Abstract
Complex network theory provides a powerful framework to statistically investigate the topology of complex system include both artificial systems and natural systems. We propose a method to construct a climate observation network with climate observation records from automatic weather stations (AWS) in different locations. A link between AWS represents the cross-correlation between them. Apply this method to the climate observation records from the city of Chengdu and find that AWS with edge connected are located very close to. And the area with dense AWS has a significantly higher correlation between AWS compared to the area with exiguous AWS. This work would be helpful for identifying the preferred strategy for location optimization problems / discovering similar observation stations associated with AWS or using this information to complete missing/error values.
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References
World Meteorological Organization. Guide to Meteorological Instruments and Methods of Observations. WMO-No.8, Geneva, Switzerland (2008)
Automatic Weather Stations, http://www.automaticweatherstation.com/index.html
Miller, P.A., Barth, M.F.: Ingest, integration, quality control, and distribution of observations from state transportation departments using MADIS. In: 19th International Conference on Interactive Information and Processing Systems (2003)
Steinhaeuser, K., Chawla, N.V., Ganguly, A.R.: Complex networks in climate science: Progress, opportunities and challenges. In: Proc. Conf. on Intelligent Data Understanding, San Francisco, CA, NASA, pp. 16–26 (2010)
Donges, J.F., Zou, Y., Marwan, N., et al.: The backbone of the climate network. EPL (Europhysics Letters) 87(4), 48007 (2009)
Donges, J.F., Zou, Y., Marwan, N., et al.: Complex networks in climate dynamics. The European Physical Journal Special Topics 174(1), 157–179 (2009)
Estévez, J., Gavilán, P., Giráldez, J.V.: Guidelines on validation procedures for meteorological data from automatic weather stations. Journal of Hydrology 402(1), 144–154 (2011)
Zhou, C., Zemanová, L., Zamora-Lopez, G., et al.: Structure–function relationship in complex brain networks expressed by hierarchical synchronization. New Journal of Physics 9(6), 178 (2007)
Zamora-López, G.: Linking structure and function of complex cortical networks[D]. Universitätsbibliothek (2009)
Papana, A., Kugiumtzis, D.: Evaluation of mutual information estimators on nonlinear dynamic systems. arXiv preprint arXiv:0809.2149 (2008)
Serrano, A., Boguna, M., Vespignani, A.: Extracting the multiscale backbone of complex weighted networks. Proceedings of the National Academy of Sciences USA 106(16), 8847–8852 (2009)
Tsonis, A.A., Roebber, P.J.: The architecture of the climate network. Physica A 333, 497–504 (2004)
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Yu, W., Zhang, H. (2015). Construct Climate Observation Network and Discover Similar Observation Stations. In: Bian, F., Xie, Y. (eds) Geo-Informatics in Resource Management and Sustainable Ecosystem. GRMSE 2014. Communications in Computer and Information Science, vol 482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45737-5_5
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DOI: https://doi.org/10.1007/978-3-662-45737-5_5
Publisher Name: Springer, Berlin, Heidelberg
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