Which delayed time observation data sets for Reanalysis ??

1. Which delayed time observation data sets for Reanalysis ?? Stéphanie Guinehut Yannice Faugère Cécile Cabanes Antoine Grouazel

2. Introduction • Observations are essential components of reanalysis project • for assimilation : • for validation : • - climatologies (T/S, U/V, MLD), current meters, ADCP, transport estimates, surface drifters, tide gauges, OC, Sea Ice (drift, concentration) … • Sea level anomalies from satellite altimeter : • - 3 sources of data: Fr, EU space agencies + Navy • - main challenges = processing : to extract a SLA from the altimeter instrumental measurement + consistency during all the life time of one mission & between the different missions / MDT • In-situ T/S profiles : CORA3.2 data set • - many sources of data : many data centers, many projects, many types of instrument • - main challenge : to qualify all these individual profiles • - !! Need to detect / correct for systematic errors, instrument failures • SST from satellites • Many products exist : summary of a comparison study performed as part of MyOcean WP4 activities

3. Sea level anomalies from satellite altimeterMyOcean SL TAC – SSALTO/DUACS system and productsFocus on the REProcessed data for assimilation Yannice Faugère

4. Level 2 products from all agencies Overview of the DUACS system • DUACS stands for Developing Use of Altimetry for Climate Studies • It is an operational production system (since 1998), and the name of a family of Level 2P to Level 4 altimeter products • Originally a EU project, it’s now part of the CNES SSALTOground segment and used in the MyOcean project • 2 components: • Near Real Time (NRT): objective is to provide operational applications with homogeneous and directly usable high quality altimeter data from all missions in operations • Delayed Time (DT), objective is to maintain a consistent altimeter climate data record using the state-of-the-art recommendations from the altimetry community •  DT data set has been entirely reprocessed (REP) last year Multimission Level 2P/3/4 products

5. The DUACS REP dataset – Input data • GDR data from all altimeter missions • 7 missions have been used for last REP • The total time series corresponds to ~60 years of cumulated data

6. The DUACS REP dataset – Input data preprocessing • 7 missions with almost 7 sets of standards (except on recent products) • Homogeneity is a strong requirement for the users: between the missions, over time • Necessary to complement L2 dataset : •  Updates instrumental correction, orbit solution, reference surfaces, … 30 types! •  Use of the most recent standards Mean (left) and stdev (right) of New (GSFC) orbit - GDR Orbit on TP whole mission -2 cm 2 cm 0 2 cm Centimetric systematic and/or variable errors in the TP GDR product that would be partly assimilated without updates of the latest orbit available

7. Standards of the SSH components for the 2010 reprocessing  Fully documented in the User Manual

8. The DUACS REP dataset – Input data Calibration/Validation • Validation activities of altimetric measurements are essential to estimate the performance of altimetric missions, consolidate the reliability of altimeter missions, quantify the evolution and improvements of altimeter • These activities are crucial to obtain high quality REP products • These validation activities consists in • Global internal analyses: Ensure the internal consistency of altimetry data and sea-level estimation from validation diagnostics derived from one mission • Global multi-mission comparisons: Measure mainly the sea-level consistency between different altimetry missions as global and geographical biases or drift for instance. • Global altimetry and In-situ data comparison : Measure the sea-level consistency between different altimetry missions and in-situ data which are independant : tide gauge networks, ARGO profiles. Estimation of the GMSL from all the altimetry missions from 1993 to 2010

9. The DUACS REP dataset – Processing • Editing process: to detect and remove the erroneous measurements • Mono satellite orbit error reduction • Multisatellite orbit error reduction based on dual crossover minimization of all the satellite on a reference mission (now Jason-2) • Estimation and correction of residual Long wave length error • Along track filtering and sampling RMS of SL variability for ERS-1 (from Le Traon and Ogor, 1998) Before OER After OER

10. The DUACS REP dataset – The REP products • 3 families of DUACS product : • The Reduced GDR or CorSSH (L2P): possibility to choose a specific corrections • Along track SLA products (L3): along track product, various resolution, and various processing • Multimission maps (L4): Merges the information of all the satellites • - Reference series (constant 2-satellites since 1993) • Update: uses all sensors available • Several kind of frequency: weekly, monthly, + daily test dataset available • Frequency of the updates : • Time series updated every ~6 months • The whole reprocessing is performed every ~2 years • The L3/4 products available for the Global Ocean and for Regional Seas with specific tuning: Mediterranean Sea and Black Sea. New regions will be available next year (Arctic, North West Shelves) « intermediate »

