Satellite Altimeter Data for Argo QC

On the use of Satellite Altimeter Data in Argo Quality Control *

by S.Guinehut, A.-L.Dhomps, G.Larnicol (1), C.Coatanoan, P.-Y.Le Taon(2)  

* Corresponding author : Stéphanie Guinehut

(1) : CLS Space Oceanography Division, Toulouse France(2) : IFREMER, Brest France

As part of climate variability and global sea level rise studies, Argo data are of great interest. Combined with in-situ and altimetric measurements, they were used in some studies to quantify the causes of global sea level rise-ocean thermal expansion, glacier and ice cap/sheet melting, and snowpack reduction (Lombard et al.2007, Willis et al., 2008). But those diagnosis need very high-qualified data since signals are very low amplitude and are highly sensitive to biases or errors present in the datasets. The best Argo quality data for climate research are only available in delayed-mode.

This study proposes a complementary approach to the delayed-mode control of Argo profiles, based on the use of satellite altimeter measurements to check the quality of the Argo profiling floats time series. Indeed, Sea Level Anomalies (SLA) from altimeter measurements and Dynamic Height Anomalies (DHA) calculated from in-situ T and S profiles are complementary and strongly correlated (Gilson et al., 1998 ; Mc Carthy et al., 2000 ; Guinehut et al, 2006 ; Dhomps et al.2011 ). By exploiting this correlation along with mean representative statistical differences between the two datasets, it is thus possible to extract random or systematic errors in Argo float time series (Guinehut et al., 2009).

Data and Method

The main idea is to compare co-located Altimeter Sea Level Anomalies (SLA) and Dynamic Height Anomalies (DHA) calculated from Argo T/S profiles for each Argo float time series. The method is activated in near real-time on a quaterly basis.

  • Argo profiles are from the Coriolis GDAC. Available Argo profiles for the period 2003-2007 have been uploaded from the Coriolis GDAC and the full Argo data set as of February 2008 (http://www.coriolis.eu.org). Only profiles with pressure (P), temperature (T) and salinity (S) measurements flagged « good » are used for this study. Delayed-mode fields are preferred to the real-time ones when available.
  • The altimeter data are Archiving, Validation and Interpretation of Satellite Oceanographic Data (AVISO) combined products which provide maps of SLA obtained from an optimal combination of all available satellite altimeters (AVISO 2012). The delayed-mode version of the product is used and it is completed by the real-time one up to the present. Available maps for SLA are generated every 7-days on the 1/3°x1/3° Mercator grid, relatively to 7-yr time mean (1993-1999). To be consistent with the Argo data set, they have been re-computed relatively to a 7-yr time mean (2003-2009).

Three steps are necessary to make a global comparison between SLA from atlimeter measuremants, and DHA computed from Argo floats :

  1. Dynamic heights are first calculated from Argo P/T/S profiles, using a reference level at 200,400,900,1200 or 1900-m-depth.
  2. To calculate DHA consistent with altimeter SLA, a contemporaneous Argo climatology, used to compute a Mean Dynamic Height (mean-DHA), is built from the same dataset but discarding « questionable » floats.
  3. SLA maps are then interpolated to the time and location of each in-situ DHA measurement using a linear space/time interpolation.

General statistics between the two datasets (correlation coefficients, rms of the differences) are thus generated for each Argo float times series.They are compared to the a priori « reference values » of these two fields. If they fall outside the given values, the Argo float time series is thus suspected to have a problem (drift, bias, spike..) and the time series is visually controlled.

Results

Since altimeter data include both steric and nonsteric contributions to sea level, and DHA from Argo floats represent only the steric contribution between the surface and the reference level, small differences between the two datasets are expected. These differences are due to the nonsteric contribution to sea level, and to T/S changes below the reference level. However, the first result of the global comparison is a great consistency between altimeter SLA and Argo DHA. The reference values of the correlation coefficients between the two time series (DHA/SLA) are almost everywhere greater than 0.5 and even greater than 0.7 in most parts of the ocean (figure 1). Lower values are found in the high latitudes, where nonsteric contributions to sea level are expected to be larger. The a priori rms of the differences between the two time series show values lower than 50% in most parts of the Indian, Pacific and Atlantic Oceans north of 30°S.

Figure 1 : (left) - Correlation coefficient between SLA and DHA-900m. Statistics have been calculated on a 1°x1° horizontal grid using observations available in a 2° latitude by 10° longitude radius of influence around each point. Boxes with less than 30 observations are masked ; (right) - Rms of the differences between SLA and DHA-900m as a percentage of the SLA variance. Statistics have been calculated using the same selection criteria as correlation coefficients.

The Antarctic Circumpolar region has value on the order of 50-70% or greater. The Atlantic and South Pacific Oceans show also different regions with high values (>80%) which correspond to regions of very low variability of the altimeter signal (<4-cm rms) and to regions with lower correlations coefficients (<0.6).

Each quaterly analysis shows that for most of the floats (about 90%), the rms of the differences between SLA and DHA for their time serie are on the order of the referenced numbers. From the 10% remaining, floats are visually controlled, 30 to 40 are confirmed and need to be further analyzed. The confirmed floats mainly show a systematic offset, a very important spike, or the drift of a sensor (salinity or pressure). Some examples of anomalous floats, which have been corrected, are given in figure 2.

Figure 2 : SLA and DHA time series for three floats (cm). The positions of each float are also indicated, the blue cross corresponding to the deployment position, and the red cross to its last reported position.

  • The WMO 1900581 exhibits a constant negative high bias since January 2006, on the order of 15 cm with the altimeter data. The mean dynamic height has been calculated from the Argo climatology, and so it is consistent with the Argo dataset. But we can suppose that this high bias reflects a problem in the salinity or pressure sensor of the float, which may be controlled with the delayed-mode QC procedure on salinity calibration (Wong et al., 2003).
  • The WMO 1900249 shows a progressive drift of the DHA time series regarding the SLA time series, associated with a null correlation (instead of a 0.5 expected correlation value), showing a clear malfunction of one of the sensors.
  • The WMO 3900225 float shows that part of the data have been delayed-mode controlled and corrected. But at the end of the time series, when values adjusted in real time are available, they show a constant offset of about 10 cm with the altimeter data. This offset seems to be due to the salinity offset value of 0.092 applied in real time, which is without any doubt overestimated and wrongcompared to the 0.015 value applied for the delayed mode.

Conclusion

This new method of delayed-mode control of Argo profiles from satellites measurements is activated in near real-time on a quaterly basis. It appears to be very complementary to the existing quality control checks performed in real and delayed mode. Quaterly updated results are available online on this link, with a list of floats to be checked by the PIs and delayed-mode operators.

References

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Guinehut S., C.Coatanoan, A.L. Dhomps, P.Y.Le Traon, and G.Larnicol : On the Use of Satellite Altimeter Data in Argo Quality Control, JAOT, vol.26, pp 395-402, doi:10.1175/2008JTECHO6481, 2009.

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