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

A comparative analysis of influences of two different observation error determination methods on the assimilation of ground-based GNSS-ZTD data

中文关键词:  观测误差确定  地基全球卫星导航系统的天顶总延迟（GNSS-ZTD）数据  资料同化  三维变分

英文关键词:Observation error determination  Ground-based GNSS-ZTD  Data assimilation  3DVar

中文摘要:

      利用2016年6—8月华北—东北地区的地基全球卫星导航系统的天顶总延迟（GNSS-ZTD）观测资料、东北区域中尺度数值预报系统，以2016年6—8月的13 d强降水为例，开展基于Desroziers等（2005）理论的Des方法和传统方法进行观测误差确定的天顶总延迟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天顶总延迟资料同化预报效果的影响，并以未做天顶总延迟资料同化的试验为对照试验，考察天顶总延迟资料在数值模式中的同化应用效果。结果表明：（1）Des方法得到的天顶总延迟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诊断的必要性；（2）天顶总延迟资料同化使强降水的强度、落区预报性能得到提高，使温、湿、风等要素的预报与观测接近，Des方案同化分析、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同化预报分析，整体而言，天顶总延迟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，修正了对照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。基于Des方法逐站诊断观测误差相比传统方法得到的观测误差更为合理，因此能够提高天顶总延迟资料的同化预报效果，同化天顶总延迟资料能够提高降水及温、湿、风等气象要素的预报水平。

英文摘要:

      The Regional Meso-scale Numerical Prediction System of Northeast China (RMNPSNC) is used to study the application of an observation error diagnostic method based on Desroziers et al (2005) (here in after Des method) in the GNSS-ZTD (Global Navigation Satellite System-Zenith Total Delay, ZTD) 3DVar assimilation. The Des method is compared with traditional observation error determination method based on ZTD assimilation and forecast experiments of thirteen rainfall cases during the period from June to August 2016. A comparative study is then conducted based on assimilation and forecast results with and without ZTD data to evaluate the effect of assimilating ZTD data into the RMNPSNC. The results are summarized as follows. (1) The errors of ZTD observations diagnosed by the Des method are relatively reasonable, and the differences of diagnostic values between stations are large, indicating the necessity of diagnosing observation errors station by station. (2) Assimilation of the ZTD data improves the forecast of the intensity and distribution of heavy rain and the forecasts of temperature, humidity and wind are closer to observations. The analysis and forecast effects of the Des scheme are better than that of the traditional scheme. (3) For the heavy rain process in Northeast China on 25 July 2016, the assimilation of ZTD effectively increases the initial humidity field, improves the content and spatial distribution of hydrometeors at the initial hours of integration, corrects the failed precipitation forecast in the east of Liaoning province, and improves temperature and wind forecasts due to realistic precipitation feedback. The diagnostic ZTD observation errors obtained by the Des method are more reasonable than that by traditional method. Therefore, the assimilation and forecast can be improved by using the Des method. Progress in the forecast of rain, temperature, humidity and wind can be made through assimilating the ZTD.