Ocean and Polar Research

Korea Institute of Ocean Science & Technology (한국해양과학기술원)
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Evaluation of International Quality Control Procedures for Detecting Outliers in Water Temperature Time-series at Ieodo Ocean Research Station

이어도 해양과학기지 수온 시계열 자료의 이상값 검출을 위한 국제 품질검사의 성능 평가

  • Min, Yongchim (Marine Disaster Research Center, Korea Institute of Ocean Science & Technology) ;
  • Jun, Hyunjung (Marine Disaster Research Center, Korea Institute of Ocean Science & Technology) ;
  • Jeong, Jin-Yong (Marine Disaster Research Center, Korea Institute of Ocean Science & Technology) ;
  • Park, Sung-Hwan (Marine Disaster Research Center, Korea Institute of Ocean Science & Technology) ;
  • Lee, Jaeik (Marine Disaster Research Center, Korea Institute of Ocean Science & Technology) ;
  • Jeong, Jeongmin (Marine Disaster Research Center, Korea Institute of Ocean Science & Technology) ;
  • Min, Inki (Marine Disaster Research Center, Korea Institute of Ocean Science & Technology) ;
  • Kim, Yong Sun (Ocean Circulation Research Center, Korea Institute of Ocean Science & Technology)
  • 민용침 (한국해양과학기술원 해양재난.재해연구센터) ;
  • 전현정 (한국해양과학기술원 해양재난.재해연구센터) ;
  • 정진용 (한국해양과학기술원 해양재난.재해연구센터) ;
  • 박숭환 (한국해양과학기술원 해양재난.재해연구센터) ;
  • 이재익 (한국해양과학기술원 해양재난.재해연구센터) ;
  • 정종민 (한국해양과학기술원 해양재난.재해연구센터) ;
  • 민인기 (한국해양과학기술원 해양재난.재해연구센터) ;
  • 김용선 (한국해양과학기술원 해양순환연구센터)
  • Received : 2021.10.26
  • Accepted : 2021.12.07
  • Published : 2021.12.30
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Abstract

Quality control (QC) to process observed time series has become more critical as the types and amount of observed data have increased along with the development of ocean observing sensors and communication technology. International ocean observing institutions have developed and operated automatic QC procedures for these observed time series. In this study, the performance of automated QC procedures proposed by U.S. IOOS (Integrated Ocean Observing System), NDBC (National Data Buy Center), and OOI (Ocean Observatory Initiative) were evaluated for observed time-series particularly from the Yellow and East China Seas by taking advantage of a confusion matrix. We focused on detecting additive outliers (AO) and temporary change outliers (TCO) based on ocean temperature observation from the Ieodo Ocean Research Station (I-ORS) in 2013. Our results present that the IOOS variability check procedure tends to classify normal data as AO or TCO. The NDBC variability check tracks outliers well but also tends to classify a lot of normal data as abnormal, particularly in the case of rapidly fluctuating time-series. The OOI procedure seems to detect the AO and TCO most effectively and the rate of classifying normal data as abnormal is also the lowest among the international checks. However, all three checks need additional scrutiny because they often fail to classify outliers when intermittent observations are performed or as a result of systematic errors, as well as tending to classify normal data as outliers in the case where there is abrupt change in the observed data due to a sensor being located within a sharp boundary between two water masses, which is a common feature in shallow water observations. Therefore, this study underlines the necessity of developing a new QC algorithm for time-series occurring in a shallow sea.

Keywords

Acknowledgement

이 논문은 2021년 해양수산부의 재원으로 한국해양과학기술진흥원의 지원을 받아 수행된 연구입니다(관할해역 첨단 해양과학기지 구축 및 융합연구). 한국연구재단의 지역해 기후장 구축 하이브리드 기술 개발(NRF-2020R1F1A1070398)과 한국해양과학기술원(북서태평양 순환과 기후변동성이 한반도 주변 해역 변화와 물질 순환에 미치는 영향I, PE99911)의 지원을 받았습니다. 연구에 사용한 자료를 획득하기 위하여 해양과학기지 내 체류 연구를 수행한 연구자분들과 과학기지 유지 보수를 도와주신 국립해양조사원과 관련 연구자 분들에게 깊은 감사를 드립니다.

