Coriolis Ocean database for ReAnalysis - ODV: ODV · Coriolis Ocean database for ReAnalysis CORA3.4 Product User Manual Version 1.0 R&D Coriolis Team February 11, 2013 IFREMER - Pointe

Coriolis Ocean database for ReAnalysis

CORA3.4 Product User Manual

Version 1.0

R&D Coriolis Team

February 11, 2013

IFREMER - Pointe du Diable -29280 Plouzané - web: www.coriolis.eu.org/Science/Data-and-Products

http://www.coriolis.eu.org/Science/Data-and-Products

Contents

1 Dataset overview 11.1 History of the dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1.1 Previous versions of CORA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1.2 Main changes for the CORA3.4 version . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Data sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Organisation of the dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.3.1 Files formats - structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3.2 Type of files and data type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.3.3 The Index files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2 Data quality 52.1 Quality flags . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.2 Adjusted parameter versus parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.3 Data processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.3.1 Climatological test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.3.2 Check of duplicates profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.3.3 XBT bias correction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.3.4 Feebacks from model assimilation . . . . . . . . . . . . . . . . . . . . . . . . . 92.3.5 Non monotonic pressures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.3.6 Duplicate pressure-depth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.3.7 Global consistancy checked with Objective analysis tool . . . . . . . . . . . . . . 9

3 Data coverage 13

A Tables 19

i

CHAPTER 1

Dataset overview

CORA3.4 dataset is a product of MyOcean2 FP7 project. It stands for the historical globaldataset of MyOcean2V3. This document is a Product User Manual, it aims to describe thecontent of the dataset and how to use it properly. Some of the validation steps refer to the article[Cabanes et al.(2013)Cabanes, Grouazel, von Schuckmann, Hamon, Turpin, Coatanoan, Paris, Guinehut, Boone, Ferry, de Boyer Montégut, Carval, Reverdin, Pouliquen, and Le Traon].

1.1 History of the dataset

The program Coriolis has been setup at Ifremer at the beginning of the 2000’s in the wake of the devel-opment of operational oceanography in France. The project was launched in order to provide ocean insitu measurements to the French operational ocean analysis and forecasting system (Mercator-Océan)and to contribute to a continuous, automatic, and permanent observation networks. The Coriolis datacentre has been set up to gather, qualify [Coatanoan and Petit de la Villéon(2005)] and distribute datafrom the global ocean both in real and delayed time. The Coriolis database is a real time database as itis updated every day as new data arrive. On the contrary, the CORA dataset corresponds to an extractionof all in situ temperature and salinity profiles from the Coriolis database at a given time. All the data isthen re-qualified.

CORA is meant to fit the needs of both re-analysis and research projects. However, dealing with thequantity of data required by re-analysis projects and the quality of data required by research projectsremains a difficult task. Several important changes have been made since the last release CORA02, bothin the production procedure (to be able to release yearly reanalysis) and quality checks applied to thedata. These changes are fully described in this document.

1.1.1 Previous versions of CORA

Two previous versions of CORA have already been released by the Coriolis data center and the R&DCoriolis team:

1. CORA01 in 2007. It contains data from 2002 to 2006

2. CORA02 in 2008. data from 1990 to 2007.

To produce such a dataset the Coriolis data center proceed basically in three steps:

1

http://dx.doi.org/10.5194/os-9-1-2013

http://www.coriolis.eu.org/Data-Services-Products/View-Download/Data-selection

CORA3.4 Documentation, Rev. 1.0

1. Data passes through a statistical quality check based on objective analysis method (see[Gaillard et al.(2009)Gaillard, Autret, Thierry, Galaup, Coatanoan, and Loubrieu] for further de-tails).This statistical check produced alerts on doubtful profiles.

2. All doubtful profiles are visually checked, and profiles are flagged - if necessary - in the Coriolisdatabase.

3. Data is extracted from the Coriolis database (to produce netCDF files).

1.1.2 Main changes for the CORA3.4 version

• A new procedure is now used to produce the dataset: This sequence of processing steps is fullydescribed in the paper Cabanes et al. 2013:

• A set of quality checks is performed on the data (see Cabanes et al. 2013 for more details)

• A check of duplicates was re-run on the whole dataset.

