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Research in Meteorological Modeling Oriented Comprehensive Surface Complexity (CSC)

Author

Listed:
  • Chunxiao Zhang

    (School of Information Engineering, China University of Geosciences in Beijing, No. 29, Xueyuan Road, Haidian District, Beijing 100083, China
    Polytechnic Center for Natural Resources Big-data, MNR of China, Beijing 100036, China)

  • Xinqi Zheng

    (School of Information Engineering, China University of Geosciences in Beijing, No. 29, Xueyuan Road, Haidian District, Beijing 100083, China
    Polytechnic Center for Natural Resources Big-data, MNR of China, Beijing 100036, China)

  • Jiayang Li

    (School of Information Engineering, China University of Geosciences in Beijing, No. 29, Xueyuan Road, Haidian District, Beijing 100083, China)

  • Shuxian Wang

    (School of Information Engineering, China University of Geosciences in Beijing, No. 29, Xueyuan Road, Haidian District, Beijing 100083, China)

  • Weiming Xu

    (National Engineering Research Center of Geospatial Information Technology, Spatial Information Engineering Research Center of Fujian Province, Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350116, China)

Abstract

Ground surface characteristics (i.e., topography and landscape patterns) are important factors in geographic dynamics. Thus, the complexity of ground surface is a valuable indicator for designing multiscale modeling concerning the balance between computational costs and the accuracy of simulations regarding the resolution of modeling. This study proposes the concept of comprehensive surface complexity (CSC) to quantity the degree of complexity of ground by integrating the topographic complexity indices and landscape indices representing the land use and land cover (LULC) complexity. Focusing on the meteorological process modeling, this paper computes the CSC by constructing a multiple regression model between the accuracy of meteorological simulation and the surface complexity of topography and LULC. Regarding the five widely studied areas of China, this paper shows the distribution of CSC and analyzes the window size effect. The comparison among the study areas shows that the CSC is highest for the Chuanyu region and lowest for the Wuhan region. To investigate the application of CSC in meteorological modeling, taking the Jingjinji region for instance, we conducted Weather Research and Forecasting Model (WRF) modeling and analyzed the relationship between CSC and the mean absolute error (MAE) of the temperature at 2 meters. The results showed that the MAE is higher over the northern and southern areas and lower over the central part of the study area, which is generally positively related to the value of CSC. Thus, it is feasible to conclude that CSC is helpful to indicate meteorological modeling capacity and identify those areas where finer scale modeling is preferable.

Suggested Citation

  • Chunxiao Zhang & Xinqi Zheng & Jiayang Li & Shuxian Wang & Weiming Xu, 2019. "Research in Meteorological Modeling Oriented Comprehensive Surface Complexity (CSC)," Sustainability, MDPI, vol. 11(15), pages 1-13, July.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:15:p:4081-:d:252541
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    References listed on IDEAS

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    1. Jiayang Li & Xinqi Zheng & Chunxiao Zhang & Youmin Chen, 2018. "Impact of Land-Use and Land-Cover Change on Meteorology in the Beijing–Tianjin–Hebei Region from 1990 to 2010," Sustainability, MDPI, vol. 10(1), pages 1-22, January.
    2. Zhang, Chunxiao & Chen, Min & Li, Rongrong & Fang, Chaoyang & Lin, Hui, 2016. "What's going on about geo-process modeling in virtual geographic environments (VGEs)," Ecological Modelling, Elsevier, vol. 319(C), pages 147-154.
    3. Min Cao & Yanhui Zhu & Guonian Lü & Min Chen & Weifeng Qiao, 2019. "Spatial Distribution of Global Cultivated Land and Its Variation between 2000 and 2010, from Both Agro-Ecological and Geopolitical Perspectives," Sustainability, MDPI, vol. 11(5), pages 1-16, February.
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