袁招洪. 2005. GPS可降水量资料应用于MM5模式的变分同化试验[J]. 气象学报, 63(4):391-404, doi:10.11676/qxxb2005.040
GPS可降水量资料应用于MM5模式的变分同化试验
VARIATIONAL ASSIMILATION OF GPS PRECIPITABLE WATER INTO MM5 MESOSCALE MODEL
投稿时间:2004-07-15  修订日期:2004-10-20
DOI:10.11676/qxxb2005.040
中文关键词:  GPS, 可降水量,三维变分同化,MM5
英文关键词:GPS, Precipitable water(PW), 3DVAR, MM5
基金项目:中国科学院知识创新工程重要方向项目(KJCX2-SW-T1-3)
作者单位
袁招洪 上海市气象局上海200030 
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中文摘要:
      利用建立在长江三角洲地区GPS观测网中13个站点的资料对2002年6月27~28日影响长江三角 洲地区的降水过程进行了MM5背景误差调节和可降水量资料的三维变分同化试验。试验结果 表明:背景误差对三维变分同化的效果起着关键作用,模式变量(u,v,T,p和q)误差的水平尺度与NMC方法的平均时间长度有直接的关系。利用NMC方法重新构建的背景误差更 接近实际的背景误差。三维变分技术能有效地同化GPS可降水量资料。GPS可降水量资料的同化使用不仅能调整模式 初始湿度场,而且也能相应地调整模式初始气压场、温度场和风场。GPS可降水量资料的同 化有利于减小模式初始场对可降水量的分析误差,并且有利于减小模式积分初期(3~6 h)可降水量的预报误差。与没有进行GPS可降水量同化相比,通过GPS可降水量资料的三维变分 同化,使MM5模式6 h和24 h累计降水能力得到提高,改善了MM5模式降水预报性能。总体上,GPS可降水量资料的变分同化有利于模式降水预报能力的提高。
英文摘要:
      The GPS precipitable water (PW) from 13 GPS sites in the Yangtze delta is explor ed to investigate the tune of background errors and the three dimensional variat ional assimilation of MM5 on rainfall event occurred from June 27 to 28,2002. T he results show: Background errors (BE) play a key role in the three dimensional variational assimilation of MM5. The horizontal scalelength of model variables ( u,v,T,pand q) is closely related to the average time of NMC technique. The scalelength of model variables is different for each other, which value is associated with the vertical height of the variable on the MM5 level. The BE cal culated by NMC technique reach the true BE more closely than that provided by MM 5-3DVAR system.GPS PW data can be assimulated into MM5 by using 3DVAR technique. After GPS PW d ata assimulation, the initial humidity field can be reanalyzed while the initial temperature, pressure and wind fields also being modified. 3DVAR of GPS PW is b enefitial to reduce analysis bias of PW in initial fields which can result in re straining PW prediction bias during the earlier period (3-6 h) of model integrat ion so as to improve PW prediction. The PW prediction improvement is related to the GPS receiver location in the area covered by GPS networks. By comparing the results with no GPS IPW ass imilation, we find that GPS PW assimilation can increase the accuracy of 6 h and 24 h accumulated precipitation prediction so as to improve the precipitation pr ediction ability of MM5. On the whole, GPS PW data assimilation will improve the precipitation prediction of MM5. However, there are several difficulties that will impact the GPS assimilation, w hich are how to get the true background errors and GPS PW errors at real time. I t will be further researches to develop a new technique to calculate more reason able background errors and GPS PW errors.
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