• An XBT bias correction has been applied.

• CORA3.4 release covers the period 1990-2011 (such as CORA3.3) but ICES (CIEM) CTD datahave been added to the dataset.

1.2 Data sources

Data submitted to, or obtained by, the Coriolis Data Centre which contains profiles of temperature and/orsalinity were potential data source for CORA3.4 dataset. The CORA3.4 dataset thus corresponds to theCoriolis database at the date of the CORA3.4 retrieval (22th of January, 2013). For CORA3.3 (previousversion), the data retrieval has been spread over time (see table 1.1).

Data Span Date of retrieval1990-2008 25-May-2010

2009 09-September-20102010 22-03-2011

2011 01-09 04-10-20112011 10-12 22-03-2012

Table 1.1: Dates of retrieval for CORA3.3

The Coriolis centre receives data from Argo GDAC, French research ships, GTS flow, GTSPP, GOSUDGDAC, MEDS, voluntary observing and merchants ships, moorings (TAO-TRITON-PIRATA-RAMAplus coastal moorings), and the World Ocean Database (not in real time for the last one and for CTDonly). CORA thus contains data from different types of instruments: mainly Argo floats, XBT, CTD,XCTD, and moorings.

1.3 Organisation of the dataset

1.3.1 Files formats - structure

Files structure is the same as for the distribution of the Argo profiles data and it is fully describedin the argo-dm-user manual, section 2.2. Each netcdf files contains many profiles (number given by

2 Chapter 1. Dataset overview

http://www.argodatamgt.org/content/download/4729/34634/file/argo-dm-user-manual-version 2.4.pdf


the dimension N_PROF) and a profile contains measurements of different variables (e.g. temperature,salinity) performed at different pressures or immersion (the number of vertical levels is given in thenetCDF file by the N_LEVELS dimension) taken as the instrument is being dropped or risen verticallyin the water column. For surface-only data, the profile consists of a single measurement. For mooredbuoys and drifting buoys, a profile is a discrete set of concurrent measurements from the instrumentsplaced at different depths.

All the variables in a file are defined for Argo float profiles, but most of these variables still have thesame signification for other types of profile (e.g. XBT or CTD profiles).

Guidance for the usersEach profile has a unique identifier in the Coriolis database and the CORA dataset which is theDC_REFERENCE number. Please, refer to this DC_REFERENCE number if you want to make afeedback on a specific profile to the Coriolis data centre. The variable PLATFORM_NUMBER is theplatform identifier that is assigned for the life of the platform (e.g. Profiling floats, moored buoys,...). For measurements collected from research vessels or merchant ships-of-opportunity the PLAT-FORM_NUMBER is the vessel/ship identifier.

1.3.2 Type of files and data type

In CORA3.4, data is sorted in 9 types depending mainly on the data sources and resolution. Most ofthese types are those defined for the GTSPP (PF, CT, XB, BA and TE) while others are ‘in-house’ types(OC, MO and HF). All the data are stored in netcdf files using the same format as the one defined for theArgo program and a naming convention that indicates the data type. Files are stored in yearly directories.There is one file per day and per type. The file name is of the form:

CO_DMQCGL01_YYYYMMDD_PR_TT.nc

This file contains all the raw data of the date YYYYMMDD and of the data type TT.

• PF files : data from Argo floats directly received from DACS (real Time and delayed mode ifavailable). These data have a nominal accuracy of 0.01° and 0.01 PSU and are transmitted withfull resolution.

• XB files : XBT or XCTD data received from research and opportunity vessels have accuracywithin 0.03° to 0.1° for temperature and 0.03 to 0.1 PSU for salinity.

• CT files : contains CTD data from research vessels (accuracy on the order of 0.002° for tempera-ture and 0.003 PSU for salinity after calibration) but also data from sea mammals equipped withCTD (accuracy is on the order of 0.01° for temperature and 0.02 PSU for salinity but can be lowerdepending of the availability of reference data for post-processing, see Boehme et al, 2009) andreceived from MNHN and some sea Gliders.

