Advertisement

Journal of Oceanology and Limnology

, Volume 37, Issue 6, pp 1983–1993 | Cite as

Quality control of marine big data—a case study of real-time observation station data in Qingdao

  • Chengcheng Qian
  • Aichao LiuEmail author
  • Rui Huang
  • Qingrong Liu
  • Wenkun Xu
  • Shan Zhong
  • Le Yu
Article

Abstract

Offshore waters provide resources for human beings, while on the other hand, threaten them because of marine disasters. Ocean stations are part of offshore observation networks, and the quality of their data is of great significance for exploiting and protecting the ocean. We used hourly mean wave height, temperature, and pressure real-time observation data taken in the Xiaomaidao station (in Qingdao, China) from June 1, 2017, to May 31, 2018, to explore the data quality using eight quality control methods, and to discriminate the most effective method for Xiaomaidao station. After using the eight quality control methods, the percentages of the mean wave height, temperature, and pressure data that passed the tests were 89.6%, 88.3%, and 98.6%, respectively. With the marine disaster (wave alarm report) data, the values failed in the test mainly due to the influence of aging observation equipment and missing data transmissions. The mean wave height is often affected by dynamic marine disasters, so the continuity test method is not effective. The correlation test with other related parameters would be more useful for the mean wave height.

Keyword

quality control real-time station data marine big data Xiaomaidao Station marine disaster 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ingleby B, Huddleston M. 2007. Quality control of ocean temperature and salinity profiles—historical and real-time data. J. Marine Syst., 65(1–4): 158–175,  https://doi.org/10.1016/j.jmarsys.2005.11.019.CrossRefGoogle Scholar
  2. Kearns E, Woody C, Bushnell M. 2004. QARTOD-I Report. First Workshop Report on the Quality Assurance of Real-Time Ocean Data. December 3–5, 2003. National Data Buoy Center, NWS/NOAA, Stennis Space Center, MS. 89pp,  https://doi.org/10.25607/OBP-380. Accessed on 2018-04-23.Google Scholar
  3. Li X K, Li F J. 1997. Marine hydro-meteorological real-time data quality control. Mar. Forecasts, 14(3): 71–79. (in Chinese)Google Scholar
  4. Lorenc A C, Hammon O. 1988. Objective quality control of observations using Bay esian methods: theory, and a practical implementation. Quart. J. Roy. Meteor. Soc., 114(480): 515–543,  https://doi.org/10.1002/qj.49711448012.CrossRefGoogle Scholar
  5. Morello E B, Lynch T P, Slawinski D, Howell B, Hughes D, Timms G P. 2011. Quantitative quality control (QC) procedures for the Australian national reference stations: sensor data. In: Proceedings of Oceans’ 11MTS/IEEE KONA. IEEE, Waikoloa, Hawaii, USA.Google Scholar
  6. National Data Buoy Center. 2009. Handbook of Automated Data Quality Control Checks and Procedures. Stennis Space Center, Mississippi, USA.Google Scholar
  7. NOAA, Integrated Ocean Observing System (IOOS) Program Office. 2008. Data Integration Framework (DIF) Customer Implementation Project Summary and Performance Assessment Plan, Version 1.1. NOAA, IOOS, Quebec City, QC, Canada.Google Scholar
  8. North China Sea Branch of the State Oceanic Administration. 1993. The North China Sea Marine Hydrology and Climate. Ocean Publishing House, Beijing, p.63–190. (in Chinese)Google Scholar
  9. Shi M C, Gao G P, Bao X W. 2008. Methods of Marine Survey. China Ocean University Press, Qingdao, China, p.6–123. (in Chinese)Google Scholar
  10. SOA (State Oceanic Administration, China). 2018. China Marine Disasters Bulletin, http://gc.mnr.gov.cn/201806/t20180619_1798021. html.Accessed on 2018-04-23. (in Chinese)Google Scholar
  11. Thadathil P, Ghosh A K, Pattanaik J, Ratnakaran L. 1998. A quality-control procedure for surface temperature and surface layer inversion in the XBT data archive from the Indian Ocean. J. Atomos. Ocean Technol., 16(7): 980–982,  https://doi.org/10.1175/1520-0426(1999)016<0980AQCPFS>2.0.CO;2.CrossRefGoogle Scholar
  12. Wan Daud W M N. 2010. Quality control for unmanned meteorological stations in Malaysian meteorological department, https://www.wmo.int/pages/prog/www/IMOP/publications/IOM-109_TECO-2012/Session2/P2_01_WanDaud_QC_Unmanned_Meteorological_Stations.pdf Accessed on 2018-04-23.Google Scholar
  13. Xu F, Ignatov A. 2014. In situ SST quality monitor (iQuam). J. Atomos. Ocean. Technol., 31(1): 164–180,  https://doi.org/10.1175/JTECH-D-13-00121.1.CrossRefGoogle Scholar
  14. Xu J, Yu D T, Yuan Z J, Li B, XuZ Z. 2014. Implementation of marine environment monitoring data quality control system. Adv. Mater. Res., 926-930: 4254–4257,  https://doi.org/10.4028/www.scientific.net/AMR.926-930.4254.CrossRefGoogle Scholar
  15. Yang Y, Mao Q S, Wei G H, Dong M M, Dong C. 2017. Quality control methods and application for the oceanic station observed data in the delayed mode. Ocean Dev. Manag., 34(10): 109–113. (in Chinese with English abstract)Google Scholar
  16. Yu T, Han G J, Guan C L, Geng Z G. 2010. Several important issues in salinity quality control of Argo float. Mar. Geod., 33(4): 424–436,  https://doi.org/10.1080/01490419.2010.518496.CrossRefGoogle Scholar

Copyright information

© Chinese Society for Oceanology and Limnology, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Chengcheng Qian
    • 1
    • 3
  • Aichao Liu
    • 1
    Email author
  • Rui Huang
    • 1
  • Qingrong Liu
    • 1
  • Wenkun Xu
    • 2
  • Shan Zhong
    • 1
  • Le Yu
    • 1
  1. 1.North China Sea Marine Forecasting Center of State Oceanic AdministrationQingdaoChina
  2. 2.Qingdao Geotechnical Investigation and Surveying Research InstituteQingdaoChina
  3. 3.Laboratory for Regional Oceanography and Numerical ModelingQingdao National Laboratory for Marine Science and TechnologyQingdaoChina

Personalised recommendations