dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2011). These data issues have been already corrected by Argo data producers or corrections are still in progress. However, it can be difficult for users to find appropriate information on the state of corrections made on the Argo data included in the global dataset they are working with. During the decade 2000-2010, the French project Coriolis, whose main aim is to supply in-situ data in real time to French and European operational oceanography programs, started to distribute a quality-controlled dataset named CORA. The first two versions were released in 2007 and 2008. In 2010, as part of the MyOcean project, the Coriolis research and development team developed a new procedure to be able to produce a quality-controlled dataset on a regular basis. Our objective was to update the CORA dataset every year with all the data acquired during the last full year available and to update the entire CORA dataset (full timespan) every 2 yr (the next release of CORA including new data from years 1990-2012 will be made available in the first half of the year 2013).
The general purpose was to meet both the requirements of reanalysis projects (GLORYS -Global Ocean Reanalysis and Simulations project,  Ferry et al. , 2010)  including validation, initialization and assimilation of models (Mercator,  Lellouche et al. , 2012, this issue)  and those of general oceanographic research studies, including ones on climate change  (e. g. von Schuckmann and Le Traon, 2011; Souza et al. , 2011; Guinehut et al. , 2012). This new procedure, described in the present paper, was used to produce the CORA3 dataset. The CORA3 dataset was rechecked with a semi-automated method quite similar to the one presented in  Gronell and Wijffels (2008). Statistical tests, performed on the whole CORA3 dataset, helped to identify suspicious profiles. These suspicious profiles were then visually checked, and control quality flags were set to discriminate "bad" from "good" data.
The application of XBT correction to the CORA3 dataset was an application of the method described in  Hamon et al. (2012). We also developed simple diagnostics to assess Argo data quality and the status of corrections in our dataset. Estimation of global ocean indicators  (GOIs, von Schuckmann and Le Traon, 2011)  such as the global OHC or the global steric sea level (GSSL) from in-situ data remains a considerable challenge, as long-term trend estimations of global quantities are very sensitive to any sensor drift or systematic instrumental bias, but can be a useful tool to monitor the quality of such a global in-situ dataset. This type of data validation tool is in particular well suited to detecting large scale errors due to measurement drifts and systematic instrumental biases (e. g. Willis et al.
, 2008; Barker et al. , 2011). In this paper, GOIs are estimated for CORA3 using the method introduced in von Schuckmann and Le Traon (2011), and used as a diagnostic of the level of quality reached by the CORA3 database. In the following sections of this paper, we present the content of the CORA3 dataset (Sect. 2), validation procedures and XBT corrections (Sect. 3), quality diagnostics performed on the CORA3 dataset, including the use of GOIs (Sect. 4), and, finally, the future perspectives for further versions of CORA.
Data sources The Coriolis data centre collects data mainly in real or nearreal time (depending on data sources and availability) in order to meet the needs of operational oceanography. In particular, Coriolis regularly provides real-time quality-controlled and integrated in-situ ocean measurements for the French operational ocean analysis and forecasting system (Mercator Ocean, www. mercator-ocean. fr) to the European GMES (Global Monitoring for Environment and Security) Marine Core Service MyOcean (www. myocean. eu), as well as to several other national systems. Coriolis is a Data Assembly Centre (DAC) for the Argo program  (Roemmich et al. , 2009, www. argo. ucsd. edu) and, like every DAC, it is responsible for collecting the raw messages from some of the Argo floats, decoding them, quality-controlling them and distributing the data.
