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Research in Meteorological Modeling Oriented Comprehensive Surface Complexity (CSC)

1
School of Information Engineering, China University of Geosciences in Beijing, No. 29, Xueyuan Road, Haidian District, Beijing 100083, China
2
Polytechnic Center for Natural Resources Big-data, MNR of China, Beijing 100036, China
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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
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Authors to whom correspondence should be addressed.
Sustainability 2019, 11(15), 4081; https://doi.org/10.3390/su11154081
Received: 17 June 2019 / Revised: 19 July 2019 / Accepted: 23 July 2019 / Published: 29 July 2019
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. View Full-Text
Keywords: comprehensive surface complexity (CSC); topography; land use and land cover; meteorological modeling comprehensive surface complexity (CSC); topography; land use and land cover; meteorological modeling
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MDPI and ACS Style

Zhang, C.; Zheng, X.; Li, J.; Wang, S.; Xu, W. Research in Meteorological Modeling Oriented Comprehensive Surface Complexity (CSC). Sustainability 2019, 11, 4081. https://doi.org/10.3390/su11154081

AMA Style

Zhang C, Zheng X, Li J, Wang S, Xu W. Research in Meteorological Modeling Oriented Comprehensive Surface Complexity (CSC). Sustainability. 2019; 11(15):4081. https://doi.org/10.3390/su11154081

Chicago/Turabian Style

Zhang, Chunxiao; Zheng, Xinqi; Li, Jiayang; Wang, Shuxian; Xu, Weiming. 2019. "Research in Meteorological Modeling Oriented Comprehensive Surface Complexity (CSC)" Sustainability 11, no. 15: 4081. https://doi.org/10.3390/su11154081

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