非高斯分布观测误差资料的变分质量控制对暴雨预报的影响
投稿时间: 2015-11-12  最后修改时间: 2016-02-03  点此下载全文
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作者单位E-mail
马旭林 南京信息工程大学 xulinma@nuist.edu.cn 
和杰 南京信息工程大学  
周勃旸 南京信息工程大学  
李琳琳 南京信息工程大学  
计燕霞 南京信息工程大学  
郭欢 南京信息工程大学  
基金项目:The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)
中文摘要:观测资料的质量控制直接影响数值预报资料同化的分析质量。本文针对GRAPES区域同化和预报系统的三维变分,发展了基于“高斯分布与均匀分布”观测误差模型的变分质量控制方案,讨论了该方案初始启动和关键参数,并检验分析了其适用性与有效性。同时,以全球预报系统(GFS)资料作为背景场,利用探空、地面、船舶、飞机、云迹风等常规观测资料和COSMIC卫星反演资料进行同化和预报,分析了华南地区特大暴雨的个例试验和2013年8月共31天的批量试验。试验结果表明: 变分质量控制能够依据观测资料的不同质量对观测权重进行合理调整,对位势高度、气压、风、比湿的分析增量场和分析场改善显著,尤其在强降水区具有更加明显的效果;对降水落区、降水强度及中心位置的预报质量具有较好的提高,特别对暴雨、大暴雨等较大降水量级的预报能力反映出更好的改善效果,充分显示了变分质量控制在中小尺度剧烈天气过程中对同化分析和预报的重要作用。
中文关键词:变分质量控制,观测误差,三维变分,资料同化,数值预报
 
Impact of variational quality control of Non-Gaussian distribution observation error on heavy rainfall prediction
Abstract:Quality control of observations directly affects the analysis quality of numerical prediction data assimilation. Based on the “Gaussian plus flat” distribution model of observation error, this paper developed the variational quality control scheme for the 3D-Var of assimilation and forecast system in regional GRAPES, and discussed the initial startup and key parameters of this scheme, furthermore analyzed and verified its applicability and effectiveness. Meanwhile, the heavy rainfall in southern China was seclected as a case for assimilating and forecasting analysis by using the Global Forecast System (GFS) data as the background fields and the conventional observation data including TEMP, SYNOP, SHIPS, AIREP, SATOB and COSMIC satellite retrievals data, so also do batch tests of a total of 31 days on August 2013. The results showed that variational quality control reasonably adjust observation weight according to different quality of observation, and significantly improve the analysis increment field and analysis field of geopotential height, pressure, wind, specific humidity, especially it has a more positive effect on heavy rainfall areas. The forecast quality are farther ameliorated for the precipitation area, precipitation intensity and center position of precipitation, the ability to forecast of the heavy rainfall, the big rainstorms and other precipitation larger level reflect the better effect particularly. Therefore, variational quality control plays an important role in assimilating and forecasting analysis of the mesoscale and microscale severe weather processes.
keywords:Variational quality control, Observation error, 3DVAR, Data assimilation, Numerical weather prediction
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