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dc.contributor.authorGourrion, Jérôme  Concept link
dc.contributor.authorSzekely, Tanguy  Concept link
dc.contributor.authorKillick, Rachel E.  Concept link
dc.contributor.authorOwens, W. Brechner  Concept link
dc.contributor.authorReverdin, Gilles  Concept link
dc.contributor.authorCharpon, Bertrand  Concept link
dc.date.accessioned2020-06-29T21:13:03Z
dc.date.issued2020-05-04
dc.identifier.citationGourrion, J., Szekely, T., Killick, R., Owens, B., Reverdin, G., & Chapron, B. (2020). Improved statistical method for quality control of hydrographic observations. Journal of Atmospheric and Oceanic Technology, 37(5), 789-806.en_US
dc.identifier.urihttps://hdl.handle.net/1912/25918
dc.descriptionAuthor Posting. © American Meteorological Society, 2020. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of the Atmospheric and Oceanic Technology 37(5), (2020): 789-806, doi:10.1175/JTECH-D-18-0244.1.en_US
dc.description.abstractRealistic ocean state prediction and its validation rely on the availability of high quality in situ observations. To detect data errors, adequate quality check procedures must be designed. This paper presents procedures that take advantage of the ever-growing observation databases that provide climatological knowledge of the ocean variability in the neighborhood of an observation location. Local validity intervals are used to estimate binarily whether the observed values are considered as good or erroneous. Whereas a classical approach estimates validity bounds from first- and second-order moments of the climatological parameter distribution, that is, mean and variance, this work proposes to infer them directly from minimum and maximum observed values. Such an approach avoids any assumption of the parameter distribution such as unimodality, symmetry around the mean, peakedness, or homogeneous distribution tail height relative to distribution peak. To reach adequate statistical robustness, an extensive manual quality control of the reference dataset is critical. Once the data have been quality checked, the local minima and maxima reference fields are derived and the method is compared with the classical mean/variance-based approach. Performance is assessed in terms of statistics of good and bad detections. It is shown that the present size of the reference datasets allows the parameter estimates to reach a satisfactory robustness level to always make the method more efficient than the classical one. As expected, insufficient robustness persists in areas with an especially low number of samples and high variability.en_US
dc.description.sponsorshipThis study has been conducted using EU Copernicus Marine Service Information and was supported by the European Union within the EU Copernicus Marine Service In Situ phase-I and phase-II contracts led by Ifremer. The publication was also supported by SOERE CTDO2 in France. The Argo data were collected and made freely available by the International Argo Program and the national programs that contribute to it (see http://www.argo.ucsd.edu, http://argo.jcommops.org). The Argo Program is part of the Global Ocean Observing System (http://doi.org/10.17882/42182). The marine mammal data were collected and made freely available by the International MEOP Consortium and the national programs that contribute to it (see http://www.meop.net; https://doi.org/10.17882/45461). Aleix Gelabert and Dídac Costa were the skippers of the OPOO, sponsored by the Intergovernmental Oceanographic Commission (UNESCO) and Pharmaton. The BWR is a periodic oceanic race organized by the Fundació Navegació Oceànica de Barcelona (FNOB). Reviewer D. Briand provided some useful comments on the final version of the draft paper before submission.en_US
dc.publisherAmerican Meteorological Societyen_US
dc.relation.urihttps://doi.org/10.1175/JTECH-D-18-0244.1
dc.subjectOceanen_US
dc.subjectClimatologyen_US
dc.subjectSalinityen_US
dc.subjectTemperatureen_US
dc.subjectData quality controlen_US
dc.subjectOceanic variabilityen_US
dc.titleImproved statistical method for quality control of hydrographic observationsen_US
dc.typeArticleen_US
dc.description.embargo2020-11-04en_US
dc.identifier.doi10.1175/JTECH-D-18-0244.1
dc.embargo.liftdate2020-11-04


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