Quality control of time-series seawater temperature and wave data adapted to the regional conditions of the Baltic Sea
DOI:
https://doi.org/10.26881/oahs-2025.1.06Keywords:
quality control, long-series data, seawater temperature, wave, Baltic SeaAbstract
Marking outliers using available methods for identifying such observations should be a standard practice in the database management process. The research aimed to adapt universal data quality control tools and tests to their applicability in the southern Baltic Sea by setting new limit values, enabling the detection of erroneous or suspicious data, which can be subjected to expert verification at a later stage. This verification stage may include analysing current conditions and processes and determining the values measured at a given time and space. Our research has proven that using global algorithms requires adapting the limit values based on regional conditions. The global quality control tests, such as the spike test, Dixon’s 4(σ), Q-Dixon, Hampel, quartile, and gradient tests for oceanographic data, were examined, and their application in the research area was verified. As part of the task, a few tests were conducted in various areas of the Baltic Sea, and they were modified to adapt them to the research results of the southern Baltic Sea. Depending on the methodology adopted, verification tests result in the selection of suspicious observations, enabling their expert assessment and the final qualification of measurements that may be considered outliers.
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References
Baltic Sea Physics Reanalysis. E.U. Copernicus Marine Service Information (CMEMS). Marine Data Store (MDS). https:// doi.org/10.48670/moi-00014 [accessed December 14, 2023].
Bitner-Gregersen, E. M., & de Valk, C. (2008). Quality control issues in estimating wave climate from hindcast and satellite data. International Conference on Offshore Mechanics and Arctic Engineering, June 15–20, 2008.
Estoril, Portugal (Vol. 48197, pp. 819–827) https://doi. org/10.1115/OMAE2008-57865.
Boyer, T. P., & Levitus, S. (1994). Quality control and processing of historical oceanographic temperature, salinity, and oxygen data (Vol. 81). US Department of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service.
Budka, A., Kayzer, D., Pietruczuk, K., & Szoszkiewicz, K. (2013). Zastosowanie wybranych procedur do wykrywania obserwacji nietypowych w ocenie jakości rzek. Infrastruktura i Ekologia Terenów Wiejskich, (3/II).
Castelão, G. P. (2015). A flexible system for automatic quality control of oceanographic data. https://doi.org/10.1016/j. cageo.2021.104803.
Chromiński, K., & Tkacz, M. (2010). Comparison of outlier detection methods in biomedical data. Journal of Medical Informatics & Technologies, 16, 89–94.
Copernicus Team. (2017). Copernicus in situ TAC, real time quality control for WAVES. Copernicus in situ TAC.
Copernicus Marine in Situ tac Data Management Team (2018). Copernicus Marine In Situ TAC - physical parameters list. Copernicus Marine In Situ TAC. https://doi. org/10.13155/53381. Copernicus Team. (2020). Copernicus in situ TAC, real time quality control for WAVES [accessed: 28.08.2024].. CMEMSINS-WAVES-RTQC. DATAWELL. 2020. Datawell Manuals [Online] [accessed: 28.08.2024].. http://www.datawell. nl/Support/Documentation/Manuals.aspx [accessed: 28.08.2024].
Cummings, J. A. (2011). Ocean data quality control. Operational oceanography in the 21st Century, 91–121. (Chapter), Springer Nature, ISBN : 978-94-007-0331-5.
Doong, D. J., Chen, S. H., Kao, C. C., Lee, B. C., & Yeh, S. P. (2007). Data quality check procedures of an operational coastal ocean monitoring network. Ocean Engineering, 34(2), 234–246. https://doi.org/10.1016/j.oceaneng.2006.01.011.
FINO. (2020). The FINO project website [Online]. https://www. fino-offshore.de/de/. [accessed: 28.08.2024].
Good, S. A., Martin, M. J., & Rayner, N. A. (2013). EN4: Quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates. Journal of Geophysical Research: Oceans, 118(12), 6704–6716. https://doi.org/10.1002/2013JC009067.
Good, S., Mills, B., Boyer, T., Bringas, F., Castelão, G., Cowley, R., Goni, G., Gouretski, V., & Domingues, C. M. (2023). Benchmarking of automatic quality control checks for ocean temperature profiles and recommendations for optimal sets. Frontiers in Marine Science, 9, 1075510. https://doi.org/10.3389/fmars.2022.1075510.
GOSUD real time QC procedures, version 1.0. (2003). http:// www.coriolis.eu.org/cdc/quality_control.htm GTSPP real-time quality control manual, First Revised Edition.
UNESCO-IOC 2010. (IOC manuals and guides No. 22, Revised Edition.) (IOC/2010/MG/22Rev.) by the United Nations Educational, Scientific and Cultural Organization 7, Place de Fontenoy, 75352, Paris 07 SP UNESCO 2010.
