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

Title
이어도 해양과학기지 수온 시계열 자료의 이상값 검출을 위한 국제 품질검사의 성능 평가
Alternative Title
Evaluation of International Quality Control Procedures for Detecting Outliers in Water Temperature Time-series at Ieodo Ocean Research Station
Author(s)
Min, Yong Chim; Jun, Hyun Jung; Jeong, Jin Yong; Park, Sung Hwan; Lee, Jae Ik; Jeong, Jong Min; Min, In Ki; Kim, Yong Sun
KIOST Author(s)
Min, Yong Chim(민용침)Jun, Hyun Jung(전현정)Jeong, Jin Yong(정진용)Park, Sung Hwan(박숭환)Lee, Jae Ik(이재익)Jeong, Jong Min(정종민)Min, In Ki(민인기)Kim, Yong Sun(김용선)
Alternative Author(s)
민용침; 전현정; 정진용; 박숭환; 이재익; 정종민; 민인기; 김용선
Publication Year
2021-12
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.
ISSN
1598-141X
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/41982
DOI
10.4217/OPR.2021.43.4.229
Bibliographic Citation
Ocean and Polar Research, v.43, no.4, pp.229 - 243, 2021
Publisher
한국해양과학기술원
Keywords
Korea Ocean Research Stations (KORS); U.S. IOOS; NDBC; OOI; outliers
Type
Article
Language
Korean
Publisher
한국해양과학기술원
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