中国物理B ›› 2022, Vol. 31 ›› Issue (4): 49201-049201.doi: 10.1088/1674-1056/ac43a3

• • 上一篇    

Characteristics of vapor based on complex networks in China

Ai-Xia Feng(冯爱霞)1, Qi-Guang Wang(王启光)2,†, Shi-Xuan Zhang(张世轩)3, Takeshi Enomoto(榎本刚)4, Zhi-Qiang Gong(龚志强)5, Ying-Ying Hu(胡莹莹)6, and Guo-Lin Feng(封国林)5   

  1. 1 Data Service Office, National Meteorological Information Center CMA, Beijing 100081, China;
    2 China Meteorological Administration Training Center, China Meteorological Administration, Beijing 100081, China;
    3 Pacific Northwest National Laboratory, Richland WA 99352, USA;
    4 Disaster Prevention Research Institute, Kyoto University, Uji, Kyoto 611-0011, Japan;
    5 Laboratory for Climate Studies, National Climate Center CMA, Beijing 100081, China;
    6 Chinese Academy of Meteorological Sciences, Beijing 100081, China
  • 收稿日期:2021-10-04 修回日期:2021-12-08 接受日期:2021-12-16 出版日期:2022-03-16 发布日期:2022-03-10
  • 通讯作者: Qi-Guang Wang E-mail:photon316@163.com
  • 基金资助:
    Project supported by the National Natural Science Foundation of China (Grant Nos. 41775081, 41975100, 41901016, and 41875100), the Innovation Project of the China Meteorological Administration (Grant No. CXFZ2021Z034), and the National Key Research and Development Program of China (Grant No. 2018YFC1507702).

Characteristics of vapor based on complex networks in China

Ai-Xia Feng(冯爱霞)1, Qi-Guang Wang(王启光)2,†, Shi-Xuan Zhang(张世轩)3, Takeshi Enomoto(榎本刚)4, Zhi-Qiang Gong(龚志强)5, Ying-Ying Hu(胡莹莹)6, and Guo-Lin Feng(封国林)5   

  1. 1 Data Service Office, National Meteorological Information Center CMA, Beijing 100081, China;
    2 China Meteorological Administration Training Center, China Meteorological Administration, Beijing 100081, China;
    3 Pacific Northwest National Laboratory, Richland WA 99352, USA;
    4 Disaster Prevention Research Institute, Kyoto University, Uji, Kyoto 611-0011, Japan;
    5 Laboratory for Climate Studies, National Climate Center CMA, Beijing 100081, China;
    6 Chinese Academy of Meteorological Sciences, Beijing 100081, China
  • Received:2021-10-04 Revised:2021-12-08 Accepted:2021-12-16 Online:2022-03-16 Published:2022-03-10
  • Contact: Qi-Guang Wang E-mail:photon316@163.com
  • Supported by:
    Project supported by the National Natural Science Foundation of China (Grant Nos. 41775081, 41975100, 41901016, and 41875100), the Innovation Project of the China Meteorological Administration (Grant No. CXFZ2021Z034), and the National Key Research and Development Program of China (Grant No. 2018YFC1507702).

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摘要： The uneven spatial distribution of stations providing precipitable water vapor (PWV) observations in China hinders the effective use of these data in assimilation, nowcasting, and prediction. In this study, we proposed a complex network framework for exploring the topological structure and the collective behavior of PWV in the mainland of China. We used the Pearson correlation coefficient and transfer entropy to measure the linear and nonlinear relationships of PWV amongst different stations and to set up the undirected and directed complex networks, respectively. Our findings revealed the statistical and geographical distribution of the variables influencing PWV networks and identified the vapor information source and sink stations. Specifically, the findings showed that the statistical and spatial distributions of the undirected and directed complex vapor networks in terms of degree and distance were similar to each other (the common interaction mode for vapor stations and their locations). The betweenness results displayed different features. The largest betweenness ratio for directed networks tended to be larger than that of the undirected networks, implying that the transfer of directed PWV networks was more efficient than that of the undirected networks. The findings of this study are heuristic and will be useful for constructing the best strategy for the PWV data in applications such as vapor observational networks design and precipitation prediction.

关键词: precipitable water vapor, complex networks, transfer entropy, nonlinear

Abstract: The uneven spatial distribution of stations providing precipitable water vapor (PWV) observations in China hinders the effective use of these data in assimilation, nowcasting, and prediction. In this study, we proposed a complex network framework for exploring the topological structure and the collective behavior of PWV in the mainland of China. We used the Pearson correlation coefficient and transfer entropy to measure the linear and nonlinear relationships of PWV amongst different stations and to set up the undirected and directed complex networks, respectively. Our findings revealed the statistical and geographical distribution of the variables influencing PWV networks and identified the vapor information source and sink stations. Specifically, the findings showed that the statistical and spatial distributions of the undirected and directed complex vapor networks in terms of degree and distance were similar to each other (the common interaction mode for vapor stations and their locations). The betweenness results displayed different features. The largest betweenness ratio for directed networks tended to be larger than that of the undirected networks, implying that the transfer of directed PWV networks was more efficient than that of the undirected networks. The findings of this study are heuristic and will be useful for constructing the best strategy for the PWV data in applications such as vapor observational networks design and precipitation prediction.

Key words: precipitable water vapor, complex networks, transfer entropy, nonlinear

中图分类号:  (Water in the atmosphere)

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