Gurova, E., Lehmann, A., & Ivanov, A. (2013). Upwelling dynamics in the Baltic Sea studied by a combined SAR/ infrared satellite data and circulation model analysis. Oceanologia, 55(3), 687–707. https://doi.org/10.5697/ oc.55-3.687.
Ingleby, B., & Huddleston, M. (2007). Quality control of ocean temperature and salinity profiles – Historical and real-time data. Journal of Marine Systems, 65(1–4), 158–175. https:// doi.org/10.1016/j.jmarsys.2005.11.019.
IOC. (1993). Manual of quality control procedures for validation of oceanographic data. Manuals and guides (Vol. 26). https://unesdoc.unesco.org/ark:/48223/pf0000138825. locale=fr.
IOOS. (2019). Manual for real-time quality control of in-situ surface wave data. In QARTOD (Ed. M. Bushnell), A guide to quality control and quality assurance of in-situ surface wave observations. Version 2.1.
Kennedy, J. J. (2014). A review of uncertainty in in situ measurements and data sets of sea surface temperature. Reviews of Geophysics, 52(1), 1–32. https://doi. org/10.1002/2013RG000434.
Lellouche, J. M., Le Galloudec, O., Drévillon, M., Régnier, C., Greiner, E., Garric, G., Ferry, N., Desportes, C., Testut, C.-E., Bricaud, C., Bourdallé-Badie, R., Tranchant, B., Benkiran, M., Drillet, Y., Daudin, A., & De Nicola, C. (2013). Evaluation of global monitoring and forecasting systems at Mercator Océan. Ocean Science, 9(1), 57–81. https://doi.org/10.5194/ os-9-57-2013.
Leppäranta, M., & Myrberg, K. (red.). (2009). Physical oceanography of the Baltic Sea. Springer.
Min, Y., Jeong, J., Shim, J. S., & Do, K. (2017). Quality enhancement of wave data observed by Radar at the Socheongcho Ocean Research Station. Journal of Coastal Disaster Prevention, 4(4), 189–196. https://doi. org/10.20481/kscdp.2017.4.4.189.
Morang, A. (1990). Quality Control and Management of Oceanographic Wave-gage Data. US Army Engineer Waterways Experiment Station.
Namieśnik, J., Konieczka, P., & Zygmunt, B. (2007). Ocena i kontrola jakości wyników pomiarów analitycznych. WNT, Warszawa, 225–299.
NORTEK. (2020). Manuals & quick guides [Online]. https:// w w w. n o r t e k g r o u p. c o m / m a n u a l s - q u i c k - g u i d e s [[accessed: 28.08.2024].].
Peterson, T. C., Vose, R., Schmoyer, R., & Razuvaëv, V. (1998). Global Historical Climatology Network (GHCN) quality control of monthly temperature data. International Journal of Climatology: A Journal of the Royal Meteorological Society, 18(11), 1169–1179.
RADAC. (2020). Documentation [Online]. Available: https:// radac.nl/documentation/ [accessed: 28.08.2024].].
Salcedo Parra, O. J., Puente, F. J., & Ortiz, R. V. (2008) Oceanographic Data Quality Control By Means Of A Computational Tool. Vol. 13 No. 1/2008: January – June https://doi.org/10.14483/23448393.2091.
SeaDataNet. Data quality control procedures. Version 2.0. May 2010. https://www.seadatanet.org/content/ download/596/file/SeaDataNet_QC_procedures_ V2_%28May_2010%29.pdf.
Svendsen, L. M., Barnich, J., Boutrup, S., Enne, P., Gustafafsson, B., Frank-Kamenetsky, D., Haapaniemi, J., Knuuttila, S., Koch, D., Kokorite, I., Larsen, S. E., Oblomkova, N., Plunge, S., Raike, A., Sokolov, A., Sonesten, L., & Ullrich, A. (2019). HELCOM Guidelines for the annual and periodical compilation and reporting of waterborne pollution inputs to the Baltic Sea (PLC-Water). HELCOM. https://helcom.fi/wpcontent/uploads/2019/08/PLC-Water-Guidelines-2019. pdf .
Team C.M.I.S. (2020). Copernicus in situ TAC, real time quality control for WAVES, Ref. CMEMS-INS-WAVES-RTQC, doi: 10.13155/46607.
Twardowski, K., & Traple, J. (2006). Uwagi dotyczące wątpliwych wyników pomiarów. Wiertnictwo, Nafta, Gaz, 23, 699–714.
Xie, J., Jiang, H., Song, W., & Yang, J. (2023). A novel quality control method of time-series ocean wave observation data combining deep-learning prediction and statistical analysis. Journal of Sea Research, 195, 102439. https://doi. org/10.1016/j.seares.2023.102439.