The Coriolis data centre is also one of the two Global Data Assembly Centres (GDACs) for the Argo program. It collects the Argo data from the other DACs and serves as a distribution point for all Argo data. Every day, the Coriolis data centre collects XBT, CTD and XCTD data from French and some European research ships, as well as from the global telecommunication system (GTS, www. wmo. int), the GOSUD project (Global Ocean Surface Underway Data, www. gosud. org), Voluntary Observing Ships project (VOS, www. vos. noaa. gov/vos scheme. shtml), moorings -in particular TAO (Tropical Atmosphere Ocean)/TRITON and PIRATA data from PMEL (Pacific Marine Environmental Laboratory) -gliders (www. ego-network. org), and from sea mammals equipped with CTD by French (Centre d' Études Biologiques de Chizé -CEBC -Chizé Centre for Biological Studies, http:// www.
cebc. cnrs. fr) and other European Union data providers (through the British Oceanographic Data Centre -BODCand Sea Mammal Research Unit -SMRU). Three times a week, the Coriolis data centre also uploads GTS and GTSPP (Global Temperature and Salinity Profile Programme) files prepared by the Canadian Marine Environmental Data Service (MEDS, www. meds-sdmm. dfo-mpo. gc. ca/isdm-gdsi/ index-eng. html). Delayed mode CTD data are regularly uploaded from the World Ocean Database (WOD09, high resolution CTD data,  Boyer et al. , 2009).
Organisation of the CORA3 dataset The CORA3 dataset consists of all the in-situ temperature and salinity profiles extracted at a given time from the Coriolis database, a database which is itself updated daily in real time. For CORA3, which covers the time period 1990 to 2010, the dates of data retrieval were 25 May 2010 for the 1990-2008 period, 9 September 2010 for 2009, and 22 March 2011 for 2010. CORA3 contains data from different instrument types: mainly Argo floats, XBT, CTD and XCTD, moorings, sea mammal data, and some drifting buoys. The data are classified into 7 types, depending mainly on the data source and resolution. Most of these data types are those defined for the GTSPP (PF, CT, XB, BA and TE), while others are "in-house" types (OC and MO). All the data are stored in netcdf files using the same format as that defined for the Argo program, and a naming convention that indicates the data type: PF files are used for Argo data obtained from the DACs; XB files are used for shipboard XBT or XCTD data; CT files are used for shipboard CTD data and CTD data from sea mammals and some sea gliders; OC files are used for CTD and XCTD data from WOD2009; MO files are used for mooring data from TAO/TRITON, RAMA and PIRATA arrays from PMEL; finally, TE and BA files are used for TESAC (TEmperature, SAlinity, Currents) and BATHY (Bathythermograph) data received from the GTS, respectively.
However, it can be difficult for users to find all the data from one kind of instrument or probe (e. g. CTD) since it is stored in different types of files (e. g. CT, OC, and TE files for CTD instruments). An effort has therefore been made to identify the "probe type" among the different file types. Eleven "probe types" are defined and a probe type code associated with each profile of the dataset (in an index file). Table  1  gives temperature, salinity and depth/pressure accuracies for each "probe type" as well as the type of the netcdf files where the data are stored.
Data coverage The CORA3 dataset spans the period 1990-2010. The year 1990 was chosen as a starting point because the Coriolis database does not contain global data before this year. Moreover, reanalysis projects, such as GLORYS, do not go back any further than the early 1990s because they assimilate satellite altimetry. The year 2010 was chosen as the end because it was the last full year available at the time of completing much of the work on CORA3 (i. e. during the year 2011). Table  1. Accuracies of the different data types included in CORA3. The type of netcdf files where each data type can be found is also listed (in italic for the most frequent occurrences). Note that data received from the GTS are not full resolution: data are truncated two places after the decimal point for the TESAC (TE) type and one place after the decimal point for the BATHY (BA) type.
2009 ); e for NDBC buoys,  Conlee and Moersdorf (2005). Figure  1  shows global data coverage for two time periods in the CORA3 dataset: the pre-Argo era in 1990-1999 and the period 2000-2010, during which Argo profiles progressively spread across the global ocean (a near-global scale of coverage was reached in 2005). In the 1990s, high coverage (mostly XBTs) was concentrated on the main shipping lanes. Large gaps are seen in the Southern Ocean, south of 30 • S, even though this region was relatively thoroughly sampled during this period because of the World Ocean Circulation Experiment (WOCE) program in 1990-1998. During the more recent period  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) , the spreading of Argo profiles ensured a minimum coverage of 1-2 profiles per year per 1 • square box (this reached 3-4 profiles per year per 1 • square box after the target of 3000 Argo floats had been met by the end of 2007).