• OC files : Others CTD and XCTD data coming from the high resolution CTD dataset of the Worldocean database 2009.

• MO files : Mooring data are mostly from TAO TRITON RAMA and PIRATA mooring and haveaccuracy generally comparable to Argo floats (except for S near surface).

• TE and BA files : The two last categories are for all the data transmitted trough the GTS (data fromArgo floats not yet received at the DACS, mooring, XBT,...). This transmission system imposeslimitation on the accuracy: data is truncated two and one places beyond decimal point for TE andBA type respectively.

• HF files: High Frequency data coming from coastal moored buoys (max 3 levels of immersion,max depth<200m, with less than 0.01° of variation in position and more than one measure per

1.3. Organisation of the dataset 3


day on at least 10 days in a year). The confidence on the quality of such data is much lower thanoffshore classical moorings because of the lack of climatology in such area.

• IC files: ICES CTD from CIEM database distributed over the whole time span.

Guidance for the users : How to find a particular data type in CORA3.4?This classification of the data in netcdf files depends mainly on the data sources and resolution. However,it can be difficult for the user to find all the data from one type of instrument (e.g. CTD) as it is found indifferent types of files (e.g. CT, OC, TE files for CTD instruments). The variable WMO_INST_TYPEin the netcdf raw files can help to distinguish the different instrument types (see table A.1). How-ever the same WMO_INST_TYPE can be attributed to different types of instrument platform (e.g. theWMO_INST_TYPE 830 standing for CTD is attributed to CTD launched from vessels or ships, CTDattached to sea mammals, some mooring buoys etc...). To facilitate the identification of a particulartype of data a PROBE_TYPE code was attributed to each profile (see table A.1 for definition of codes).The PROBE_TYPE variable can be found in the Index files (see section 1.3.2) and be used to select aparticular type of data.

1.3.3 The Index files

An Index for the raw data of CORA3.4 is available (./RAW/Index). The index is organized as follow:There is one index file per month (e.g. index_cora3.4_01_1990.nc) containing only the main informationabout the profile (e.g. LONGITUDE, LATITUDE, JULD, PROBE_TYPE, PLATFORM_NUMBER,etc...), the name of the corresponding raw file (FILE_NAME) and the profile number in the raw file(NUM_PROF_IN_FILE). As the index files can be read very quickly, this allows the user to make hisown data selection or to pick up a single profile in the raw dataset. The PROBE_TYPE variable wasadded to the index files (but not in the raw files) to facilitate the identification of a particular type of data(see table A.2).

4 Chapter 1. Dataset overview

CHAPTER 2

Data quality

2.1 Quality flags

Each measurement for each profile is associated with a control quality flag ranging from 0 to 9. Basically,a flag 1 stand for good data, a flag 4 stand for bad data. See table 2.1 for a complete description.

Quality Code Meaning0 No QC was performed1 Good data2 Probably good data3 Bad data that are potentially correctable4 Bad data5 Value changed6 Not used7 Not used8 Interpolated value9 Missing value

Table 2.1: Quality flags and their definition

Guidance for users: How to use flag values?We advise to keep stations for which:

POS _QC , 3 or 4 and JULD_QC , 3 or 4

and for each station it is advised to keep only measurements for which: PRES _QC

DEPH_QC

= 0, 1 or 2 and PARAM_QC = 1 or 2 }

Quality flags exist both for the PARAM (TEMP_QC, PSAL_QC,...) and the PARAM_ADJUSTED(TEMP_ADJUSTED_QC, PSAL_ADJUSTED_QC,...). Thus if one uses the adjusted values of salinity(PSAL_ADJUSTED) it should check the flag PSAL_ADJUSTED_QC to determine if the salinity valueis good or not. PARAM_ADJUSTED are filled for Argo data but for other type of platform most of thetime it is an empty field.

