公颖,周晓珊. 2020. 不同观测误差确定方法对地基GNSS-ZTD资料同化预报效果影响的对比分析[J]. 气象学报, (0):-, doi:10.11676/qxxb2020.053
不同观测误差确定方法对地基GNSS-ZTD资料同化预报效果影响的对比分析
Comparison and Analysis of Influence of Two Different Observation Error Determination Methods on Ground-based GNSS-ZTD Data Assimilation
投稿时间:2019-07-16  修订日期:2020-01-16
DOI:10.11676/qxxb2020.053
中文关键词:  观测误差确定,地基GNSS-ZTD,资料同化,3DVAR
英文关键词:Observation error determination, Ground-based GNSS-ZTD, Data assimilation, 3DVAR
基金项目:国家自然科学基金(41705011)
作者单位E-mail
公颖 中国气象局沈阳大气环境研究所 gongying74@sohu.com 
周晓珊 中国气象局沈阳大气环境研究所 Xiaoshan_zhou@163.com 
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中文摘要:
      利用2016年6~8月华北~东北地区的地基GNSS-ZTD观测资料、东北区域中尺度数值预报系统,以2016年6~8月时段内13天的强降水为例,开展了基于Desroziers等(2005)理论的Des方法和传统方法进行观测误差确定的ZTD资料同化对比试验研究,探讨Des方法相对于传统观测误差确定方法对ZTD资料同化预报效果的影响,并以未做ZTD资料同化的试验为对照试验(以下称Ctl试验),考察ZTD资料在数值模式中的同化应用效果,结果表明:(1)Des方法得到的ZTD观测误差诊断值较为合理,诊断值站点间差别较大,说明逐站进行观测误差统计的必要性。(2)ZTD资料同化使强降水的强度、落区预报性能获得提高,使温、湿、风等要素的预报向观测靠近,Des方案预报效果优于传统方案。(3)对2016年7月25日华北~东北强降水过程进行了分析,整体而言,ZTD资料同化有效增强了对流层中低层初始湿度场,修正了积分初期水凝物含量与位置,进而改善了降水预报效果,修正了Ctl试验对辽宁东部地区强降水的明显漏报,且通过降水的反馈作用改进了温度与风场预报效果。
英文摘要:
      The Regional Meso-scale Numerical Prediction System of Northeast China(RMNPSNC) was used to study the application of an observation error diagnostic method based on Desroziers et al (2005) (hereinafter referred to as Des method) in the GNSS-ZTD 3DVAR assimilation. Basing on that, the comparison of Des method with traditional observation error determination method was carried out by ZTD assimilating and forecasting tests using thirteen rainfall cases of the period June to August, 2016. Then, the contrasting study of assimilating and forecasting results with to without ZTD data was launched to evaluate the effect of assimilating ZTD data into the RMNPSNC. The results are summarized in the following points: (1) The numerical size of ZTD observation error diagnosis value obtained by Des method was relatively reasonable, and the differences of diagnosis value between stations were large, indicating the necessity of taking observation error statistics station by station. (2) ZTD data assimilation improved forecasting performance of intensity and distribution of heavy rain and made the forecast of temperature, humidity and wind closer to the observation. The forecasting effect of Des schemes was better than those of traditional schemes. (3) For the heavy rain process in Northeast China during July 25, 2016, ZTD assimilation effectively strengthened the initial humidity field, improved the content and spatial distribution of hydrometeor at the beginning hours of integral, modified the failed precipitation forecast in the east of Liaoning Province, and then improved temperature and wind prediction by precipitation feedback.
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