Ice-covered or shallow-depth regions are less densely sampled. Regions with more than 10 profiles a year are found in the west side of the North Pacific Ocean, along the US and Canadian east coasts and west coasts of Europe. Some areas in the Southern Ocean are also highly sampled by sea mammals equipped with CTD (first data in 2004). Figure  2  shows the number of temperature and salinity profiles per month at a given depth in CORA3. Before the year 2000, salinity profiles were essentially from CTDs and often reached down to 3000 m depth. Since 2000, the number of temperature and salinity profiles reaching down to 2000 m depth has gradually increased as a result of the growth of the Argo program, but at the same time, the number of deeper CTD profiles has been significantly reduced. Since 2004-2005, Argo data has been the main source of global subsurface measurements in the CORA3 dataset.
However, the subsurface salinity data from these moorings were not included in the Coriolis database and CORA3 before 2003, even though some of the buoys measured subsurface salinity before this date. Figure  3  shows the number of profiles in CORA3 divided by data type as a function of time. The number of profiles increases after 2001 as the Coriolis data centre was connected to real-time data streams. It can be noted that, in 2000, there is a gap in the acquisition of TAO/TRITON and PIRATA data. This gap and the subsurface salinity gap (before 2003) could have been filled by directly sourcing data from PMEL. However, sourcing data from places other than the Coriolis database compromises our ability to release an update of the CORA dataset every year. Our general approach was first to download data into the Coriolis database and then to update the CORA dataset.
CORAdata processing Figure  4  is a diagram of the different stages used for processing the CORA3 dataset. The overall process includes checking for duplicates to ensure that data is unique in the dataset, various quality checks that help differentiate "bad" from "correct" data, and adjustments applied to parameters. These different steps are detailed in the following subsections. Some are processed in real or near-real time and applied to the data freshly downloaded into the Coriolis database; others are processed in delayed time (when the CORA3 files are generated).
Checking for duplicate profiles Identical profiles can be found with several occurrences in the Coriolis database because different paths can be used to transmit data from sensors to the data centre. Although a duplicate check is performed in real time at the Coriolis data centre, some duplicate profiles slip through. The duplicate check is run when the data are collected, but, because it is a real-time process, the duplicate check procedure occasionally fails and then does not run for a short while. Moreover, the duplicate check program run at the Coriolis centre has evolved, but new versions are not systematically run on data already loaded onto the Coriolis database. A new duplicate check was therefore performed on the whole CORA3 dataset. The duplicate check run in delayed time looks for pairs within 0.
Among them, some are exact duplicates, some are duplicates but data or metadata differ slightly because they went through different processing, and others are not duplicates (e. g. an Argo profile and a CTD profile made when the float was launched). Hence, some pairs are systematically excluded from being possible duplicates unless the data are strictly identical (e. g. Argo and CTD or XBT profiles, CTD and XBT profiles, mooring and CTD or XBT profiles). It is also verified that the values of the two profiles selected as duplicates are not too different (0. 4 • C for the mean T and 0. 5 PSU for the mean S). If this is the case, the pair is excluded from the possible duplicate list. The choice is then made of which record should be kept in the CORA3 dataset. First, there is a preference for records with both temperature and salinity observations.
If this is not decisive, preference is given to the record with the format allowing highest precision (i. e. the GTS formats TE and BA are of lower precision), then to the deepest record with highest vertical resolution. If no choice has yet been possible, the record with less metadata available or the one that appears more often than the other in the list of duplicates is excluded. Finally, if none of those steps is conclusive, an arbitrary decision is made about the deletion of one of the two profiles. In CORA3, 1. 5 % of the profiles had at least one duplicate, most of them having only one. We have removed these duplicates from the dataset. A large proportion of the duplicates found were in the BA and TE files (data from the GTS).
