刘瑞婷,陈明轩,肖现,秦睿,高峰,杨璐,吴剑坤,孙娟珍. 2021. 雷达资料快速更新四维变分同化中增加地面资料同化对强对流临近数值预报的影响[J]. 气象学报, 79(6):921-942, doi:10.11676/qxxb2021.065
雷达资料快速更新四维变分同化中增加地面资料同化对强对流临近数值预报的影响
The impact of assimilating surface observations in rapid-refresh four-dimensional Variational Radar Data Assimilation System on model-based severe convection nowcasting
投稿时间:2021-03-10  修订日期:2021-07-19
DOI:10.11676/qxxb2021.065
中文关键词:  雷达四维变分分析系统  数据同化  地面观测  数值模拟  临近预报
英文关键词:VDRAS  Data assimilation  Surface observations  Numerical simulation  Nowcasting
基金项目:北京市自然科学基金项目(8204060、8212026、8192016)、国家重点研发计划项目(2018YFC1507801)、国家自然科学基金项目(41575050、41805034)、灾害天气国家重点实验室开放课题(2019LASW-B06)
作者单位E-mail
刘瑞婷 北京城市气象研究院北京100089
中国气象科学研究院灾害天气国家重点实验室北京100081 
 
陈明轩 北京城市气象研究院北京100089 mxchen@ium.cn 
肖现 北京城市气象研究院北京100089  
秦睿 北京城市气象研究院北京100089  
高峰 北京城市气象研究院北京100089  
杨璐 北京城市气象研究院北京100089  
吴剑坤 北京城市气象研究院北京100089  
孙娟珍 美国国家大气研究中心科罗拉多州博尔德80307  
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中文摘要:
      基于雷达资料快速更新四维变分同化(RR4DVar)技术和三维数值云模式发展的快速更新雷达四维变分分析系统(VDRAS),通过在系统中加入地面自动气象站观测资料的同化方法,对发生在北京地区的10个强对流过程开展了地面资料同化的高分辨率模拟分析和检验评估,并与已经业务使用的地面资料融合方法进行对比。研究结果发现,地面观测资料同化使边界层1 km高度以下的分析场改善最为明显,风速和风向的均方根误差分别平均降低0.1 m/s和7.2°,温度的均方根误差降低0.2℃。模式最低层100 m高度的风速均方根误差降低0.5 m/s,风速的误差随高度上升逐渐增大。模式最低层风向的均方根误差降低15.5°,温度的均方根误差降低0.4℃,且1.5 km高度以下的温度偏差都减小。区域内地面10 m高风速的均方根误差平均降低0.2 m/s,风向的均方根误差降低10.8°,地面2 m气温的偏差也降低。随着预报时效的延长,地面温度和风场的误差不断增大,但地面资料同化方法在一定程度上可以提高1 h内地面气象要素的预报效果。对2019年5月17日北京地区局地强对流新生和增强过程的详细分析表明,地面自动气象站观测资料的同化方法相对于融合,可以通过更细致准确地分析低层大气的热动力特征,改善低层气象要素的预报效果。在此基础上,通过探究对流单体的局地触发机理发现,海风锋辐合线与城市的相互作用一定程度上影响了对流的局地新生和发展,该同化方法可以进一步提高北京地区局地突发强对流的临近数值预报能力。
英文摘要:
      The high-resolution numerical simulations and verifications of 10 convective cases that occurred in Beijing have been conducted by assimilating surface observations in a four-dimensional Variational Doppler Radar Analysis System (VDRAS) based on the rapid-refresh 4D variational assimilation (RR4DVar) technique of multi-radar observations and three-dimensional cloud-scale numerical model. Compared with the surface observations fusion scheme, the verification results show that the surface observations assimilation obviously can improve analysis results below 1 km boundary layer height, and the root mean square errors (RMSE) of simulated wind speed and wind direction are respectively reduced by 0.1 m/s and 7.2° on average, the RMSE of temperature is reduced by 0.2℃. The RMSE of wind speed is decreased by 0.5 m/s at the lowest model level of 100 m, and the error of wind speed increases with height below 3 km. The RMSEs of wind direction and temperature are respectively reduced by 15.5° and 0.4℃ at the lowest model level, and the errors of temperature are decreased at all levels below 1.5 km height. The RMSEs of 10 m surface wind speed and wind direction are respectively reduced by 0.2 m/s and 10.8°, and the error of 2 m surface temperature is also reduced. In addition, the surface observations assimilation can to a certain extent improve the 1 hour forecast of surface fields, whereas the RMSEs of regional surface temperature and wind field increase with forecast time. Combined with the detailed analysis of localized and rapidly intensified convection case that occurred in Beijing on 17 May, 2019, it is found that the surface observations assimilation can better represent the dynamical and thermodynamical characteristics in the lower atmosphere with more details, and thus improves the forecast of low-level meteorological variables. Further investigation of the local trigger mechanism of convection indicates that the interaction between the convergence line of sea breeze and the urban condition to some extent has affected the trigger and development of local convection in Beijing. This method can further improve the nowcasting of localized and rapidly intensified convection in Beijing.
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