5


2.2 Adjusted parameter versus parameter

CORA3.4 dataset not only contains the raw parameters such as temperature, salinity, pressure or depthas received from the instrument. It can also include adjusted parameters, i.e. temperature, salinity,pressure or depth corrected from a drift or an offset. The data types affected by these adjustments areArgo floats and XBTs. For Argo data, it is the responsibility of each DAC to provide data correctionsboth in real time and in delayed mode. The Coriolis data centre, as a GDAC, gathers these correctionsand stores raw and adjusted parameters into the Coriolis database. No supplementary correction hasbeen made or applied to the Argo data in the CORA3.4 dataset. On the contrary, corrections for XBTdata have been calculated as part of the CORA3.4 data processing. For more details, see section 3.3 of the[Cabanes et al.(2013)Cabanes, Grouazel, von Schuckmann, Hamon, Turpin, Coatanoan, Paris, Guinehut, Boone, Ferry, de Boyer Montégut, Carval, Reverdin, Pouliquen, and Le Traon].

Guidance for the users: How to use adjusted parameters?It is advice to take the PARAM_ADJUSTED values instead of the PARAM values each time thePARAM_ADJUSTED values exist. The user should then take the PARAM_ADJUSTED values forthe whole profile if the variable DATA_MODE =’A’ or ’D’.

2.3 Data processing

The creation of CORA consists in a sequence of extra validation/correction steps performed indelayed mode and the whole dataset is also validated trhough real time checks: To have moredetails about the data validation done at Ifremer data center in real time refer to section 3.2.1 of[Cabanes et al.(2013)Cabanes, Grouazel, von Schuckmann, Hamon, Turpin, Coatanoan, Paris, Guinehut, Boone, Ferry, de Boyer Montégut, Carval, Reverdin, Pouliquen, and Le Traon].Here is an enumeration of usual processing steps performed in delayed-mode in CORA.

2.3.1 Climatological test

To detect coarse outliers on temperature and salinity, we use a climatological check. For CORA3.4 thecriteria choosen were:Temprature:

v = max(clim + 9xS igma, clim + 9 ∗ 0.1) (2.1)

Salinity:v = max(clim + 9xS igma, clim + 9 ∗ 0.025) (2.2)

In CORA3.4 this test has been performed on IC files and not a single alert was risen. The climatologyused was ’arglv502’ built from ARIVO dataset.

Figure 2.1 is a diagram of the different steps performed for the CORA3.4 dataset processing. The overallprocess includes duplicate checks to ensure the data is unique in the dataset, various quality checks thathelp differentiate ‘bad’ data from ‘correct’ ones, and adjustments applied to parameters. These differentsteps are detailed in the Cabanes et al 2013 paper. Some of them are processed in real or near-real timeand applied on the data freshly downloaded into the Coriolis database; others are processed in delayedtime (when the CORA3.4 files are generated).

6 Chapter 2. Data quality


Figure 2.1: Process used to update the CORA dataset

2.3. Data processing 7


2.3.2 Check of duplicates profiles

To have details about duplicates suppression whithin CORA dataset refer to section 3.1 of[Cabanes et al.(2013)Cabanes, Grouazel, von Schuckmann, Hamon, Turpin, Coatanoan, Paris, Guinehut, Boone, Ferry, de Boyer Montégut, Carval, Reverdin, Pouliquen, and Le Traon].Find below the table giving criteria of duplicate detection 2.2, an other table 2.3 explaining how weexclude some pairs of profiles to be duplicates thanks to data types comparison and in table 2.4 you havethe number of deleted profiles for CORA3.4. Note that duplicates in CORA3.4 concern solely PR_CTand PR_IC files, a previous duplicates check has been done in CORA3.3 between all the different typesof files.

Couple with same type(ex: BA BA or TE TE)

Couple with different type(ex: BA TE, BA OC)

delta date = +/- 0,00001 days (0,864s) 0,042 days (1h28s) - exception forTE PF : 24h -

Delta longitude = +/- 0,0001° 0,1°

delta latitude = +/- 0,0001° 0,1°

Platform number Can be different Must be the same

Table 2.2: Temporal and spatial criteria to identify possible duplicates

types OC BA XB CT PF MO TE HF IC

OC ? del. BA no del. OC no no del. TE del. HF del. OC

BA ? del. BA del. BA del. BA del. BA del. BA del. HF del. BA

XB ? no no no del. TE del. HF no

CT ? no no no del. HF del. CT

PF ? no del. TE del. HF no

MO ? del. TE del. HF no

TE ? del. HF del. TE

HF ? del. HF

IC ?

