Research in Meteorological Modeling Oriented Comprehensive Surface Complexity (CSC)
Research in Meteorological Modeling Oriented Comprehensive Surface Complexity (CSC)
复制标题
面向气象建模的综合地表复杂度（CSC）研究
DOI：
10.3390/su11154081
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发表时间：
2019-07
期刊：
影响因子：
3.9
通讯作者：
Xu Weiming
中科院分区：
文献类型：
--
作者：
Zhang Chunxiao;Zheng Xinqi;Li Jiayang;Wang Shuxian;Xu Weiming
地表特征(即地形和景观格局)是地理动态的重要因素。因此，地表的复杂性是设计多尺度模型的一个有价值的指标，它涉及到计算成本和模拟精度之间的平衡。通过将表征土地利用/土地覆盖(LULC)复杂性的地形复杂性指数与景观指数相结合，提出了综合地表复杂性(CSC)的概念来量化地面的复杂程度。本文以气象过程模拟为核心，通过建立气象模拟精度与地表地形和LULC复杂性之间的多元回归模型来计算CSC。针对中国研究较多的五个领域，本文给出了中国社会保障中心的分布情况，并分析了窗口大小效应。研究区域之间的比较表明，川渝地区的CSC最高，武汉地区最低。为了探讨CSC在气象模拟中的应用，以京津冀地区为例，进行了天气研究预报模型(WRF)的模拟，分析了CSC与2m气温的平均绝对误差(MAE)之间的关系。结果表明，MAE在研究区北部和南部较高，中部较低，与CSC值总体呈正相关。因此，可以得出结论，CSC有助于指示气象模拟能力，并确定哪些地区更适合进行细尺度模拟。
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.
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