Quality Control of Large Argo Datasets

Fabienne Gaillard IFREMER, LPO (CNRS-Ifremer-IRD-UBO), Plouzané, France

Search for other papers by Fabienne Gaillard in
Current site
Google Scholar
PubMed
Close
,
Emmanuelle Autret IFREMER, LPO (CNRS-Ifremer-IRD-UBO), Plouzané, France

Search for other papers by Emmanuelle Autret in
Current site
Google Scholar
PubMed
Close
,
Virginie Thierry IFREMER, LPO (CNRS-Ifremer-IRD-UBO), Plouzané, France

Search for other papers by Virginie Thierry in
Current site
Google Scholar
PubMed
Close
,
Philippe Galaup IFREMER, LPO (CNRS-Ifremer-IRD-UBO), Plouzané, France

Search for other papers by Philippe Galaup in
Current site
Google Scholar
PubMed
Close
,
Christine Coatanoan IFREMER, IDM, Plouzané, France

Search for other papers by Christine Coatanoan in
Current site
Google Scholar
PubMed
Close
, and
Thomas Loubrieu IFREMER, IDM, Plouzané, France

Search for other papers by Thomas Loubrieu in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Argo floats have significantly improved the observation of the global ocean interior, but as the size of the database increases, so does the need for efficient tools to perform reliable quality control. It is shown here how the classical method of optimal analysis can be used to validate very large datasets before operational or scientific use. The analysis system employed is the one implemented at the Coriolis data center to produce the weekly fields of temperature and salinity, and the key data are the analysis residuals. The impacts of the various sensor errors are evaluated and twin experiments are performed to measure the system capacity in identifying these errors. It appears that for a typical data distribution, the analysis residuals extract 2/3 of the sensor error after a single analysis. The method has been applied on the full Argo Atlantic real-time dataset for the 2000–04 period (482 floats) and 15% of the floats were detected as having salinity drifts or offset. A second test was performed on the delayed mode dataset (120 floats) to check the overall consistency, and except for a few isolated anomalous profiles, the corrected datasets were found to be globally good. The last experiment performed on the Coriolis real-time products takes into account the recently discovered problem in the pressure labeling. For this experiment, a sample of 36 floats, mixing well-behaved and anomalous instruments of the 2003–06 period, was considered and the simple test designed to detect the most common systematic anomalies successfully identified the deficient floats.

Corresponding author address: Fabienne Gaillard, Laboratoire de physique des océans, IFREMER, BP 70, 29280 Plouzané, France. Email: fabienne.gaillard@ifremer.fr

Abstract

Argo floats have significantly improved the observation of the global ocean interior, but as the size of the database increases, so does the need for efficient tools to perform reliable quality control. It is shown here how the classical method of optimal analysis can be used to validate very large datasets before operational or scientific use. The analysis system employed is the one implemented at the Coriolis data center to produce the weekly fields of temperature and salinity, and the key data are the analysis residuals. The impacts of the various sensor errors are evaluated and twin experiments are performed to measure the system capacity in identifying these errors. It appears that for a typical data distribution, the analysis residuals extract 2/3 of the sensor error after a single analysis. The method has been applied on the full Argo Atlantic real-time dataset for the 2000–04 period (482 floats) and 15% of the floats were detected as having salinity drifts or offset. A second test was performed on the delayed mode dataset (120 floats) to check the overall consistency, and except for a few isolated anomalous profiles, the corrected datasets were found to be globally good. The last experiment performed on the Coriolis real-time products takes into account the recently discovered problem in the pressure labeling. For this experiment, a sample of 36 floats, mixing well-behaved and anomalous instruments of the 2003–06 period, was considered and the simple test designed to detect the most common systematic anomalies successfully identified the deficient floats.

Corresponding author address: Fabienne Gaillard, Laboratoire de physique des océans, IFREMER, BP 70, 29280 Plouzané, France. Email: fabienne.gaillard@ifremer.fr

Save
  • Antonov, J., Levitus S. , Boyer T. P. , Conkright M. , O’Brien T. , and Stephens C. , 1998: Temperature of the Atlantic Ocean. Vol. 1, World Ocean Atlas 1998, NOAA Atlas NESDIS 27, 166 pp.

