Delayed-mode Quality Control Research Articles

Filtering method based on cluster analysis to avoid salinity drifts and recover Argo data in less time

Abstract. Currently there is a huge amount of freely available hydrographic data, and it is increasingly important to have easy access to it and to be provided with as much information as possible. Argo is a global collection of around 4000 active autonomous hydrographic profilers. Argo data go through two quality processes, real time and delayed mode. This work shows a methodology to filter profiles within a given polygon using the odd–even algorithm; this allows analysis of a study area, regardless of size, shape or location. The aim is to offer two filtering methods and to discard only the real-time quality control data that present salinity drifts. This takes advantage of the largest possible amount of valid data within a given polygon. In the study area selected as an example, it was possible to recover around 80 % in the case of the first filter that uses cluster analysis and 30 % in the case of the second, which discards profilers with salinity drifts, of the total real-time quality control data that are usually discarded by the users due to problems such as salinity drifts. This allows users to use any of the filters or a combination of both to have a greater amount of data within the study area of their interest in a matter of minutes, rather than waiting for the delayed-mode quality control that takes up to 12 months to be completed. This methodology has been tested for its replicability in five selected areas around the world and has obtained good results.

Delayed-Mode Quality Control of Oxygen, Nitrate, and pH Data on SOCCOM Biogeochemical Profiling Floats

The Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) project has deployed 194 profiling floats equipped with biogeochemical (BGC) sensors, making it one of the largest contributors to global BGC-Argo. Post-deployment quality control (QC) of float-based oxygen, nitrate, and pH data is a crucial step in the processing and dissemination of such data, as in situ chemical sensors remain in early stages of development. In situ calibration of chemical sensors on profiling floats using atmospheric reanalysis and empirical algorithms can bring accuracy to within 3 μmol O2 kg–1, 0.5 μmol NO3– kg–1, and 0.007 pH units. Routine QC efforts utilizing these methods can be conducted manually through visual inspection of data to assess sensor drifts and offsets, but more automated processes are preferred to support the growing number of BGC floats and reduce subjectivity among delayed-mode operators. Here we present a methodology and accompanying software designed to easily visualize float data against select reference datasets and assess QC adjustments within a quantitative framework. The software is intended for global use and has been used successfully in the post-deployment calibration and QC of over 250 BGC floats, including all floats within the SOCCOM array. Results from validation of the proposed methodology are also presented which help to verify the quality of the data adjustments through time.

Accuracy and Long-Term Stability Assessment of Inductive Conductivity Cell Measurements on Argo Floats

AbstractThis study demonstrates the long-term stability of salinity measurements from Argo floats equipped with inductive conductivity cells, which have extended float lifetimes as compared to electrode-type cells. New Argo float sensor payloads must meet the demands of the Argo governance committees before they are implemented globally. Currently, the use of CTDs with inductive cells designed and manufactured by RBR, Ltd., has been approved as a Global Argo Pilot. One requirement for new sensors is to demonstrate stable measurements over the lifetime of a float. To demonstrate this, data from four Argo floats in the western Pacific Ocean equipped with the RBRargo CTD sensor package are analyzed using the same Owens–Wong–Cabanes (OWC) method and reference datasets as the Argo delayed-mode quality control (DMQC) operators. When run with default settings against the standard DMQC Argo and CTD databases, the OWC analysis reveals no drift in any of the four RBRargo datasets and, in one case, an offset exceeding the Argo target salinity limits. Being a statistical tool, the OWC method cannot strictly determine whether deviations in salinity measurements with respect to a reference hydrographic product (e.g., climatologies) are caused by oceanographic variability or sensor problems. So, this study furthermore investigates anomalous salinity measurements observed when compared with a reference product and demonstrates that anomalous values tend to occur in regions with a high degree of variability and can be better explained by imperfect reference data rather than sensor drift. This study concludes that the RBR inductive cell is a viable option for salinity measurements as part of the Argo program.

