龚建东,邱崇践,王强,陈伟民. 1999. 区域四维变分资料同化的数值试验[J]. 气象学报, 57(2):131-142, doi:10.11676/qxxb1999.012
区域四维变分资料同化的数值试验
THE NUMERICAL EXPERIMENT IN AREA FOUR-DIMENSIONAL VARIATIONAL DATA ASSIMILATION
投稿时间:1997-07-31  修订日期:1998-03-10
DOI:10.11676/qxxb1999.012
中文关键词:  区域四维变分资料同化  误差来源  同时同化  数值试验。
英文关键词:Area four-dimensional variational data assimilation  Error source  Assimilation simultaneity  Numerical experiment
基金项目:国家自然科学基金;甘肃自然科学基金
作者单位
龚建东 兰州大学大气科学系, 兰州, 730000 
邱崇践 兰州大学大气科学系, 兰州, 730000 
王强 兰州中心气象台, 兰州, 730000 
陈伟民 兰州中心气象台, 兰州, 730000 
摘要点击次数: 2270
全文下载次数: 3146
中文摘要:
      针对中尺度数值预报模式预报误差的主要来源,尝试利用四维变分资料同化的方法来改善预报效果。在已建立的中尺度模式(MM4)四维变分资料同化系统基础上,进行了若干数值试验,通过比较同化前后的预报来检验同化的效果。这些试验中初始场、模式误差和侧边界条件被分别或同时作为控制变量来进行调整,主要探讨了模式误差和侧边界条件对同化及预报的影响,以及同时结合两者或三者的途径和方法。对两组个例分别进行的试验结果表明,区域中尺度模式预报误差除了来源于初始误差外,模式误差、侧边界条件也有不可忽视的作用。同化时应同时考虑初始场、模式误差和侧边界条件这三方面的共同作用,仅修正其中某一个或某两个会把由于其它方面造成的预报误差转嫁到它们之上,从而出现尽管目标函数下降很快而预报结果并没有相应改善的现象。
英文摘要:
      In this paper, a number of numerical experiments are carried out with area meso scale four-dimensional variational data assimilation system. In those experiments, initial condition, modelerror and boundary condition are selected as control variable to adjust. The results of data assimilation and following prediction are tested. The main propose of those experiments is to find out the actions of modelerror and boundary condition in data assimilation, and to select the method for those three as control variable at same time. The results show that modelerror and boundary condition have large affection to forecasterror except initial condition in area mesoscale model. The experiments use a method to correct modelerror, and the results show this correction makes cost function converge quickly in data assimilation and has persistence in the following prediction. The adjust ment to boundary condition makes cost function converge quickly. The results also show that initial condition, modelerror, and boundary condition should select as control variable at same time in data assimilation. Otherwise, it will transpose one error to another error because the model has noability to disting uish where forecast error really comes from, and makes cost function converge quickly but the following forecast has no improvement.
HTML   查看全文   查看/发表评论  下载PDF阅读器
分享按钮