Quality Assurance of Oceanographic Observations: Standards and Guidance Adopted by an International Partnership

Bushnell, Mark; Waldmann, Christoph; Seitz, Steffen; Buckley, Earle; Tamburri, Mario N.; Hermes, Juliet; Henslop, Emma; Lara‐Lopez, Ana

doi:10.3389/fmars.2019.00706

Cited by 26 publications

(22 citation statements)

References 18 publications

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“…Several processes to obtain high-quality ocean data were followed during the 2000-2004 medgoos cruises in the Sardinia Sea, western Mediterranean. Quality assurance, control, assessment, standard and best practices, defined at international level (see Bushnell et al, 2019) and after decades of practices in ocean data acquisition, were considered during all cruises and are in agreement with recent standardized procedures for all sensors (Hood et al, 2010). Sensors pre and postcalibration, use of redundant ones, comparisons with on-board analyzed water samples showed their efforts resulted in achieving the required accuracy standard for that period, due to sensors accuracies, and for today.…”

Section: Discussionmentioning

confidence: 99%

“…Redundant or secondary sensors were always used for a data quality assessment (as defined in Bushnell et al, 2019) of both temperature and salinity measurements, apart during the cruise medgoos1 in May-June 2000 (see table 1). A secondary SBE-43 sensor for dissolved oxygen was also added just in the last three cruises, from medgoos5 to 7.…”

Section: Instrumentation Technologymentioning

confidence: 99%

“…They changed and were adapted depending by several factors like the working environment, the oceanographic instruments/sensors used, the type of data acquired. These practices included quality assurance, control and assessment, standards and best practices refined till nowadays (Hood et al, 2010;Bushnell et al, 2019;Pearlman et al, 2019). In this paper we describe all the procedures or best practices followed to assure and control the quality of the acquired data during the medgoos cruises, the sensors used, their calibrations and intercomparisons.…”

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confidence: 99%

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Quality assurance and control on hydrological data off western Sardinia (2000–2004), western Mediterranean

Ribotti¹,

Sorgente²,

Borghini³

2020

Preprint

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Abstract. Seven oceanographic cruises in five years were organized in the Sardinia Sea with the repeated collection of physical, chemical and biological data. An accurate and sustained quality assurance on physical sensors was acted through prior and post-cruise calibration and verified during in-situ acquisitions with the use of redundant sensors and other instruments. Moreover, for dissolved oxygen and conductivity, seawater samples at standard depths were frequently analyzed on-board. Then an accurate quality control was used to verify all hydrological data profiles, that passed a further quality check following standard procedures. Finally all hydrological data have been included in two online public open access datasets in the SEANOE repository (https://doi.org/10.17882/59867 and https://doi.org/10.17882/70340, Ribotti et al., 2019a, b). During and after all cruises also chlorophyll and nutrients analyses were carried on but data are not yet open access; the same for water current profiles, both at casts and during vessel moves, and geophysical data. These ocean data are the first covering the whole Sardinia Sea for its whole extension. Here data and assurance/control procedures used are described as they became standards in deep sea acquisitions in the years.

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Section: Discussionmentioning

confidence: 99%

Section: Instrumentation Technologymentioning

confidence: 99%

mentioning

confidence: 99%

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Quality assurance and control on hydrological data off western Sardinia (2000–2004), western Mediterranean

Ribotti¹,

Sorgente²,

Borghini³

2020

Preprint

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“…A large number of classical attempts to support and automate the QC exists (e.g., Behrendt et al, 2018;Simoncelli et al, 2018b;Bushnell et al, 2019;Gourrion et al, 2020 or the IEO CTD Checker) and in the framework of IQuOD 4 new AI methods have been developed (Castelao, 2020). A collaboration with the IQuOD team is ongoing in order to advance jointly at international level on marine data quality and consequent ocean knowledge.…”

Section: Introductionmentioning

confidence: 99%

SalaciaML: A Deep Learning Approach for Supporting Ocean Data Quality Control

et al. 2021

Self Cite

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We present a skillful deep learning algorithm for supporting quality control of ocean temperature measurements, which we name SalaciaML according to Salacia the roman goddess of sea waters. Classical attempts to algorithmically support and partly automate the quality control of ocean data profiles are especially helpful for the gross errors in the data. Range filters, spike detection, and data distribution checks remove reliably the outliers and errors in the data, still wrong classifications occur. Various automated quality control procedures have been successfully implemented within the main international and EU marine data infrastructures (WOD, CMEMS, IQuOD, SDN) but their resulting data products are still containing data anomalies, bad data flagged as good and vice-versa. They also include visual inspection of suspicious measurements, which is a time consuming activity, especially if the number of suspicious data detected is large. A deep learning approach could highly improve our capabilities to quality assess big data collections and contemporary reducing the human effort. Our algorithm SalaciaML is meant to complement classical automated quality control procedures in supporting the time consuming visually inspection of data anomalies by quality control experts. As a first approach we applied the algorithm to a large dataset from the Mediterranean Sea. SalaciaML has been able to detect correctly more than 90% of all good and/or bad data in 11 out of 16 Mediterranean regions.

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“…The study area is limited to between 38 and 42 • N in latitude and between 7 • E and the western Sardinian coast in longitude, with an offshore bottom reaching a depth of 2950 m in the abyssal plain. The shelf extends from 5.5 km in the north to 37 km in its center with a shelf break at about a 200 m depth (Conforti et al, 2016;Brambilla et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Quality assurance and control on hydrological data off western Sardinia (2000–2004), western Mediterranean

Ribotti

Sorgente

Borghini

2020

Earth Syst. Sci. Data

View full text Add to dashboard Cite

Abstract. Seven oceanographic cruises in 5 years were organized in the Sea of Sardinia with the repeated collection of physical, chemical and biological data. An accurate and sustained quality assurance on physical sensors was enacted through pre- and postcruise calibration and verified during in situ acquisitions with the use of redundant sensors and other instruments. Moreover, for dissolved oxygen and conductivity, seawater samples at standard depths were frequently analyzed on board. Then an accurate quality control was used to verify all hydrological data profiles, which passed a further quality check following standard procedures. Finally all hydrological data have been included in two online public open-access datasets in the SEANOE (SEA scieNtific Open data Edition) repository (https://doi.org/10.17882/59867 and https://doi.org/10.17882/70340; Ribotti et al., 2019a, b). During and after all cruises Chlorophyll a and nutrient analyses were also carried out, but these data are not yet open access; the same applies for water current profiles, both at casts and during vessel moves, and geophysical data. These ocean data are the first covering the Sea of Sardinia for its whole extension. Here data and assurance and control procedures used are described as they have become standards in deep-sea acquisitions over the years.

show abstract

Quality Assurance of Oceanographic Observations: Standards and Guidance Adopted by an International Partnership

Cited by 26 publications

References 18 publications

Quality assurance and control on hydrological data off western Sardinia (2000–2004), western Mediterranean

Quality assurance and control on hydrological data off western Sardinia (2000–2004), western Mediterranean

SalaciaML: A Deep Learning Approach for Supporting Ocean Data Quality Control

Quality assurance and control on hydrological data off western Sardinia (2000–2004), western Mediterranean

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