Argo资料协同管理方法研究
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摘要
Argo资料已成为当前海洋和大气科学领域基础研究的重要数据来源。但由于其剖面元数据与观测数据混合存放的特点,现有的共享平台无法实现浮标漂移轨迹与剖面图的实时绘制。因此本文提出一种结构化与半结构化并存的Argo资料协同管理方法,通过分析Argo资料结构组成与特点,将结构化数据与半结构化数据分离提取;然后利用关系型数据库对半结构化类型属性的扩展支持,建立剖面元数据与观测数据间的关联关系;并利用分表存储,降低数据量快速增长对单数据表带来的存储压力。最后通过对近20 a的全球Argo资料解析建库结果进行分析,证明该方法具有良好的可扩展性和高效的轨迹数据获取效率,能够支持浮标漂移轨迹和剖面图的实时绘制。同时,该方法也可为特征类似的剖面观测数据管理提供技术参考。
Argo data have become an important data source in basic researches of ocean and atmosphere sciences. But with the hybrid storage of profile metadata and its observations,it is difficult for existing shared platforms to realize the real-time visualizing of the drift trajectory and profile chart. A method of collaborative management of structured and semi-structured Argo data were proposed. Based on the analysis of Argo data structure and characteristics,Argo data were separated as structured and semi-structured data. By means of extended support of relational database to a semistructured property,a relationship between profile metadata and observation data was established. Faced of the rapid growth of data,a multi-table storage method was used to reduce storage pressure of single data table. Finally,an Argo database was set up and global Argo data in period of 1997-2016 are parsed and imported. The results indicate that the method has good scalability of both storage and query,as well as high efficiency of float trajectory query,and it is able to support real-time visualization of float trajectory and profile chart. Our result will offer a reference for management of other similar observation profile data.
Argo data have become an important data source in basic researches of ocean and atmosphere sciences. But with the hybrid storage of profile metadata and its observations,it is difficult for existing shared platforms to realize the real-time visualizing of the drift trajectory and profile chart. A method of collaborative management of structured and semi-structured Argo data were proposed. Based on the analysis of Argo data structure and characteristics,Argo data were separated as structured and semi-structured data. By means of extended support of relational database to a semistructured property,a relationship between profile metadata and observation data was established. Faced of the rapid growth of data,a multi-table storage method was used to reduce storage pressure of single data table. Finally,an Argo database was set up and global Argo data in period of 1997-2016 are parsed and imported. The results indicate that the method has good scalability of both storage and query,as well as high efficiency of float trajectory query,and it is able to support real-time visualization of float trajectory and profile chart. Our result will offer a reference for management of other similar observation profile data.
引文
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[2]STEPHEN C R,HOWARD J F.Fifteen years of ocean observations with the global Argo array[J].Nature Climate Change,2016,6(2):145-153.
[3]ZHANG Chun-ling,XU Jian-ping.T/S distribution and variation in the Pacific based on Argo observations Part I:Temperature[J].Marine Science Bulletin,2014,33(6):647-658.张春玲,许建平.基于Argo观测的太平洋温、盐度分布与变化(I):温度[J].海洋通报,2014,33(6):647-658.
[4]ZHANG Chun-ling,XU Jian-ping.T/S distribution and variation in the Pacific based on Argo observations Part II:Salinity[J].Marine Science Bulletin,2015,34(1):21-31.张春玲,许建平.基于Argo观测的太平洋温、盐度分布与变化(II):盐度[J].海洋通报,2015,34(1):21-31.
[5]YANG Sheng-long,JIN Shao-fei,HUA Cheng-jun,et al.Spatial-temporal distribution of bigeye tuna Thunnus obesus in the tropical Atlantic Ocean based on Argo data[J].Chinese Journal of Applied Ecology,2015,26(2):601-608.杨胜龙,靳少非,化成君,等.基于Argo数据的热带大西洋大眼金枪鱼时空分布[J].应用生态学报,2015,26(2):601-608.
[6]YUAN D L,ZHANG Z C,CHU C P,et al.Geostrophic circulation in the tropical North Pacific Ocean based on Argo profiles[J].Journal of Physical Oceanography,2014,44(2):558-575.