Data validations in real and near-real time Data validations done in real and near-real time are described in  Coatanoan and Petit de la Villéon (2005). Figure  4  shows that most of the data first go through automatic quality checks. These automatic checks are designed for Argo floats (Argo Quality Control Manual,  Wong et al. , 2012) , but are also applied to all other profiles downloaded into the Coriolis database (except for Argo floats managed by other DACs than Coriolis that have been already quality-controlled with the same automatic tests, and sea mammal data that have already been quality-controlled by CEBC). Table  3  summarizes the automatic checks applied, for more details see the Argo Quality Control Manual  (Wong et al. , 2012). These automatic checks set quality flags for each measurement (i.
However, in case of steep bathymetric variations, this latter test can erroneously attribute a flag 4 to a position. Some of the erroneous position flags were corrected during the subsequent visual checks (i. e. reset to flag 1). This test is no longer used. Date flags are attributed during the real-time tests by checking whether the date and time are realistic (Test 2) and whether the platform travel speed between two profiles does not exceed a maximum value defined for each platform type (see Table  3 ). Measurement flags are attributed during the real-time tests by checking overall ranges and regional ranges (only for Mediterranean and Red Seas) by detecting spikes and gradients that are too steep. An automatic test is performed to detect and flag density inversions (Test 14). Until October 2011, data downloaded into the Coriolis database were flagged for density inversion if the in-situ density difference between two adjacent levels was negative.
As a consequence, density inversions were not well detected by the automatic test. This has been corrected and a new density inversion test is now run as described in Version 2. 7 of the Argo Quality Control Manual  (Wong et al. , 2012). However, the data in the CORA3 dataset have only been checked for density inversions with the older test. Visual checks are performed by an operator within 48 h of collection on the data managed by the Coriolis centre (Argo floats from Coriolis DAC, data acquired by French research vessels, gliders), as well as sea mammal data from CEBC. , 2006, and Antonov et al. , 2006). This visual approach is combined with an interactive editor, and quality flags for position, date or measurements can be modified if necessary. 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.
Data validation in delayed time Once the CORA3 files are extracted from the Coriolis database, the data go through delayed-time validation procedures (see Fig. 4 ). Only the data considered as good or probably good (flag 1 or 2) after real-and near-real-time tests or those which have never been checked (flag 0) are further verified in delayed mode (it should be remembered that the CORA3 dataset contains all the data, even those qualified as bad during the previous real-time tests). Procedures for CORA3 validation include simple tests, comparisons with climatology, and tests designed for Argo floats that verify each suspicious float over its entire lifespan. Finally, a model background check based on the global ocean reanalysis GLORYS2V1  (Ferry et al. , 2010)  is applied. A test, when it fails, issues an alert on a profile.
The profile is then visually checked and control quality flags are examined for all measurements at all profile levels and changed when necessary. Control quality flags are not attributed automatically during this validation phase. A profile can fail a simple test for several reasons. This can happen when a pressure value is negative (within instrument accuracy), when a temperature or a salinity value is outside an acceptable range according to depth and region (thresholds are those defined in appendix 9 of the World Ocean Database 2005 documentation,  Johnson et al. , 2006) , when temperature or salinity are equal to zero at the bottom or at the surface, when temperature or salinity values are constant at depth, or if there is a large salinity gradient at the surface (more than 5 PSU within 2 dB).
Density inversion (14) Until October 2011, data downloaded into the Coriolis database was flagged for density inversion if the in-situ density difference between two adjacent levels was negative. Grey list  (15)  Argo float only (see  Wong et al. , 2012). Gross salinity or temperature sensor drift (16) Argo float only (see  Wong et al. , 2012). 18 frozen profile  (18)  This test can detect a float that reproduces the same profile over and over again with very small deviations (see  Wong et al. , 2012). are confirmed after the visual check. The equation is written for the temperature, but it is applied in the same way for the salinity. An example is given in Fig. 5. An offset is visible: the salinity observations are about 0. 5 PSU fresher than the climatological estimate (WOA09), but this is inside the 10σ envelope in the area where the standard deviation is large.