11. The DUACS REP dataset – Final product validation MSL trend differences between new and old DT data set (mm/year) • Internal consistency of the DUACS DT series which allows us to measure the intrinsic performances of altimeter system at different spatial and temporal scales (SSH at crossovers, along-track statistics, global and local MSL trends…) • External altimetry data and diagnostics are generated. In this case, the tools are based on comparisons between altimeter and in-situ measurements in order to detect drift in altimeter SSH with external and independent data MSL L4 DT – MSL Tide Gauge For more details see : http://www.aviso.oceanobs.com/fileadmin/documents/OSTST/2010/Pujol_DUACS.pdf

12. The DUACS REP dataset – Summary • The use of reprocessed data is crucial for model reanalysis: • A 60 years – data set • Recent altimetric standards, correction, calibration, intermission biases • Homogeneity of the standard for all the missions • Temporal homogeneity • The Validation and Calibration activities are crucial to : • Reduce the unavoidable errors of the input (GDR) data (biases, drift, …) • Select the best correction • Allows to have the accuracy needed for the climate applications • The DUACS reprocessing exercise is a complex and heavy process. All the DUACS processing steps have to be applied again •  nominal frequency every two years •  Any feedback from the users welcome !!!!

13. The ocean Mean Dynamic Topography Marie-Hélène Rio • Very important referenced surface needed for SLA assimilation • May be computed from OGCM runs, climatological fields, inverse modeling, combination of altimetry and gravity (GRACE, GOCE… but only at large scales (>100km with GOCE)) • The use of an observation-based MDT has proved to improve both the analysis and the forecasts of OGCM assimilating SLA • Do this group needs/wants to use the same MDT ???

14. The ocean Mean Dynamic Topography • Latest « observed » solution computed at CLS (as part of the SLOOP project) = the CNES-CLS09 ¼° MDT from altimetry, GRACE geoid model, and in situ measurements (drifters/ARGO) for the 1993-2008 time period The CNES-CLS09 MDT (Rio et al, 2011) cm • Further improvements are expected with the use of the new GOCE geoid models, improved altimetric Mean Sea Surface (CNES-CLS11, DTU10) and updated in-situ dataset of Argo and drifters

15. T/S profiles from in-situ observationsThe CORA3.2 data set MyOcean INS TAC – Coriolis data center Cécile Cabanes Antoine Grouazel

16. Description of the CORA data set • Temperature and salinity profiles at observed levels • Global data set for the 1990-2010 periods (CORA3.2) • Corresponds to an extraction of all in situ T and S profiles from the Coriolis database at a given time • Updated on a yearly basis with several processing steps : • Distributed as part of MyOcean (next week!) • Addition of new profiles – duplicate check • Update of existing profiles (raw  adjusted parameters) • Quality control • Specific processing: XBT fall rate correction

17. Data types and sources • 7 data types : • PF files : Argo profiles (RT&DM) • XB files : high res. XBT profiles from research vessels and GTSPP • CT files : CTD high res. profiles from research vessels + sea gliders data + marine mammals data received from MNHN • OC files : high resolution CTD profiles from WOD09 • MO files : TAO, TRITON PIRATA data received in real time • BA and TE files : low resolution profiles received from GTS

18. Data types and sources temperature salinity CORA3.1 • High frequency coastal moorings  large number of stations in ‘TE’ files • XBT stations from GTSPP have been added in this new version of CORA • WOD05 WOD09 for high resolution CTD • A large number of Argo profiles have been processed in DM between the two releases

19. Quality Controls : two steps 1. TESTS CLS • 2. CHECKS of the WHOLE database • follow platforms with recurrent alerts • analyses of AO residuals

20. Plateforme : 3900062 θ S TEMP PSAL Quality Controls • 1. Automatic tests (CLS) : • values outside regional limits, values=0, constant along a profile • comparison to climatology ( > 10 σ) (WOA09) + systematic bias compared to climatology • Large salinity gradient at the surface •  All suspicious profiles are visually checked and QC flags are modified if necessary • 2. Checks of the whole database : • Anomaly Method test : Bias > 7 sigma over at least half of the profile values • Objective Analysis test : statistical test involving an objective analysis method and based on residual analysis (Gaillard et al., 2009) • Argo platform control : Argo floats with recurrent alerts as well as Argo floats pointed out by the altimetry test (Guinehut et al., 2009) are visually checked over all their life period Check of the Argo platform over all its life period

21. CORA 3.1 flag =1 flag= 3 clim +/- 5 STD Quality controls: feedback from GLORYS2V1 (N. Ferry) • Suspicious profile detected by comparison with the model solution : • ~ 50 % of the 1993-2007 alerts confirmed after visual check and corrected in CORA3.2 • This kind of feedback can help to improve previous tests •  Your feedbacks are important and really welcome !!!!