    • Search Google Scholar
    • Export Citation
  • Argo Data Management, 2005: Argo quality control manual V2.1. 28 pp. [Available online at http://www.coriolis.eu.org/cdc/argo/argo-quality-control-manual.pdf.].

    • Search Google Scholar
    • Export Citation
  • Bendat, J. S., and Piersol A. G. , 2000: Random Data: Analysis and Measurement Procedures. 3rd ed. Wiley, 594 pp.

  • Boehme, L., and Send U. , 2005: Objective analyses of hydrographic data for referencing profiling float salinities in highly variable environments. Deep-Sea Res. II, 52 , 651664.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boyer, T. P., Levitus S. , Antonov J. , Conkright M. , O’Brien T. , and Stephens C. , 1998: Salinity of the Atlantic Ocean. Vol. 4, World Ocean Atlas 1998, NOAA Atlas NESDIS 30, 166 pp.

    • Search Google Scholar
    • Export Citation
  • Bretherton, F., Davis R. , and Fandry C. , 1976: A technique for objective analysis and design of oceanic experiments applied to Mode-73. Deep-Sea Res., 23 , 559582.

    • Search Google Scholar
    • Export Citation
  • Dickson, B., Yashayaev I. , Meincke J. , Turrell B. , Dye S. , and Holfort J. , 2002: Rapid freshening of the deep North Atlantic Ocean over the past four decades. Nature, 416 , 832837.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ide, K., Courtier P. , Ghil M. , and Lorenc A. C. , 1997: Unified notation for data assimilation: Operational, sequential, and variational. J. Meteor. Soc. Japan, 75 , 181189.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnson, G. C., Toole J. M. , and Larson N. G. , 2007: Sensor corrections for Sea-Bird SBE-41CP and SBE-41 CTDs. J. Atmos. Oceanic Technol., 24 , 11171130.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kaplan, A., Kushnir Y. , Cane M. A. , and Blumenthal M. B. , 1997: Reduced space optimal analysis for historical data sets: 136 years of Atlantic sea surface temperatures. J. Geophys. Res., 102 , (C13). 2783527860.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lherminier, P., Mercier H. , Gourcuff C. , Alvarez M. , Bacon S. , and Kermabon C. , 2007: Transports across the 2002 Greenland-Portugal Ovide section and comparison with 1997. J. Geophys. Res., 112 , C07003. doi:10.1029/2006JC003716.

    • Search Google Scholar
    • Export Citation
  • Liebelt, P. B., 1967: An Introduction to Optimal Estimation. Addison-Wesley, 267 pp.

  • Lozier, M. S., McCartney M. S. , and Owens W. B. , 1994: Anomalous anomalies in averaged hydrographic data. J. Phys. Oceanogr., 24 , 26242638.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reynaud, T., Legrand P. , Mercier H. , and Barnier B. , 1998: A new analysis of hydrographic data in the Atlantic and its application to inverse modeling. International WOCE Newsletter, No. 32, WOCE International Project Office, Southampton, United Kingdom, 29–31.

    • Search Google Scholar
    • Export Citation
  • Schiermeier, Q., 2007: Artefacts in ocean data hide rising temperatures. Nature, 447 , 89. doi:10.1038/447008a.

  • WMO, 2004: Manual on the global telecommunication system. Vol. I. WMO 386. World Meteorological Organization, 483 pp. [Available online at http://www.wmo.int/pages/prog/www/ois/Operational_Information/WMO386/ManualOnTheGTS.pdf.].

    • Search Google Scholar
    • Export Citation
  • Wong, A. P. S., Johnson G. C. , and Owens W. B. , 2003: Delayed-mode calibration of autonomous CTD profiling float salinity data by θS climatology. J. Atmos. Oceanic Technol., 20 , 308318.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 681 200 13
PDF Downloads 428 107 6