Table 2.3: Criteria on the data types to decide whether the pair is a duplicate

2.3.3 XBT bias correction

This particular correction follows the method decribed by [Hamon et al.(submitted 2011)Hamon, Reverdin, and Le Traon]but has not been applied in CORA3.4 because new data are ex-clusively CTD. More information can be found in section 3.3.2 of[Cabanes et al.(2013)Cabanes, Grouazel, von Schuckmann, Hamon, Turpin, Coatanoan, Paris, Guinehut, Boone, Ferry, de Boyer Montégut, Carval, Reverdin, Pouliquen, and Le Traon]

year 1990 1991 1992 1993 1994 1995 1996 1997 1998 19994 0 0 0 0 0 0 0 0 22000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 20110 0 0 0 0 0 2 12 18 66 2 0

Table 2.4: Number of pair of profiles considered as duplicate with the detection check for CORA3.4



1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

0

20000

40000

60000

80000

100000

120000

Number of pairs detected Number of profiles deleted

Figure 2.2: Number of pair detected and profiles deleted

The values of the coefficient are given in table A.3. Figure 2.3 gives the number of XBT with co-localised profiles that can be found each year. The difference between the XBT profile and the referenceprofile before and after applying the correction is plotted in figure 2.4.

2.3.4 Feebacks from model assimilation

Mercator Ocean uses to provide Coriolis a list of anomalies detected thanks to an innovation checkwhen CORA observations are assimilated into runs such as GLORYS2V2. CORA benefits from thosefeedbacks and has confirmed about 50% of Mercator’s alerts in CORA3.2. This process has not beenapplied for CORA3.4 because of a shift between the release of CORA and the run date of GLORYS.

2.3.5 Non monotonic pressures

Nothing has been done in CORA3.4 about this particular Argo float issue. It has been done for CORA3.3.

2.3.6 Duplicate pressure-depth

Some profiles have depth values copied in the pressure (i.e. ’PRES’) field and nothing in ’DEPH’ field.Nothing has been done in CORA3.4 because it mostly concerns historical data from Coriolis database.

2.3.7 Global consistancy checked with Objective analysis tool

The ultimate process applied to CORA observations is an objective analysis (ISAS software also used inreal-time at Coriolis data center). It gives us alerts on profiles that are not consistant with the spatial andtemporal neighbourhood and secondarily produce 3D gridded fields with our raw data on the temperatureand salinity and respective standard deviation.



Guidance for the users: How to use XBT with thermal offset and depth corrected?For XBT profiles (PROBE_TYPE=10 in the index files) the corrected values are reported in the AD-JUSTED fields: TEMP_ADJUSTED field for the temperature corrected from the thermal offset andDEPH_ADJUSTED for the depth corrected.The depth correction which stretches the values of depth can lead to negative depth value on the firstlevel of some profiles (depending on the year, on the category of XBT and on the value of the first level).Those negative depth values have been kept in the DEPH_ADJUSTED field but the quality flag has beenset to 4. Those negative values concern between 50 and 70% of the profiles each year. Users are free toreject or not this first level (notice that the correction applied in the first layer is not that relevant).It appeared to us that some XBT profiles of the CORIOLIS database and CORA dataset have valuesof depth incorrectly stored in the field PRES. As we were not able to find the origin of this error, wecomputed the depth correction assuming that we had a depth and not pressure information. However toallow future corrections, for those XBT, we let the corrected depth in the PRES_ADJUSTED parameter.

