中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Marine spatio-temporal process semantics and its applications-taking the El Nino Southern Oscilation process and Chinese rainfall anomaly as an example

文献类型:SCI/SSCI论文

作者Xie J.
发表日期2012
关键词marine process semantics hierarchical abstraction and inclusion by level ENSO process rainfall anomalies enso evolution pacific events
英文摘要Spatio-temporal semantics based on "object views" or "event views" has few abilities to represent and model the continuity and gradual oceanic phenomena or objects, which seriously limits the specific marine applications and knowledge discovery and data mining, so this paper proposes a hierarchical abstraction semantics with "marine spatio-temporal process -> life span phases -> evolution sequences -> state units" and process objects included by level with "marine process objects -> phase objects -> sequence object -> state objects" with the oceanic process characteristics into the marine process semantics. In addition, this paper designs the storage and representation of marine process objects using the backus normal forms (BNF) and abstract data type (ADT). Base on El Nino Southern Oscilation (ENSO) index and Chinese rain gauging station data, this paper also gives a case of study. The spatio-temporal analysis between ENSO process and Chinese rainfall anomalies shows that the marine spatio-temporal semantics not only can illustrate the spatial distribution of Chinese rainfall anomalies in different time scales at ENSO process, life span phases and state units, but also analyze the dynamic changes of Chinese rainfall anomalies in different life span phases or state units within ENSO evolution.
出处Acta Oceanologica Sinica
31
2
16-24
收录类别SCI
语种英语
ISSN号0253-505X
源URL[http://ir.igsnrr.ac.cn/handle/311030/26729]  
专题地理科学与资源研究所_历年回溯文献
推荐引用方式
GB/T 7714
Xie J.. Marine spatio-temporal process semantics and its applications-taking the El Nino Southern Oscilation process and Chinese rainfall anomaly as an example. 2012.

入库方式: OAI收割

来源:地理科学与资源研究所

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