GRAPES_MESO中时间步长和空间分辨率对于预报效果的影响

The Impacts of Time-Step Size and Spatial Resolution on the Prediction Skill of the GRAPES-MESO Forecast System

基于GRAPES区域中尺度数值预报系统(GRAPES_MESO),针对700 h Pa、500 h Pa和200 h Pa的位势高度场H,温度场T,风场纬向分量U,经向分量V和地面降水场,在给定的模式物理过程下,分别考察了时间步长和空间分辨率对于模式预报效果的影响。研究结果表明,空间分辨率(0.3°×0.3°)相同时,各变量在不同层次的预报几乎都存在最优时间步长使得预报技巧最高,初步说明最优时间步长理论在复杂的偏微分方程组中的适用性。随后,将空间分辨率为0.3°×0.3°时最优时间步长(240 s)的预报结果与当前业务中(空间分辨率为0.15°×0.15°、时间步长为90 s)的预报结果进行比较,发现前者的变量H、T、U、V和地面降水场的预报技巧均高于后者,表明并不是空间分辨率越高预报效果越好。

This study considered the impacts of time-step size and spatial resolution on the prediction skill of the Global/Regional Assimilation and Prediction System（GRAPES） mesoscale numerical forecast system（GRAPES-MESO） for a given parameter set. The forecasts of geopotential height（H）, temperature（T）, and the zonal（U） and meridional（V） components of wind at 700, 500, and 200 hPa, were assessed, as well as surface precipitation. The results showed that, at a spatial resolution of 0.3°×0.3°, the prediction skill of almost all variables, including H, T, U and V, in the three vertical layers were optimized at a particular time step of approximately 240 s. This raises the possibility of an optimal time-step size for a particular spatial resolution, and the explanation for this relationship might be related to the computational uncertainty principle. The operational forecasts based on a spatial resolution of 0.15°×0.15° and a time-step size of 90 s were also compared with the best results obtained previously, in which the spatial resolution was 0.3°×0.3° and the time step was 240 s. The latter configuration possessed higher skill than the operational forecasts for all variables, indicating that the prediction quality may not be significantly improved by an increase in the spatial resolution of the model.