Quality Control of Large Argo Datasets

Abstract

International audienceArgo 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

The CORA dataset: validation and diagnostics of in-situ ocean temperature and salinity measurements

“…Indeed, different issues exist with eXpendable BathyThermograph (XBT) data and, if not corrected, can contribute to anomalous global oceanic heat content (OHC) variability (e.g. Gouretski and Koltermann, 2007;Wijffels et al, 2008;Levitus et al, 2009;Ishii and Kimoto, 2009;Gouretski and Resghetti, 2010;Lyman et al, 2010). In the WOD09 database, the XBT data at observed levels retain the depths received from the data submitter, while the XBT data interpolated to standard levels use the appropriate corrected depth equation when possible and the appropriate time-varying bias correction from Levitus et al (2009).…”

“…A statistical analysis is then performed once a day using all the data available (with quality flags 1 or 2 only), dedicated to detecting possible data problems that could have escaped the automatic tests. The statistical test is based on an objective analysis method (Bretherton et al, 1976) with a three-week window (see Gaillard et al, 2009, for further details). For each profile, a residual is calculated at each standard level and a mean is computed in each layer (0-100 m, 100-300 m, 300-700 m, 700-1000 m, 1000-1600 m, 1600-2000 m).…”

“…In 1995-1996 Sippican and TSK manufacturers implemented this new fall-rate equation. However, other sources of XBT temperature uncertainties and biases exist (Gouretski and Reseghetti, 2010;Gouretski and Koltermann, 2007). For example, it has been known since the use of these probes started that the fall rate should depend on the physical characteristics of seawater like viscosity, temperature, and density (Thadathil et al, 2002;Kizu et al, 2011).…”

Sea surface freshening inferred from SMOS and ARGO salinity: impact of rain

“…They are derived, after a thorough quality check of ARGO measurements, with the optimal interpolation method described in (Gaillard et al, 2009); they are named ARGO OI in the following. The choice for the time and space scales used in that method results from a compromise between what is known of ocean time and space scales and what can actually be resolved with the ARGO array (3 • , 10 days); two scale lengths are considered: the first one is isotropic and equal to 300 km, the second one is set equal to 4 times the average Rossby radius of the area.…”

Formation and variability of the South Pacific Sea Surface Salinity maximum in recent decades

“…As a complement, especially for studying the recent interannual changes, we also use a monthly, 1 = 2 by 1 = 2 global (77 S-66.5 N) gridded SSS product. The SSS values are then derived from an objective analysis of Argo data using the in situ analysis system (ISAS, 6th version) tool not including VOS measurements [see Gaillard et al, 2009]. This product covers the 2002-2012 years, but only data with an associated error below 80% were kept.…”