.2281 CrossRefGoogle Scholar
  25. Leblond E, Lazure P, Laurans M, Rioual C, Woerther P, Quemener L, Berthou P (2010) RECOPESCA: a new example of participative approach to collect in-situ environmental and fisheries data. Joint Coriolis–Mercator Ocean Quarterly Newsletter 37Google Scholar
  26. Mourre B, Ballabrera-Poy J (2009) Salinity model errors induced by wind stress uncertainties in the Macaronesian region. Ocean Model 29(3):213–221. ISSN 1463–5003. 10.1016/j.ocemod.2009.05.002
  27. Mourre B, De Mey P, Lyard F, Le Provost C (2004) Assimilation of sea level data over continental shelves: an ensemble method for the exploration of model errors due to uncertainties in bathymetry. Dyn Atmos Oceans 38:93–121. doi:10.1016/j.dynatmoce.2004.09.001 CrossRefGoogle Scholar
  28. Pairaud IL, Lyard F, Auclair F, Letellier T, Marsaleix P (2008) Dynamics of the semi-diurnal and quarter-diurnal internal tides in the Bay of Biscay. Part 1: barotropic tides. Cont Shelf Res 28(10):1294–1315CrossRefGoogle Scholar
  29. Pairaud IL, Auclair F, Marsaleix P, Lyard F, Pichon A (2010) Dynamics of the semi-diurnal and quarter-diurnal internal tides in the Bay of Biscay. Part 2: baroclinic tides. Cont Shelf Res 30(3):253–269CrossRefGoogle Scholar
  30. Pichon A, Correard S (2006) Internal tides modelling in the Bay of Biscay. Comparisons with observations. Sci Mar 70(S1):65–88CrossRefGoogle Scholar
  31. Pingree RD, Le Cann B (1989) Celtic and Armorican slope and shelf residual currents. Progr Oceanogr 23(4):303–338CrossRefGoogle Scholar
  32. Pingree RD, Le Cann B (1990) Structure, strength and seasonality of the slope currents in the Bay of Biscay region. J Mar Biol Assoc UK 70:857–885CrossRefGoogle Scholar
  33. Quattrocchi G, De Mey P, Ayoub N, Vervatis V D, Testut C E, Reffray G, Chanut J, Drillet Y (2014) Characterisation of errors of a regional model of the Bay of Biscay in response to wind uncertainties: a first step toward a data assimilation system suitable for coastal sea domains. J Oper Oceanogr 7(2)Google Scholar
  34. Sakov P, Evensen G, Bertino L (2010) Asynchronous data assimilation with the EnKF. Tellus A 62(1):24–29. doi:10.1111/j.1600-0870.2009.00417.x CrossRefGoogle Scholar
  35. Xie XH, Cuypers Y, Bouruet-Aubertot P, Ferron B, Pichon A, Lourenço A, Cortes N (2013) Large-amplitude internal tides, solitary waves, and turbulence in the central Bay of Biscay. Geophys Res Lett 40:2748–2754. doi:10.1002/grl.50533 CrossRefGoogle Scholar

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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