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GRAPES_GFS 2.0模式系统误差评估
引用本文:张萌,于海鹏,黄建平,沈学顺,苏勇,薛海乐,杨志坚.GRAPES_GFS 2.0模式系统误差评估[J].应用气象学报,2018,29(5):571-583.
作者姓名:张萌  于海鹏  黄建平  沈学顺  苏勇  薛海乐  杨志坚
作者单位: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, 兰州 730000
基金项目:国家自然科学基金项目(41705077),公益性行业(气象)科研专项(GYHY201206009),高等学校学科创新引智计划(B13045)
摘    要:通过选取2014年1月、4月、7月、10月的GRAPES_GFS 2.0预报产品和NCEP FNL分析资料进行对比分析,发现GRAPES_GFS 2.0的系统误差具有以下特性:位势高度场误差的空间分布具有纬向条带状或波列状特征,误差大值集中在中高纬度地区,低纬度地区误差较小。误差在南北半球各自的冬季最大、夏季最小,并呈现明显的季节变化特征。误差随预报时效的增速略低于线性增速且不同预报时效下误差随高度变化的曲线趋势相似。温度场误差的空间分布相对均匀,误差大值位于30°S~30°N附近地区。纬向风场误差没有十分明显的分布规律,与纬度变化、海陆分布和地形的关系均不密切,西风误差和东风误差交替出现。结果表明:模式对冬季中高纬度地区和边界层及对流层顶的模拟技巧尚需提高。明确GRAPES_GFS 2.0的系统误差分布特性,有助于有针对性地进行模式订正,改善误差大值区域的模式预报方法。

关 键 词:GRAPES_GFS  2.0    系统误差    空间分布    时间演变
收稿时间:2018/3/20 0:00:00
修稿时间:2018/5/21 0:00:00

Assessment on Systematic Errors of GRAPES_GFS 2.0
Zhang Meng,Yu Haipeng,Huang Jianping,Shen Xueshun,Su Yong,Xue Haile and Yang Zhijian.Assessment on Systematic Errors of GRAPES_GFS 2.0[J].Quarterly Journal of Applied Meteorology,2018,29(5):571-583.
Authors:Zhang Meng  Yu Haipeng  Huang Jianping  Shen Xueshun  Su Yong  Xue Haile and Yang Zhijian
Affiliation:College of Atmospheric Sciences and Key Laboratory for Semi-arid Climate change, Lanzhou University, Lanzhou 730000,College of Atmospheric Sciences and Key Laboratory for Semi-arid Climate change, Lanzhou University, Lanzhou 730000;Unit 86 of No.93811 PLA, Lanzhou 730020;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, Lanzhou 730020,College of Atmospheric Sciences and Key Laboratory for Semi-arid Climate change, Lanzhou University, Lanzhou 730000,Numerical Prediction Center of CMA, Beijing 100081;State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081,Numerical Prediction Center of CMA, Beijing 100081,State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081 and Unit 86 of No.93811 PLA, Lanzhou 730020
Abstract:The Global and Regional Assimilation and Prediction System(GRAPES) model is set up as a new generation multi-scale universal data assimilation and numerical prediction system in China. The global forecasting system version of GRAPES_GFS 2.0 is formally established in June 2016, and thus a comprehensive assessment on its forecasting capacity is urgently needed. Comparing with NCEP FNL data, the hindcast of a whole year of 2014 and 4 seasonal representative months by GRAPES_GFS 2.0 are analyzed.The systematic error of 500 hPa potential height field is characterized by the obvious gradient and zonal distribution or wave columnar distribution, concentrated in mid and high latitudes. The error shows significant seasonal variation, which is much larger in winter than that in summer of both the north and south hemispheres. Furthermore, compared with the linear growth rate, GRAPES_GFS 2.0 forecast error is lower, and changing trends of errors with height are similar when lead time changes. The distribution of the initial error of 500 hPa temperature field is concentrated in tropics, while along with the growth of the forecast time, the large area of forecast error gradually moves to middle and high latitude areas. Moreover, the zonal average temperature error is mainly negative, while slightly positive near the tropopause of high latitude areas. There is no obvious distribution law of latitudinal wind field error, which is not closely related to latitude, sea land distribution and topography, alternated with west wind error and east wind error. The error of the height field in the tropopause, the temperature field and zonal wind field in the boundary layer and in the tropopause increases rapidly as well.Results above show that the evaluation on the oblique pressure instability of geopotential height field in mid and high latitudes still needs improving. As the low latitude area is dominated by positive pressure structure, the absolute error value with its growth is relatively small. Over-estimated thermal forces in plateau and desert regions result in the large error area of temperature field. The zonal wind field error is similar but may result in meridional wind error. In addition, the performance of the model in boundary layer and tropopause simulation needs improving.
Keywords:GRAPES_GFS 2  0  systematic error  spatial distribution  temporal evolution
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