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

0

5000

10000

15000

20000

25000

30000

35000

Colocalized with refe-rence profileTotal number of XBT

Figure 2.3: Number of XBT profiles colocalized with a reference profile



A) Difference between XBT and reference profiles before correction

year

B) Difference between XBT and reference profiles after correctionyear

0

0.05

0.15

0.1

-0.05

-0.1

0.15

0.1

0.1

0.05

0

-0.05

-0.1

DEPT

HDE

PTH

Figure 2.4: Difference between XBT and reference profiles before (A) and after (B) corrections




CHAPTER 3

Data coverage

This section gives an overview of the CORA3.4 raw dataset. Figures 3.1 - 3.10 give the spatial distribu-tion of the different data types (table A.2) for each year between 1990-2011.

13


Figure 3.1: Spatial distribution of Argo floats data for the year 1990 to 2011

Figure 3.2: Spatial distribution of XBT data for the year 1990 to 2011

14 Chapter 3. Data coverage


Figure 3.3: Spatial distribution of XCTD data for the year 1990 to 2011

Figure 3.4: Spatial distribution of CTD data for the year 1990 to 2011

15


Figure 3.5: Spatial distribution of Gliders data for the year 1990 to 2011

Figure 3.6: Spatial distribution of Sea mammals data for the year 1990 to 2011



Figure 3.7: Spatial distribution of TAO-TRITON-PIRATA-RAMA moorings array for the year 1990 to2011

Figure 3.8: Spatial distribution of Coastal Moorings data for the year 1990 to 2011

17


Figure 3.9: Spatial distribution of other types of Moorings and drifters data for the year 1990 to 2011

Figure 3.10: Spatial distribution of unknown type of platform/instrument data for the year 1990 to 2011


APPENDIX A

Tables

19


WMO_INST_TYPE Description WMO_INST_TYPE Description001 Sippican T-4 700 Sippican XCTD standard002 Sippican T-4 new eq. 710 Sippican XCTD deep009 T-04 460m T-04 1500F 720 Sippican AXCTD011 Sippican T-5 730 Sippican SXCTD019 T-05 1830m 741 TSK XCTD021 Sippican Fast Deep 742 TSK XCTD-2022 inconnu022 743 TSK XCTD-2F031 Sippican T-6 751 TSK AXCTD032 Sippican T-6 new eq. 800 MBT Mechanical Bathy Thermograph039 T-06 460m 810 Hydrocast041 Sippican T-7 820 Thermistor Chain042 Sippican T-7 new eq. 830 CTD049 T-07 760m 831 Profiling Float (PF) - Generic051 Sippican Deep Blue 840 PF, PROVOR, no conductivity sensor052 Sippican Deep Blue new eq. 841 PF, PROVOR, SBE conductivity sensor059 T-DB 760m 842 PF, PROVOR, FSI conductivity sensor060 inconnu060 843 PF, Polar Ocean Profiling System (POPS), PROVOR SBE061 Sippican T-10 844 PF, ARVOR, Seabird conductivity sensor069 T-10 200m 845 PF, APEX, no conductivity sensor071 Sippican T-11 846 PF, APEX, SBE conductivity sensor079 T-11 460m 847 PF, APEX, FSI conductivity sensor081 Sippican AXBT (300m probes) 850 PF, SOLO, no conductivity sensor201 TSK T-4 851 PF, SOLO, SBE conductivity sensor202 TSK T-4 new eq. 852 PF, SOLO, FSI conductivity sensor211 TSK T-6 853 PF, SOLO2 (SCRIPPS), Seabird conductivity sensor212 TSK T-6 new eq. 856 PF, NINJA, SBE conductivity sensor221 TSK T-7 858 PF, NINJA, TSK conductivity sensor222 TSK T-7 new eq. 860 PF, NEMO, SBE conductivity sensor229 TSK T-7 861 PF, NEMO, FSI conductivity sensor231 TSK T-5 995 Instrument attached to marine mammals241 TSK T-10 999 Unknown251 TSK Deep Blue252 TSK Deep Blue401 Sparton XBT-1411 Sparton XBT-3421 Sparton XBT-4431 Sparton XBT-5451 Sparton XBT-6460 Sparton XBT-7 (old)461 Sparton XBT-7462 Sparton XBT-7481 Sparton XBT-10491 Sparton XBT-20501 Sparton XBT-20DB

