GRAPES_GFS 2.0模式非系统误差评估  

Assessment on Unsystematic Errors of GRAPES_GFS 2.0

作　　者：张萌 于海鹏[1,2,3] 黄建平 沈学顺[4,5] 苏勇[4] 薛海乐[5] 窦宝成[6] Zhang Meng;Yu Haipeng;Huang Jianping;Shen Xueshun;Su Yong;Xue Haile;Dou Baocheng(Collage of Atmospheric Sciences and Key Laboratory for Semi-arid Climate Change,Lanzhou University,Lanzhou 730000;Unit 86 of No.93811 PLA,Lanzhou 730000;Key Laboratory of Arid Climatic Change and Reducing Disaster of Gansu Province,Key Open Laboratory of Arid Climatic Change and Disaster Reduction of CMA,Institute of Arid Meteorology CMA,Lanzhou730020;Center for Numerical Prediction China Meteorological Administration,Beijing 100081;State Key Laboratory of Severe Weather,Chinese Academy of Meteorological Sciences,Beijing 100081;Meteorological Observatory of No.95920 PLA,Hengshui 253801)

机构地区：[1]兰州大学大气科学学院半干旱气候变化教育部重点实验室 [2]中国人民解放军93811部队86分队 [3]中国气象局兰州干旱气象研究所甘肃省干旱气候变化与减灾重点实验室中国气象局干旱气候变化与减灾重点开放实验室 [4]中国气象局数值预报中心 [5]中国气象科学研究院灾害天气国家重点实验室 [6]中国人民解放军95920部队气象台

出　　处：《应用气象学报》2019年第3期332-344,共13页Journal of Applied Meteorological Science

基　　金：国家自然科学基金项目(41705077);中国博士后科学基金(2017M613250);公益性行业(气象)科研专项(GYHY201206009)

摘　　要：选取2014年1月、4月、7月、10月的GRAPES_GFS 2. 0预报产品和对应时刻的NCEP FNL分析资料进行对比。从时间演变看,南、北半球的非系统误差均在各自冬季达到极盛,误差呈现周期性变化规律。位势高度场的非系统误差随时间演变先呈指数增长,后呈线性增长,温度场和纬向风场的误差则近似于线性增长。从空间分布看,GRAPES_GFS 2.0的非系统误差大值主要分布在中高纬度地区,呈条带状分布,误差大值区域基本不随预报时效的变化而发生变化;位势高度场和纬向风场的误差大值区出现在对流层顶附近,而温度场的误差大值区则出现在边界层顶附近。将误差增长曲线参数化拟合后发现,南半球的初始场误差、可预报上限和初始场误差占比均高于北半球,随离地高度增加初始场误差占比逐渐减小。Unsystematic error is one of main sources of model simulation error, which is mainly induced by initial field error and model defect. Global and Regional Assimilation and Prediction System(GRAPES) global model forecast data and final analysis data made by National Centers for Environmental Prediction(NCEP)during January, April, July, October in 2014 are chosen to be compared and analyzed. In terms of temporal variation, conclusion could be made that peaks of unsystematic errors in both north and south hemispheres occur in their respective winters, and errors show periodical changes. With the increase of forecasting time, the model unsystematic mean square error along with geopotential height field increases over time, first in an exponential function way, and then linear growth. In addition, linear growth in temperature field and zonal wind field are discovered. It shows in the consequence that the large value of model unsystematic mean square error center in mid-latitude and distribute like zonal banded. Large value regions basically do not change along with forecast time. In zonal average geopotential height field and zonal wind field, large value regions are found in tropopause, whereas it is found in boundary layer in temperature field. The cause is that the parameterization scheme does not fully describe differences of these two stratifications in physical process and dynamic framework. It is worth mentioning that the error of the increase in the height of the temperature field at middle and upper levels of the troposphere decreasing. After fitting the error-time line, the error of the initial field, the upper limit of the prediction and the proportion error taken up by the initial field in the south hemisphere are all higher than that in the north hemisphere. Besides, it is found that the initial field gradually decreases in proportion with height increasing. It shows in the above results that the precision of GRAPES_GFS 2. 0 for the simulation of mid-latitudes of the south hemisphere and the entire troposp

关 键 词：GRAPES_GFS 2.0 非系统误差 空间分布 时间演变 

分 类 号：P456.7[天文地球—大气科学及气象学]