[7]XU L X,LI P L,XIE S P,et al.Observing mesoscale eddy effects on mode-water subduction and transport in the North Pacific[J].Nature Communications,2016(7):1-9.
[8]WU L X,JING Z,RISER S,et al.Seasonal and spatial variations of Southern Ocean diapycnal mixing from Argo profiling floats[J].Nature Geoscience,2012,4(6):363-366.
[9]WANG Shuai,XU Cong-fu,CHEN Ya-fang.Web GIS based Argo data-sharing service system[J].Marine Sciences,2011,35(3):32-36.王帅,徐从富,陈雅芳.基于Web GIS的Argo数据共享服务系统[J].海洋科学,2011,35(3):32-36.
[10]WANG Xian-ling,QIN Bo,LIU Pei-shun.Argo data sharing system based on grid technology[J].Computer Engineering and Design,2009,30(15):3 634-3 637.王显玲,秦勃,刘培顺.基于网格技术的Argo数据共享系统[J].计算机工程与设计,2009,30(15):3 634-3 637.
[11]YANG Feng,ZHOU Jian-zheng,DU Yun-yan,et al.Multisource Argo data sharing and visualization based on Web Service[J].Science of Surveying and Mapping,2011,36(5):62-64.杨峰,周建郑,杜云艳,等.基于Web服务的多源Argo数据共享与可视化应用研究[J].测绘科学,2011,36(5):62-64.
[12]Oracle XML DB Developer’s Guide[DB/OL].[2016-12-16].http://docs.oracle.com/cd/B19306_01/appdev.102/b14259/toc.htm.
[13]The Postgre SQL Global Development Group.Postgre SQL 9.4.10 Documentation[DB/OL].[2016-12-16].https://www.postgresql.org/docs/9.4/static/index.html.
[14]LIU Zeng-hong,WU Xiao-fen,XU Jian-ping,et al.Fifteen years of ocean observations with China Argo[J].Advances in Earth Science,2016,31(5):445-460.刘增宏,吴晓芬,许建平,等.中国Argo海洋观测十五年[J].地球科学进展,2016,31(5):445-460.
[15]Description about the global Argo profile data set(V2.0)[EB/OL].[2016-12-16].www.argo.org.cn.全球Argo剖面浮标资料集(V2.0)说明[EB/OL].[2016-12-16].www.argo.org.cn.
[16]HAN Min,FENG Hao.Research of GIS data exchange method based on JSON[J].Science of Surveying and Mapping,2010,35(1):159-161.韩敏,冯浩.基于JSON的地理信息数据交换方法研究[J].测绘科学,2010,35(1):159-161.
[17]ZHANG Hu-yin,QU Qian-song,HU Rui-yun.Data exchange model based on JSON[J].Computer Engineering and Design,2015,26(12):3 380-3 384.张沪寅,屈乾松,胡瑞芸.基于JSON的数据交换模型[J].计算机工程与设计,2015,26(12):3 380-3 384.
[18]FEDER T.Argo begins systematic global probing of the upper oceans[J].Physics Today,2000,53(7):50-51.
[19]WANG Ji-min,LQing,WAN Ding-sheng.Geological section modeling system based on borehole data[J].Journal of Hohai University Natural Sciences,2009,37(4):463-466.王继民,吕庆,万定生.基于钻孔数据的地质剖面建模系统[J].河南大学学报:自然科学版,2009,37(4):463-466.
[20]WU Qing-song,WANG Qi,YING Jian-yun,et al.Real-time visualization of the DGPS data based on Matlab and its application in shipmounted observation[J].Journal of Marine Sciences,2016,34(2):60-64.吴清松,王琪,应剑云,等.基于Matlab的DGPS数据实时可视化与走航观测[J].海洋学研究,2016,34(2):60-64.
[21]WEI Jin-lan,ZHENG Hong-ming,XU Qun-zhou.Data management and application system for vertical seismic profiling[J].Journal of Computer Applications,2011,31(2):239-241.魏金兰,郑鸿明,徐群洲.垂直地震剖面数据管理和应用系统[J].计算机应用,2011,31(2):239-241.