Research in Meteorological Modeling Oriented Comprehensive Surface Complexity (CSC)

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.From the perspective of topography, usually topographic complexity (TC) is considered to be the same as surface roughness, representing the degree of the difference in elevation among the adjacent cells of a digital elevation model (DEM). The topographic complexity index (TCI) is derived to reflect the average elevation change between any point on a grid and its surrounding areas [6]. This TCI is widely applied in geographic research. For instance, Li et al. [7] argued that the uncertainties in volume calculations based on a DEM are primarily related to the discrete error, which is usually caused by DEM resolution and the TCI. In the dynamic process simulation, the TCI is also considered to achieve better simulation performance. In meteorological simulation, the modeling accuracy is related to the TCI of the study area, since the presence of complex terrain causes spatial (in both horizontal and vertical directions) and temporal variations in air flow and turbulence, which strongly influence the meteorological conditions [8].Concerning land use and land cover (LULC), the structural complexity of LULC is essential for the analysis of the patterns in structural elements and processes that occur [9]. On the one hand, it is highly relevant to the accuracy of data collection. When LULC information is obtained using satellite images and the proposed spe...