龚建东,张林,王瑞春. 2021. 集合预报误差在GRAPES全球四维变分同化中的应用研究Ⅱ:参数选取与数值试验[J]. 气象学报, 79(1):48-64, doi:10.11676/qxxb2021.007
集合预报误差在GRAPES全球四维变分同化中的应用研究Ⅱ:参数选取与数值试验
The study of introducing ensemble forecast errors in the GRAPES global 4DVar Part Ⅱ:Parameters determination and numerical experiments
投稿时间:2020-04-24  修订日期:2020-10-13
DOI:10.11676/qxxb2021.007
中文关键词:  集合资料同化  集合预报误差  En4DVar  参数选择  数值试验
英文关键词:Ensemble data assimilation  Ensemble forecast errors  En4DVar  Parameter determination  Numerical experiments
基金项目:国家重点研发计划项目(2018YFC1506700、2018YFC1506705)
作者单位
龚建东 国家气象中心, 北京, 100081
中国气象局数值预报中心, 北京, 100081 
张林 国家气象中心, 北京, 100081
中国气象局数值预报中心, 北京, 100081 
王瑞春 国家气象中心, 北京, 100081
中国气象局数值预报中心, 北京, 100081 
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中文摘要:
      研究的第一部分讨论了如何有效应用集合预报误差的科学方案,确定了集合预报误差在GRAPES(Global Regional Assimilation and PrEdiction System)全球4DVar(four dimensional variational data assimilation)中应用的分析框架。在此基础上研究了针对集合预报误差实际应用于GRAPES全球4DVar,解决接近或超过100个集合样本数时高效生成的计算效率问题,以及与GRAPES全球4DVar匹配的同化关键参数确定问题。选择基于4DVar的集合资料同化方法生成集合样本,通过将第1个样本极小化迭代过程中产生的预调节信息用于其他样本极小化做预调节,将计算效率提高了2倍。通过时间错位扰动方法增加集合样本数,实现集合样本增加到3倍。对集合方差进行膨胀,并选择水平局地化相关尺度为流函数背景误差水平相关的1.4倍。通过批量数值试验方法确定背景误差与集合预报误差的权重系数,对60个集合样本当集合预报误差权重为0.7时预报效果最好。对北半球夏、冬两季各52 d的批量试验表明,对于南、北半球En4DVar (ensemble 4DVar)较4DVar的改进在冬季主要集中在700—30 hPa,而在夏季主要集中在400—150 hPa。赤道地区受季节影响较小,En4DVar对位势高度、风场与温度的改进都较为明显,且经向风场的改进最为显著。文中研发的集合预报误差在GRAPES全球4DVar中应用的方法合理可行。
英文摘要:
      In the first part of the study, the scientific scheme of how to effectively apply ensemble forecast errors is discussed, and the framework for the application of ensemble forecast errors in the global four dimensional variational data assimilation (4DVar) of the Global Regional Assimilation and PrEdiction System (GRAPES) is identified. Based on this work, the present paper further studies the application of ensemble forecast errors in the GRAPES global 4DVar. The study is focused on solving computational efficiency issues for efficient generation of ensemble samples close to or exceeding 100, as well as key parameters determination issues for matching with the GRAPES global 4DVar. The 4DVar based Ensemble of Data Assimilations (EDA) method is chosen to generate the ensemble samples. By using the preconditioning information generated during the minimization iteration of the first sample to precondition the minimization of other samples, the computational efficiency is increased by a factor of two. By using the Valid-Time-Shifting Perturbation (VTSP) method, the ensemble members are enlarged by a factor of three. The ensemble forecast errors variance inflation method is used, and the horizontal localization scale is selected to be 1.4 times of the horizontal length scale of the stream-function background error. The weight coefficients of background error and ensemble forecast errors are determined by numerical experiments, and the best weight for ensemble forecast error is 0.7 for 60 ensemble members. The results of two 52 d numerical experiments over the winter and summer seasons show that the improvement of ensemble four dimensional variational data assimilation (En4DVar) over 4DVar in the northern and southern hemisphere is mainly concentrated at 700—30 hPa in winter season and 400—150 hPa in the summer season. The tropical region is less affected by seasonal changes, and the improvement of geopotential height, wind field and temperature are obvious with En4DVar, and the improvement of meridional wind field is the most significant. The method developed in this study for the application of ensemble forecast errors in GRAPES Global 4DVar is reasonable and feasible.
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