Table A.1: WMO INSTRUMENT TYPES and their definition

20 Appendix A. Tables


PROBE_TYPE Description10 XBT20 CTD30 XCTD40 PROFILING FLOAT51 TAO-TRITON PIRATA RAMA MOORINGS52 COASTAL MOORINGS (< 20km from the coast)50 OTHER MOORINGS60 GLIDERS70 INSTRUMENT ATTACHED TO SEA MAMMALS80 DRIFTING BUOYS0 UNKNOWN

Table A.2: PROBE TYPES: Codes and definitions

21


HO

Tan

dD

EE

PH

OT

and

SHA

LL

OW

CO

LD

and

SHA

LL

OW

CO

LD

and

DE

EP

ab

ca

bc

ab

ca

bc

1990

-7.1

143e

-07

0.00

8364

50.

6906

52.

1037

e-05

-0.0

0104

390.

6806

84.

2526

e-06

0.00

6319

80.

7973

44.

2959

e-06

0.00

4332

70.

6585

819

911.

3984

e-06

0.00

7346

60.

6860

11.

0786

e-05

0.00

3023

60.

6717

93.

9592

e-07

0.00

7183

90.

7733

73.

4361

e-06

0.00

4904

0.60

144

1992

1.04

72e-

060.

0074

213

0.65

338

1.08

47e-

050.

0028

244

0.65

256

7.62

53e-

060.

0030

343

0.51

567

5.37

15e-

060.

0034

703

0.60

528

1993

2.97

32e-

070.

0080

382

0.61

483

3.18

62e-

070.

0071

422

0.61

035

1.96

61e-

05-0

.003

6154

0.33

827

2.84

72e-

060.

0051

036

0.54

112

1994

1.59

63e-

060.

0073

180.

5837

71.

6427

e-05

0.00

0876

990.

6284

61.

0832

e-05

0.00

8353

81.

0879

3.15

58e-

060.

0049

934

0.56

838

1995

3.35

09e-

060.

0060

772

0.51

941

1.08

86e-

060.

0018

374

0.22

987

0.00

0162

99-0

.011

499

3.55

748.

3509

e-06

0.00

0484

410.

5751

719

968.

0693

e-06

0.00

5777

61.

1429

4.41

81e-

05-0

.004

3531

0.42

843

4.61

05e-

06-0

.009

6887

0.00

5048

2.21

01e-

060.

0046

373

0.53

954

1997

1.06

53e-

050.

0009

5079

0.89

169

-2.0

466e

-05

0.02

4874

-0.4

6646

2.92

97e-

050.

0329

02-1

.883

71.

2932

e-05

-0.0

0113

470.

8670

219

98-6

.221

6e-0

70.

0075

039

0.27

032

-3.7

186e

-06

0.01

1031

1.29

13-7

.240

6e-0

50.

0315

210.

5801

51.

0817

e-06

0.00

4931

30.

4544

619

996.

2702

e-06

0.00

4398

51.

4626

7.50

43e-

060.

0002

3827

-0.5

9822

5.33

83e-

05-0

.003

4839

0.79

974

1.42

28e-

060.

0047

577

0.62

921

2000

5.45

07e-

060.

0137

073.

2011

-0.0

0018

074

0.07

7799

3.00

920.

0001

4598

-0.0

0867

353.

0833

7.78

39e-

060.

0027

011

3.37

5720

01-2

.319

3e-0

60.

0142

952.

1633

4.05

22e-

07-0

.000

5023

4-1

.072

7-0

.000

3653

60.

1158

35.

7531

1.68

03e-

05-0

.016

518

0.89

113

2002

2.09

87e-

050.

0026

797

2.06

225.

0699

e-05

0.02

2176

4.20

650.

0001

7235

-0.0

3970

43.

4083

1.43

25e-

05-0

.007

1179

2.47

6920

031.

0295

e-05

0.00

3334

11.

5199

-1.7

672e

-05

0.01

7371

1.48

36-0

.000

1589

40.

0421

830.

2733

36.

6755

e-07

-0.0

0012

579

1.78

5320

045.