22. Update of existing profiles : temperature, salinity, pressure corrected from a drift or an offset etc… CORA database not only contains the raw parameters but also the adjusted parameters if it exists • Argofloats : the adjusted parameter is mainly the salinity corrected in delayed mode by the PI of the float (Owens and Wong method) – but also the pressure About 75% of Argo Data is processed in delayed mode in CORA3.2  Up-to-date compared to the GDAC Argo data sets

23. After correction Median bias before correction Depth 0 Specific processing: correction of XBT fall rate • Many corrections available – choice of the home made one: empirical correction developed by M. Hamon : • “M. Hamon, P. Y. Le Traon, and G. Reverdin: Empirical correction of XBT fall rate and its impact on heat content analysis, Ocean Sci. Discuss., 8, 291-320, 2011.” • Collocation method between XBT and ARGO or CTD profiles from CORA • Statistical corrections computed for each year and different classes of probes: • Correction of a thermal bias : Txbt = Txbt-Toff (due to possible errors on the calibration of the probe and obtained with selected profiles with a weak T gradient in the upper layer) • Correction of the deph bias : Ztrue= Zobs( 1-A-B*Zobs)-Zoff (due to too simple approximation of the XBT fall rate) Median temperature bias between XBT and collocated CTD or Argo Total number of XBT Number of collocated pairs (1° lat, 2° lon, 15 days) -0.02

24. CORA3.2 – Summary • CORA3.2 = more than 5 million profiles • First challenge : to gathered all the individual profiles ! • Second challenge : to qualify them ! • Third challenge : to be up-to-date with common corrections/adjustments • In-situ data sets: alive datasets : new validation tools, detection of a failure on a sensor, on a datasets … delayed time processing needed to correct drift, offset … •  Any feedback from the users welcome !!!!

25. SST from satellite Comparison study performed as part of MyOcean WP4 activities Stéphanie Guinehut

26. 8 SST products to be compared • Over the year 2008

27. Differences between 2 analysis • Spatial scales resolved :

28. Differences with Ostia • Differences with Ostia – mean over the year 2008:

29. Differences with Argo • Annual zonal mean of the differences Argo-SST

30. Differences with Argo • Annual zonal rms of the differences Argo-SST • Ostia* • Reynolds AVHRR-AMSR • Odyssea • RTG • * more resolution requested

31. Global mean averaged values • No mask / without OdysseaOdyssea mask From Balmaseda & al.

32. Which delayed time observation data sets for Reanalysis ?? • Long time series : reprocessing are always complex and heavy processes – real need to be up-to-date with the last standards - up-to-date with commons correction/adjustments • Still uncertainties on certain data sets (XBT fall rate, Argo floats, new sensors …) – it is important to know exactly which data sets are used! • When several datasets available: which one to choose ?? Preliminary studies needed • With the improvement of the reanalysis outputs – they can be used as new tools for validation / intercomparison  Any feedback from the users welcome !!!!

33. … • …

34. The ocean Mean Dynamic Topography • Very important surface reference needed for SLA assimilation. • May be computed from OGCM runs, climatological fields, inverse modeling, combination of altimetry and gravity (GRACE, GOCE… but only at large scales (>100km with GOCE)) • The use of an observation-based MDT has proved to improve both the analysis and the forecasts of OGCM assimilating SLA. • For years, CLS has been working on computing « observed » MDT (combination of gravity, altimetry and in-situ measurements (ARGO, drifters…)

35. Latest « observed » solution computed at CLS: the CNES-CLS09 ¼° MDT from altimetry, GRACE geoid model, and in situ measurements (drifters/ARGO) for the 1993-2008 time period The CNES-CLS09 MDT (Rio et al, 2011) cm • Further improvements are expected with the use of the new GOCE geoid models, improved altimetric Mean Sea Surface (CNES-CLS11, DTU10) and updated in-situ dataset of ARGO and drifters

36. The ocean Mean Dynamic Topography MSS CNES-CLS10 – ITG-GRACE2010s, 100km low-pass filtered cm

37. The ocean Mean Dynamic Topography MSS CNES-CLS10 – GOCE, 100km low-pass filtered cm

38. Impact of removing or not Ekman currents from drifter velocities on the final CNES-CLS09 MDT estimate cm

39. Ekman model used in the CNES-CLS09 MDT computation (Rio et al, 2011) to extract the SVP geostrophic velocities