| 摘要: |
| 保障长期连续的数据完整性和质量可靠性是进行浮标数据应用的首要问题。本文基于中国科学院近海观测研究网络黄海站位于北黄海长海县附近海域的五套浮标于2010~2019年连续10 a采集到的海洋表层温盐数据，进行数据分析与处理方法的研究。为了辨识原始温盐数据中的异常值，综合运用极值法、拉依达准则和箱型图法给出适合温盐的异常数据分析与处理方法，并基于2σ原则和箱型图法修正了温盐界限。为了解决温盐数据的缺失问题，提出SoftImpute与IterativeImpute相结合的插补方法，有效降低了温盐数据的标准差。研究结果表明，采用本文的方法可有效消除异常和插补缺失，修正数据中的异常点，得到连续、平滑、具有显著年际变化特征与趋势的温盐数据分析曲线，也可增加对该海域海洋温盐特征、变化规律和发展趋势等的深入理解，为海上现场观测数据处理提供借鉴，并为后续应用研究提供更高质量的数据。 |
| 关键词: 北黄海 水温 盐度 异常数据 缺失数据 插补处理 |
| DOI：10.11693/hyhz20210700170 |
| 分类号:P714.1 |
| 基金项目:国家自然科学基金面上基金，41876102号；中国科学院战略性先导科技专项项目，XDA190203号；中国科学院仪器设备功能开发技术创新项目，GYH201802号；国家重点研发计划“海洋环境安全保障”专项项目，2019YFC1407903号；国家自然科学基金，62172352号；河北省自然科学基金，F2017209070号。 |
附件 |
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| ANALYSIS AND PROCESSING OF LONG SEQUENCE AND MASSIVE TEMPERATURE AND SALINITY DATA OF THE NORTH YELLOW SEA FROM 2010 TO 2019 |
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CHEN Xiao1,2, LIU Chang-Hua3, LIU Zhi-Liang1,2, WANG Xu3, WANG Chun-Xiao3, JIA Si-Yang3
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1.Research Center of Marine Science, Hebei Normal University of Science & Technology, Qinhuangdao 066004, China;2.Hebei Key Laboratory of Ocean Dynamics, Resources and Environments, Qinhuangdao 066004, China;3.Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
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| Abstract: |
| Ensuring the integrity and reliability of long-term continuous buoy data is the primary issue for the application of the data. Five sets of buoys in the Yellow Sea located in the waters near Changhai County, North Yellow Sea deployed by the Chinese Academy of Sciences Offshore Observation and Research Network were used. Data analysis and processing methods of the sea surface temperature and salt data collected by the buoys for 10 years from 2010 to 2019 were studied. To identify the abnormal values in the original temperature and salinity data, the extreme value method, the Laida criterion, and the box plot method were compared to find the best one to treat abnormal data. In the 2σ principle with the box diagram method, the boundary values were adjusted. In addition, to address the data missing, interpolation combining the SoftImpute and IterativeImpute was proposed, by which the standard deviations of the data could be effectively reduced. Results show that the methods are effective and can be used to eliminate anomalies and imputation defects, correct abnormal points, smooth out data curve, and highlight significant interannual variations and trends in the study sea area. This study provided a reference for enhancing marine observation data for future research. |
| Key words: North Yellow Sea temperature salinity abnormal data missing data imputation processing |