XBT, ARGO Float and Ship-Based CTD Profiles Intercompared under Strict Space-Time Conditions in the Mediterranean Sea: Assessment of Metrological Comparability

Accurate measurement of temperature and salinity is a fundamental task with heavy implications in all the possible applications of the currently available datasets, for example, in the study of climate changes and modeling of ocean dynamics. In this work, the reliability of measurements obtained by oceanographic devices (eXpendable BathyThermographs, Argo floats and Conductivity-Temperature-Depth sensors) is analyzed by means of an intercomparison exercise. As a first step, temperature profiles from XBT probes, deployed by commercial ships crossing the Ligurian and Tyrrhenian seas during the Ship of Opportunity Program (SOOP), were matched with profiles from Argo floats quasi-collocated in space and time. Attention was then paid to temperature/salinity profiling Argo floats. Since Argo floats usually are not recovered and should last up to five years without any re-calibration, their onboard sensors may suffer some drift and/or offset. In the literature, refined methods were developed to post-process Argo data, in order to correct the response of their profiling CTD sensors, in particular adjusting the salinity drift. The core of this delayed-mode quality control is the comparison of Argo data with reference climatology. At the same time, the experimental comparison of Argo profiles with ship-based CTD profiles, matched in space and time, is still of great importance. Therefore, an overall comparison of Argo floats vs. shipboard CTDs was performed, in terms of temperature and salinity profiles in the whole Mediterranean Sea, under space-time matching conditions as strict as possible. Performed analyses provided interesting results. XBT profiles confirmed that below 100 m depth the accordance with Argo data is reasonably good, with a small positive bias (close to 0.05 °C) and a standard deviation equal to about 0.10 °C. Similarly, side-by-side comparisons vs. CTD profiles confirmed the good quality of Argo measurements; the evidence of a drift in time was found, but at a level of about E−05 unit/day, so being reasonably negligible on the Argo time-scale. XBT, Argo and CTD users are therefore encouraged to take into account these results as a good indicator of the uncertainties associated with such devices in the Mediterranean Sea, for the analyzed period, in all the climatological applications.

Argo real-time quality control intercomparison

The real-time quality control (RTQC) methods applied to Argo profiling float data by the United Kingdom (UK) Met Office, the United States (US) Fleet Numerical Meteorology and Oceanography Centre, the Australian Bureau of Meteorology and the Coriolis Centre are compared and contrasted. Data are taken from the period 2007 to 2011 inclusive and RTQC performance is assessed with respect to Argo delayed-mode quality control (DMQC). An intercomparison of RTQC techniques is performed using a common data set of profiles from 2010 and 2011. The RTQC systems are found to have similar power in identifying faulty Argo profiles but to vary widely in the number of good profiles incorrectly rejected. The efficacy of individual QC tests are inferred from the results of the intercomparison. Techniques to increase QC performance are discussed.

Drift in Salinity Data from Argo Profiling Floats in the Sea of Japan*

Abstract In drift analysis of salinity sensors, one major problem is the difficulty in delineating sensor drift from water mass changes. In the present study, a new method is proposed for finding sensor drift that is free from water mass changes. The efficiency of this new method in finding out possible drift in the Argo salinity is demonstrated in the Sea of Japan (SOJ) by using the “near-linear” subsurface salinity structure of the SOJ. The new method is based on the time–space decorrelation scale. The salinity difference between two neighboring observations within the time–space decorrelation scale (SALD) is used to find out possible drift. Neighboring observations within the time–space decorrelation scale are referred to as matchups. The SALD derived from matchups between Argo floats and shipboard CTD observations from the SOJ shows linear drift. Although all four selected floats (5 yr completed) from the SOJ show linear drift (<0.001 PSS yr−1), the drift alone is not so significant to affect the objective of the Argo program in understanding climate variability. In the SOJ, SALD identified salinity error other than drift in good quality data that are flagged by the Argo delayed-mode quality control (ADMQC) method. Therefore, SALD could be used as an effective additional tool in the Argo data quality control. To examine the applicability of SALD in open ocean regions, in addition to confined basins such as SOJ, SALD was applied successfully to detect salinity error in Argo data from the subtropical North Pacific (SNP).

Several Important Issues in Salinity Quality Control of Argo Float

Several floats that deployed by different countries during the last years over the global ocean, which served as a contribution to the Array for real time geostrophic oceanography (Argo), are studied in this paper with respect to their uniqueness in the Delayed Mode Quality Control (DMQC) process. Of these floats, issues were found in the temperature-salinity relationships during the DMQC, which would easily be misinterpreted or overlooked. This study lists and summarizes the possible situations of the floats that might confront in their observation life, together with the reasonable explanations. The authors present some thoughts on evaluating the performance of the conductivity sensors and calibrating the float salinity data.

