CTD data processing

The dataset comprises profiles of temperature (°C) and practical salinity (psu) as a function of pressure (dbar). Each profile is located in space and time. It must be emphasized that the dataset of each individual CTD-SRDL has been edited and corrected separately, as a given CTD-SRDL has its own specificities in terms of data accuracy and quality of the estimated correction.

The CTD data distributed via the MEOP portal has an accuracy level that can vary depending on the considered CTD-SRDL, based on the technology or the water mass types. Overall, once adjusted and validated, a minimal data accuracy of 0.03°C and 0.05 PSU can be assumed. 

A post-processing procedure is applied on hydrographic data in order to ensure the best possible data quality (see Roquet et al., 2011). This includes :

  • tag-by-tag data visualization and editing of profiles (semi-automatic procedure)
  • replacement of low-resolution satellite-transmitted datasets by high-resolution recovered datasets whenever possible, i.e. when the tag has been recovered on the field after the deployment period.
  • adjustment of salinity profile data. These corrections are determined based on comparisons with historical CTD and Argo data, or by cross-comparison between different tag datasets. Adjustments applied on salinity data are especially critical to achieve a level of accuracy suitable for oceanographic and climatic studies.
  • adjustment of temperature profile data, when possible. In practice, it is only possible to adjust temperature data in the case where seals foraged in freezing cold waters, allowing to use the known freezing temperature as a reference.
  • A thermal cell effect correction has been applied on the entire database. Details of the method can be found in the following submitted manuscript:
    Mensah, V., Roquet, F., Picard, B., Pauthenet, E., Guinet, C., 2017.  A correction methodology for the thermal mass induced-errors of CTD tags mounted on marine mammals. In review in the Journal of Atmospheric and Oceanic Technologies. [PDF]
  • A density inversion removal algorithm is also applied, which seeks the minimum adjustment on the salinity profile to achieve neutral stability. The method is described in: Barker, P. M. and McDougall, T. J., 2017. Stabilizing Hydrographic Profiles with Minimal Change to the Water Masses. Journal of Atmospheric and Oceanic Technology, 34:1935-1945. doi: 10.1175/JTECH-D-16-0111.1
  • quality-control of the adjusted data set, using statistical metrics (see below).
  • storage and distribution of data in a standardized netCDF format


Below we consider the dynamic height anomaly at 20 dbar relative to 500 dbar (here denoted as DH500) to validate the seal database from the Southern Indian Ocean sector, comparing its spatial distribution based on raw and adjusted seal data with the distribution obtained using hydrographic profiles present in the World Ocean Database (WOD) for the period 2000 to 2013 (from Roquet et al. 2014). The surface dynamic height anomaly relative to a given reference pressure level is obtained by vertically integrating inverse density anomalies. It is a key quantity in physical oceanography because its horizontal gradient is directly proportional to the large-scale (geostrophic) currents. It is also a useful quantity to validate seal data, because it provides a single scalar quantity for each profile that depends on both temperature and salinity profiling data.

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