On the use of Satellite Altimeter data in Argo quality control

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Sydney Anderson
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1 On the use of Satellite Altimeter data in Argo quality control Stéphanie Guinehut CLS, Space Oceanography Division Argo-France 14-15/05/2009 Brest -1- -

2 Objective Background idea: to check the data before the DMQC flag the data more quickly as soon as a malfunction is detected to get a cleaner data set in real-time global consistency check As, Sea Level Anomalies (SLA) from altimeter measurements and Dynamic Height Anomalies (DHA) calculated from in-situ T and S profiles are complementary but also strongly correlated Satellite altimeter measurements are used to check the quality of the Argo profiling floats time series Altimeter measurements represent the mesoscale and the interannual variability The main idea is to compare co-located SLA and DHA to detect systematic or punctual Analysis are performed for each float time series S. Guinehut, C. Coatanoan, A.-L. Dhomps, P.-Y. Le Traon and G. Larnicol, 2009: On the use of -2-

3 Data and Method The main idea is to compare co-located : Altimeter Sea Level Anomalies (SLA) and Dynamic Height Anomalies (DHA) from Argo T/S profiles for each Argo float time series Method : DHA = DH Mean-DH / SLA 2 times series co-located in time and space SLA : AVISO combined maps DHA : Argo Coriolis-GDAC data base DH calculated from T/S profiles using a reference level at 900-m depth only data with : POSITION_QC = 0, 1, 5, 8 JULD_QC = 0, 1, 5, 8 PRES/TEMP/PSAL_QC = 1 (DATA_MODE= R ) PRES_ADJ/TEMP_ADJ/PSAL_ADJ_QC = 1 (DATA_MODE= A / D ) Mean-DH : Argo climatology -3-

4 Data and Method Very good consistencies between the two time series -4- Impact of the delayed-mode and real-time adjustment

5 Method Method : DHA = DH Mean-DH / SLA Differences between DHA and SLA can arises from : Differences in the physical content of the two data sets Problems in SLA (assumed to be perfect for the study) Problems in the Mean-DH / Inconsistencies between Mean-DH and DH Problems in DH (i.e. the Argo data set) In order to minimize the problems in the Mean-DH, when have used a 2-steps approach : 1st : Mean-DH = Levitus annual climatology, comparisons, questionable Argo floats separated 2st : calculation of an Argo Mean-DH consistent with the Argo period comparisons on the all data sets In order to take into account the differences in the physical content of the two data sets, mean representative statistics of these differences have first been computed -5-

6 The Argo Mean dynamic height Correction to the Levitus Mean Dynamic Height General statistics : ( observations*) Levitus Argo Correlation Mean-diff Rms-diff (% Rms-SLA) *questionable Argo floats separated -6-

7 Impact of the Argo Mean dynamic height Levitus mean dynamic height offset of 5.7 cm due to the mean bad float -7-

8 Impact of the Argo Mean dynamic height Argo mean dynamic height offset reduced good float!!! The Mean-DH has a very important impact for bias identification!!! -8-

9 Mean representative statistics Computed using the same data set questionable floats separated Correlation coefficient (DHA/SLA) : Rms of the differences (SLA-DHA) as % of SLA variance : % -9-

10 Global results One point represents a time series at its mean position (~ 4100 floats) Correlation coefficient (DHA/SLA) : Î Questionable 1.0 floats can already be extracted by comparing to the neighbours Rms of the differences (SLA-DHA) as % of SLA variance : %

11 Global results Comparisons with the mean representative statistics : %

12 Global results Comparisons with the mean representative statistics extraction of 111 anomalous floats % -12-

13 Global results ftp://ftp.ifremer.fr/ifremer/argo/etc/argo-ast9-item13-altimetercomparison List of floats to be checked : DAC WMO INST-TYPE TYPE OF ANOM kma spikes meds offset meds offset meds offset incois offset coriolis spike coriolis ? coriolis offset coriolis drift bodc spike bodc spikes. The AIC monthly report for April Dacs should check the suspicious floats they are managing together with their delayed mode operators and Pis and provide appropriate corrections if needed provide feedback on the method -13-

14 The majority of floats! Global results very good consistency Float : r : 0.96 rms-diff : % mean-diff : cm samples :

15 Global results very good consistency Float : r : 0.91 rms-diff : % mean-diff : cm samples :

16 Global results very good consistency Float : r : 0.94 rms-diff : 6.53 % mean-diff : 1.20 cm samples :

17 Global results representative anomalies Spike PSAL_ADJUSTED = 0.0 Float : r : 0.83 rms-diff : % mean-diff : cm samples : 95 Delayed-mode values to be qualified GUI might help -17-

18 Global results representative anomalies Problem in the Adjusted time series : Delayed-mode value : S-offset = Real-time adjusted value : S-offset = Float : r : 0.45 rms-diff : % mean-diff : 0.20 cm samples : 147 Adjusted values in real-time to be qualified Float now corrected -18-

19 Global results representative anomalies Systematic bias of 13 cm: Float : r : 0.57 rms-diff : % mean-diff : cm samples :

20 Global results representative anomalies Progressive drift - SOLO-FSI : Float : r : -0.1 rms-diff : % mean-diff : cm samples : 167 Flotteur corrigé?!?!? -20-

21 Global results representative anomalies Pressure sensor oil leak problem grey list Float : r : 0.77 rms-diff : % mean-diff : 4.3 cm samples :

22 Global results representative anomalies Pressure sensor oil leak problem grey list Courtesy of Annie Wong -22-

23 Global results representative anomalies Pressure sensor oil leak problem grey list Courtesy of Annie Wong -23-

24 Global results representative anomalies Pressure sensor oil leak problem grey list Quel signal en pression la méthode est-elle capable de détecter??? A approfondir -24-

25 Update of the results Every three months Floats have been corrected New floats have been detected -25-

26 Feedbacks from DM operators Only feedbacks from Argo SIO program some floats have been corrected some floats have been detected by the Altimetry QC method but data are considered good ex. of the limitation of the method Offset of -8.9 cm for 11 measurements «High» differences because of the Mean-DH? frontal zone?? Next cycles not analyzed because shallower than 900-m method to be adapted to other depth -26-

27 Conclusions and Perspectives Outil supplémentaire à disposition des PI Errors mainly detected in the real-time data set big big errors Only a few isolated example for adjusted values qualification needed Only a few isolated example for delayed-mode files qualification needed The method is efficient for big big errors (spikes, offset, drift) but only says that one of The method is complementary to the real-time and delayed-mode QC What the method is not able to do : Extract small errors in high variability regions And very small bias (~2-3 cm) in lower variability regions Future plans : What kind of signals (in term of T/S/P) the method is able to detect?? Might depend on the area Method to be adapted to the mean max depth of each float -27-

North-Atlantic Regional Data Center. Virginie Thierry E. Autret, F. Gaillard, Y.Gouriou, S. Pouliquen

North-Atlantic Regional Data Center Virginie Thierry E. Autret, F. Gaillard, Y.Gouriou, S. Pouliquen Overview Introduction: A North-Atlantic DAC Part I - Analysis of T and S at Coriolis: a contribution