태평양 Argo 자료의 지연모드 품질관리 및 검증연구

Quality control of Argo(Array for Real-time Geostrophic Oceanography) data is crucial by reason that salinity measurements are liable to experience some drift and offset due to biofouling, contamination of sensor and wash-out of biocide. The automated Argo real-time quality control has a limit of sorting data quality, so that WJO program is adopted as standardized method of Argo delayed mode quality control (DMQc) in the world that is a precise quality control method. We conducted DMQC on pressure, temperature and salinity measured by Argo floats in the Pacific Ocean including expert evaluation. Particularly, salinity data were corrected using WJO program. 4 salinity profiles of Argo delayed mode were compared with nearby in situ CTD data and other Argo data in deep layer where oceanographic conditions are stable in time and space. The differences of both salinities were lower than target accuracy of Argo. As compared with the difference of salinities before DMQC, those after DMQC decreased by 60-80 percent. Quality of delayed mode salinity data seemed to be improved correcting salinity data suggested by WJO program.

The Real-Time Data Management System for Argo Profiling Float Observations

Abstract Argo is an internationally coordinated program directed at deploying and maintaining an array of 3000 temperature and salinity profiling floats on a global 3° latitude × 3° longitude grid. Argo floats are deployed from research vessels, merchant ships, and aircraft. After launch they sink to a prescribed pressure level (typically 1000–2000 dbar), where most floats remain for 10 days. The floats then return to the surface, collecting temperature and salinity profiles. At the surface they transmit the data to a satellite and sink again to repeat the cycle. As of 10 August 2006 there are 2489 floats reporting data. The International Argo Data Management Team oversees the development and implementation of the data management protocols of Argo. Two types of data systems are active—real time and delayed mode. The real-time system receives the transmissions from the Argo floats, extracts the data, checks their quality, and makes them available to the users. The objective of the real-time system is to provide Argo profiles to the operational and research community within 24 h of their measurement. This requirement makes it necessary to control the quality of the data automatically. The delayed-mode quality control is directed at a more detailed look at the profiles using statistical methods and scientific review of the data. In this paper, the real-time data processing and quality-control methodology is described in detail. Results of the application of these procedures to Argo profiles are described.

Importance of Reference Dataset Improvements for Argo Delayed-Mode Quality Control

For the Argo Project, monitoring the global upper ocean by a large number of profiling floats, maintaining the quality of salinity data is critical; the goal for measurement accuracy is ±0.01. Experiments using the method of Wong et al. (2003), the standard delayed-mode quality control (dQC) for the Project, show that its performance depends critically on the reference datasets used. This study concludes that the method is useful for Argo and has sufficient potential to achieve the goal for salinity measurement in the North Pacific, when suitable reference datasets are prepared. Considering the Wong et al. (2003) algorithms, we suggest that reference datasets with the following characteristics will be most suitable for Argo dQC: They should be basically derived from the most extensive datasets, such as the latest World Ocean Database; in regions with denser observations, datasets with carefully quality controls should be used; in the regions with subsurface temperature inversions, such as the subarctic North Pacific, the profiles used for the reference must extend below the deepest temperature maximum to prepare proper salinities for the deep layer reference.

Delayed-Mode Calibration of Autonomous CTD Profiling Float Salinity Data byθ–SClimatology*

Autonomous CTD profiling floats are free-moving floats that report vertical profiles of salinity, temperature, and pressure at regular time intervals. The Argo program plans to deploy 3000 such floats to observe the upper 2000 m of the global ocean. These floats give good measurements of temperature and pressure, but salinity measurements may experience significant sensor drifts with time. The moving nature of these floats means that it is too expensive to retrieve them regularly for physical calibrations. Thus a system has been set up to correct the drift in these profiling float salinity data by using historical hydrographic data. An objective mapping technique is used to estimate the background climatological salinity field onu surfaces from nearby historical data. Temporal variations in water mass properties are accounted for in the objective estimate. The float salinity data are fitted to the background climatology in potential conductivity space by weighted least squares with a time-varying slope. The error associated with estimating the background climatology is carried through in the weighted least squares calculations. The result is a set of calibrated salinity data with error estimates. Because of the need to accumulate a time series for calculating a stable slope correction term, this system is a delayed-mode quality control system, with reliable calibrations available a few months after float data are obtained. However, contemporary ship-based measurements are essential in determining whether a measured trend is due to sensor drift or due to natural variability.