张旭斌,万齐林,薛纪善,丁伟钰,李昊睿. 2015. 风廓线雷达资料质量控制及其同化应用[J]. 气象学报, 73(1):159-176, doi:10.11676/qxxb2015.015
风廓线雷达资料质量控制及其同化应用
Quality control of wind profile radar data and its application to assimilation
投稿时间:2014-01-10  修订日期:2014-10-11
DOI:10.11676/qxxb2015.015
中文关键词:  风廓线雷达  质量控制  变分资料同化
英文关键词:Wind profile radar  Quality control  Variational data assimilation
基金项目:中国气象局数值预报(GRAPES)发展专项(GRAPES-FZZX-2013-24、 GRAPES-FZZX-2014-25)、广东省气象局气象科技计划项目(2012B11、 2014B03)、公益性行业(气象)科研专项(GYHY201406013)、广东科技计划项目(2012A061400012)、十二五科技计划项目(2012BAC22B00)。
作者单位
张旭斌 中国气象局/广东省区域数值天气预报重点实验室, 广州, 510080
中国气象局广州热带海洋气象研究所, 广州, 510080 
万齐林 中国气象局/广东省区域数值天气预报重点实验室, 广州, 510080
中国气象局广州热带海洋气象研究所, 广州, 510080 
薛纪善 中国气象局广州热带海洋气象研究所, 广州, 510080
中国气象科学研究院, 北京, 100081 
丁伟钰 中国气象局/广东省区域数值天气预报重点实验室, 广州, 510080
中国气象局广州热带海洋气象研究所, 广州, 510080 
李昊睿 中国气象局/广东省区域数值天气预报重点实验室, 广州, 510080
中国气象局广州热带海洋气象研究所, 广州, 510080 
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中文摘要:
      为更好地同化风廓线雷达观测资料开展了相应的质量控制与同化应用研究。针对2013年5月广东地区13部风廓线雷达的观测数据,采用经验正交函数(EOF) 分析方法对其进行质量控制。相比原始观测,经过质量控制的风场提高(降低)了来自时空大(小)尺度的贡献,较好地滤除了小尺度高频脉动,也较好地保留了大尺度平均状态与局地中小尺度系统的共同影响,并且更加接近ECMWF再分析场。此外,还对质量控制后的数据进行了垂直稀疏化。分别计算了质量控制前、后风廓线雷达观测与NCEP 6 h预报场的差值,对比差值的特征发现,经过质量控制的数据的观测增量更好地满足了高斯分布与无偏假设。针对一个实际天气个例,基于GRAPES 3D-Var同化系统,分析了质量控制后的风廓线雷达资料对模式分析与预报的影响。试验表明,在循环同化过程中加入风廓线雷达资料可以更好地描述模式初始场低层风场的特征,从而对强降水的位置与强度做出更好的预报。针对2013年5月的批量试验表明,同化风廓线雷达资料使短期降水预报有明显的改善。
英文摘要:
      In order to improve the assimilation of wind profile radar (WPRD) observations, research is carried out to develop the quality control (QC) and assimilation application of WPRD. For the 13 WPRD observations in Guangdong during May 2013, the method based on empirical orthogonal function (EOF) analysis is used to do the QC of WPRD. Compared with the original observations, the new datasets of observations after QC increase (decrease) the contribution of large (small) scales in both space and time, filter the small-scale and high-frequency noises very well, retain the combined effects of large-scale mean states and small-scale local systems successfully, as well as are closer to the ECMWF reanalysis data. After QC, the datasets of observations are thinned in the vertical. The differences between the NCEP 6 h forecasts and the observations datasets before and after QC are calculated, respectively. These differences show that, the distributions of the innovations corresponding to the observations after QC are more Gaussian-like and unbiased. Based on the GRAPES 3D-Var data assimilation system, the WPRD observations after QC are used in a real-case study characterized by severe rainfall, to illustrate the influence of WPRD observations on model analysis and forecasting. Assimilating WPRD observations in the update cycle is showed to improve the descriptions of the wind fields at the lower levels in the initial conditions, and the corresponding forecast performance of precipitation in terms of both location and intensity is improved as a result. A series of the data assimilation experiments for May 2013 shows that, the assimilation with WPRD observations significantly outperforms the one without WPRD observations with regard to short-range precipitation prediction.
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