References

  1. KHOA (2019) A report on the improvement of the national ocean observation network to advance the ocean forecast decision-making system, Korea Hydrographic and Oceanographic Agency, Busan, 11-1192136-00507-01, 522 p
  2. KMA (2019) Quality control of Meteorological Observations, KMA National Climate Data Center, 11-1360000-001637-01, Seoul, 62 p
  3. Min Y, Jeong J-Y, Jang CJ, Lee J, Jeong J, Min I-K, Shim J-S, Kim YS (2020) Quality control of observed temperature time series from the Korea ocean research stations: preliminary application of ocean observation initiative's approach and its limitation. Ocean Polar Res 42(3):195-210. doi:10.4217/OPR.2020.42.3.195
  4. Yoo J, Choo J (2019) A study on the test and visualization of change in structures associated with the occurrence of non-stationary of long-term time series data based on unit root test. KIPS Trans Soft Data Eng 8(7):289-302. doi:10.3745/KTSDE.2019.8.7.289
  5. Lee SH (2015) Outlier detection and treatment using R. Freeacademi, Paju, 383p
  6. Lee J, Lee S, Jeong J, Min Y, Jeong J-Y, Kim YS ( 2021) Salinity drift prevention experiments in the Korea ocean research stations and suggestions for high quality salinity observation. J Coast Prev 8(4):323-333. doi:10.20481/kscdp.2021.8.4.323
  7. Cho H-Y, Lee K-S, Ahn S-M (2016) Impact of outliers on the statistical measures of the environmental monitoring. Ocean Polar Res 38(2):149-159. doi:10.4217/OPR.2016.38.2.149
  8. Chae J-H, Park M-S, Choi Y-J (2014) The WISE quality control system for integrated meteorological sensor data. Kor Meteorol Soc 24(3):445-456. doi:10.14191/Atmos.2014.24.3.445
  9. Ha K-J, Nam S, Jeong J-Y, Moon I-J, Lee M, Yun J, Jang CJ, Kim YS, Byun D-S, Heo K-Y, Shim J-S (2019) Observations utilizing Korea ocean research stations and their applications for process studies. B Am Meteorol Soc 100(10):2061-2075. doi:10.1175/BAMS-D-18-0305.1
  10. Hawkins DM (1980) Identification of outliers. Springer, Amsterdam, 188 p
  11. IOOS (2014) Manual for real-time quality control of in-situ temperature and salinity data Version 1.0. Integrated Ocean Observing System, Silver Spring, 55 p
  12. IOOS (2021) IOOS QC. https://github.com/ioos/qartod Accessed 1 Mar 2021
  13. Kim YS, Jang CJ, Noh J-H, Kim K-T, Kown J-I, Min Y, Jeong J, Lee J, Min I-K, Shim J-S, Byun D-S, Jeong J-Y (2019) A Yellow Sea monitoring system and its scientific applications. Front Mar Sci 6:601. doi:10.3389/fmars.2019.00601
  14. NDBC (2009) Handbook of automated data quality control checks and procedures. National Data Buoy Center, Mississippi, 78 p
  15. OOI (2012a) Data product specification for gradient test, version 1-01. Consortium for Ocean Leadership. https://oceanobservatories.org/wp-content/uploads/2015/09/1341-10004_Data_Product_SPEC_GLBLRNG_OOI.pdf Accesssed 30 Sep 2019
  16. OOI (2012b) Data product specification for spike test, version 1-01. Consortium for Ocean Leadership. https://oceanobservatories.org/wp-content/uploads/2015/09/1341-10006_Data_Product_SPEC_SPKETST_OOI.pdf Accesssed 30Sep 2019
  17. OOI (2013) Protocols and procedures for OOI data products: QA, QC, calibration, physical samples, version 1-22. Consortium for Ocean Leadership, 1102-00300 https://oceanobservatories.org/wp-content/uploads/2015/09/1102-00300_Protocols_Procedures_Data_Products_QAQC_Cal_Physical_Samples_OOI Accessed 30 Sep 2019
  18. OOI (2021) Automated QC Algorithms. https://github.com/ooici/ion-functions/tree/master/ion_functions/qcAccessed 1 Mar 2021
  19. UNESCO (1993) Manual of quality control procedures for validation of oceanographic data. IOC, Paris, 25 p
  20. Weller RA, Baker DJ, Glackin MM, Roberts SJ, Schmitt RW, Twigg ES and Vimont DJ (2019) The challenge of sustaining ocean observations. Front Mar Sci 6:105. doi:10.3389/fmars.2019.00105
  21. WMO (2010) Guide to the global observing system, WMO-NO. 488. World Meteorological Organization. https://community.wmo.int/wmo-no-488-guide-global-observing-system Accesssed 1 Mar 2021
  22. Zhou Y, Qin R, Xu H, Sadiq S, Yu Y (2018) A data quality control method for seafloor observatories: the application of observed time series data in the East China Sea. Sensors 18(8):2628. doi:10.3390/s18082628