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作 者:刘德强[1,2,3] 冯杰[1,2] 李建平[4,5] 王金成[6]
机构地区:[1]中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室,北京100029 [2]中国科学院大学,北京100049 [3]福建省气象台,福州350001 [4]北京师范大学全球变化与地球系统科学研究院,北京100875 [5]全球变化研究协同创新中心,北京100875 [6]中国气象局数值预报中心,北京100081
出 处:《大气科学》2015年第6期1165-1178,共14页Chinese Journal of Atmospheric Sciences
基 金:国家自然科学基金项目41375110;41175069
摘 要: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.
关 键 词:GRAPES_MESO 时间步长 空间分辨率 预报效果
分 类 号:P456.7[天文地球—大气科学及气象学]
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