(2009) , of which the results can be found on ftp://ftp. ifremer. fr/ifremer/argo/etc/ argo-ast9-item13-AltimeterComparison/. These floats are verified systematically over their whole life to ensure a homogeneous quality control for all the profiles made by the same float. Profiles of these Argo floats are plotted against the climatology and visualized one by one; each profile is also compared to the previous and following ones. All the profiles from the same float are plotted on a -S diagram and control quality flags are modified if necessary. In the framework of the MyOcean global ocean reanalysis work  (Ferry et al. , 2011) , a close collaboration with the French GLORYS reanalysis project enabled a last step in the quality control of the CORA3 dataset to be performed, based on a model background check.
The global 1/4 • ocean reanalysis GLORYS2V1  (Ferry et al. , 2010 ) is part of the MyOcean "Global Ocean Physics Reanalysis and Reference Simulations" product and assimilated sea surface temperature (SST) and sea level anomaly (SLA). In-situ profiles from a preliminary version of the CORA3 dataset were also assimilated (this preliminary version contained the same profiles as the final version of CORA3 but only covered 1990-2009). The first year of the GLORYS2V1 reanalysis (1993) coincided with the start of the altimetry data from TOPEX/POSEIDON. Once the whole reanalysis was produced, a quality control was made offline that listed suspicious profiles present in the CORA3 dataset. This list was transmitted to the Coriolis data centre in order to check its contents and, if necessary, flag observations in the final CORA3 dataset.
Below, we describe the method used to detect those suspicious profiles. Based on this 17-yr-long reanalysis, "innovation" (i. e. observation minus model background) statistics for in-situ temperature and salinity profiles were collected and used to detect suspicious profiles and provide a black list of observations present in CORA3 dataset. This observation screening is known as background quality control. The probability density functions (PDFs) of the innovations are calculated as a function of spatial location (x, y, z) in the global ocean. We find that in most places, innovation PDFs are very close to a normal distribution. Therefore, we assume that innovations have a Gaussian distribution and that the tails of the probability density function contain suspicious observations. First, the collected innovations are binned on a 5 • × 5 • grid on the horizontal, the model vertical grid, and the season.
This happened until 2001 with the strong La Niña. This is attributed to the fact that the threshold values defined for the quality control salinity may be underestimated because the statistics may not contain enough ENSO events to fully sample the ocean variability. In 1998, only 30 % of the suspicious profiles identified with this background quality control were confirmed to have at least one bad measurement. Finally, in the CORA3 dataset, quality flags were only modified for those measurements that were confirmed to be bad after visual check. Figure  9  shows the spatial distribution of suspicious temperature and salinity profiles in 2009. The reasons for erroneous measurements are numerous. They can be due to a sensor (pressure, temperature, salinity) defect or ageing, or may result from transmission errors.
These reasons are a priori independent from the profile location. Therefore, erroneous profiles are expected to be randomly distributed in space, which is almost the case. However, we can see some places where there are concentrations of points (e. g. in the central tropical Pacific or east of the Philippines) corresponding to a moving Argo float with defective sensors. An example of suspicious profiles and their impact on an ocean analysis can be found in  Lellouche et al. (2012). For the years 1993 to 2009, the background quality control allowed identification of 2760 suspicious temperature and salinity profiles, which were reported to Coriolis to improve the CORA3 dataset. All these profiles were then visually checked and about 50 % of them were confirmed to have at least one bad measurement.
Data corrections The CORA3 dataset not only contains the raw parameters such as temperature, salinity, pressure or depth, as received from the instrument, it can also include adjusted parameters, i. e. temperature, salinity, pressure or depth corrected from a drift or offset. The data types affected by these adjustments are Argo floats and XBTs. For Argo data, it is the responsibility of each DAC to provide data corrections both in real time and delayed mode. The Coriolis data centre, as a GDAC, gathers these corrections and stores raw and adjusted parameters in the Coriolis database. No supplementary correction has been made or applied to the Argo data in the CORA3 dataset. On the contrary, corrections for XBT data were calculated as part of CORA3 data processing. The method is described in Sect.