4394

e-05

-0.0

2932

70.

5006

93.

9714

e-05

0.00

6209

21.

0057

-7.9

741e

-06

0.00

0721

452.

3911

-1.0

725e

-06

0.00

2635

82.

1516

2005

-1.2

825e

-06

0.01

2921

1.69

754.

2472

e-05

0.00

8682

31.

9908

5.62

5e-0

5-0

.024

077

-0.5

2684

2.85

11e-

05-0

.010

609

1.81

520

062.

4648

e-05

-0.0

0319

660.

1327

8-3

.388

5e-0

60.

0085

185

0.43

597

2.52

82e-

05-0

.027

834

0.28

719

-1.0

889e

-05

0.01

6215

1.84

2220

07-2

.885

1e-0

50.

0443

121.

098

-1.9

285e

-05

0.02

4406

3.57

10.

0002

0061

-0.0

8033

2-1

.440

9-1

.492

8e-0

50.

0211

72.

1417

2008

-2.1

199e

-05

0.03

8473

1.05

83-7

.519

7e-0

90.

0098

791.

4405

3.82

29e-

05-0

.035

406

-1.5

448

-1.5

126e

-05

0.02

7662

1.54

6620

09-1

.303

5e-0

60.

0205

21.

8746

-3.6

274e

-05

0.03

0775

4.23

021.

5642

e-05

0.00

6066

70.

7146

62.

3378

e-05

-0.0

0506

752.

2219

2010

3.86

18e-

060.

0167

30.

6974

3-7

.710

1e-0

50.

0383

861.

6881

1.74

55e-

05-0

.010

886

-2.3

344

3.02

86e-

05-0

.011

257

1.36

0520

112.

5301

e-05

0.00

3911

30.

1218

4-2

.233

2e-0

50.

0282

92-0

.069

972

-0.0

0011

707

0.06

6528

-0.1

6712

-1.5

268e

-05

0.01

9196

0.00

0941

37

Tabl

eA

.3:

Coe

ffici

ents

ofth

epa

rabo

licco

rrec

tion

and

the

dept

hoff

setf

orX

BT

22 Appendix A. Tables

Bibliography

[Cabanes et al.(2013)Cabanes, Grouazel, von Schuckmann, Hamon, Turpin, Coatanoan, Paris, Guinehut, Boone, Ferry, de Boyer Montégut, Carval, Reverdin, Pouliquen, and Le Traon]Cabanes, C., A. Grouazel, K. von Schuckmann, M. Hamon, V. Turpin, C. Coatanoan, F. Paris,S. Guinehut, C. Boone, N. Ferry, C. de Boyer Montégut, T. Carval, G. Reverdin, S. Pouliquen, andP.-Y. Le Traon, 2013: The cora dataset: validation and diagnostics of in-situ ocean temperature andsalinity measurements. Ocean Science, 9, 1–18, doi:10.5194/os-9-1-2013.URL http://www.ocean-sci.net/9/1/2013/

[Coatanoan and Petit de la Villéon(2005)] Coatanoan, C. and L. Petit de la Villéon, 2005: Coriolis DataCentre, In-situ data quality control procedures. Ifremer report, 17 pp.

[Gaillard et al.(2009)Gaillard, Autret, Thierry, Galaup, Coatanoan, and Loubrieu] Gaillard, F.,E. Autret, V. Thierry, P. Galaup, C. Coatanoan, and T. Loubrieu, 2009: Quality controls oflarge Argo datasets. Ocean. Tech., 26, 337–351.

[Hamon et al.(submitted 2011)Hamon, Reverdin, and Le Traon] Hamon, M., G. Reverdin, and P.-Y. LeTraon, submitted 2011: Empirical correction of XBT data. J. Geophys. Res..

23

http://www.ocean-sci.net/9/1/2013/

Documents

Coriolis Ocean database for ReAnalysis - ODV: ODV · Coriolis Ocean database for ReAnalysis CORA3.4 Product User Manual Version 1.0 R&D Coriolis Team February 11, 2013 IFREMER - Pointe