Sensitivity Study on High-Resolution Numerical Modeling of Static Topographic Data

Both research-grade and operational numerical weather prediction models perform simulations with horizontal grid spacings as fine as 1 km, and their multi-scale terrain data have become increasingly important for high-resolution model forecasting. This study focused on the influence of multi-scale surface databases of topographical height and land use on the modeling of atmospheric circulation in a megacity. The default data were the global 30S United States Geographic Survey terrain data set and Moderate Resolution Imaging Spectroradiometer land-use data. The capacity for topographical expression under the combined scale effect was evaluated against observational data. The experiments showed that surface input data using finer resolutions for the Weather Research and Forecasting model with 1-km resolution gave better topographical expression and meteorological reproduction in a megacity and agreed with observational data in the fields of temperature and relative humidity, but precipitation values were not sensitive to the surface input data when verified against a suite of observational data including, but not limited to, ground-based instruments. The results indicated that the use of high-resolution databases improved the local atmospheric circulation in a megacity and that a fine-scale model was sensitive to the resolution of the surface input data whereas a coarse-scale model was less sensitive to it