The impacts of grid spacing and station network on surface analyses and forecasts in Beijing Winter Olympic complex terrain

Abstract

Abstract This study investigates the impacts of grid spacing and station network on surface analyses and forecasts including temperature, humidity and winds in Beijing Winter Olympic complex terrain. The high-resolution analyses are generated by a rapid-refresh integrated system that includes a topographic downscaling procedure. Results show that surface analyses are more accurate with a higher targeted grid spacing. Particularly, the average analysis errors of surface temperature, humidity, and winds are all significantly reduced when the grid size is increased. This improvement is mainly attributed to a more realistic simulation of the topographic effects in the integrated system because the topographic downscaling at higher grid spacing can add more details in complex mountain region. From 1km to 100m, 1-12h forecasts of temperature and humidity are also largely improved, while the wind only show slight improvement for 1-6h forecasts. The influence of station network on the surface analyses is further examined. Results show that the spatial distributions of temperature and humidity at hundred-meter space scale are more realistic and accurate when adding an intensive automatic weather station network, as more observational information can be absorbed. The adding of station network can also reduce the forecast errors, which can last for about 6 hours. However, although surface winds display better analysis skill when adding more stations, the wind at the mountain top region sometimes encounter a marginally worse effect for both analysis and forecast. The results are helpful to improve the analysis and forecast products in complex terrain, and have some implications for downscaling from coarse grid size to a finer grid.