韩峰,储可宽,刘浩铄,张熠.一次过度预报的温带气旋的观测资料影响性研究.气象科学,2018,(5):637-647 HAN Feng,CHU Kekuan,LIU Haoshuo,ZHANG Yi.Observation impact on an over-forecasted extratropical cyclone.Journal of the Meteorological Sciences,2018,(5):637-647
一次过度预报的温带气旋的观测资料影响性研究
Observation impact on an over-forecasted extratropical cyclone
投稿时间:2017-03-21  修订日期:2017-11-01
DOI:10.3969/2017jms.0091
中文关键词:  爆发性气旋  资料同化  观测资料影响性
英文关键词:Explosive cyclone  Date assimilation  Observation impact
基金项目:国家自然科学基金面上项目(41275059,41675053);江苏省自然科学基金面上项目(BK20161395)
作者单位E-mail
韩峰 南京大学 大气科学学院/中尺度灾害性天气教育部实验室, 南京 210093  
储可宽 南京大学 大气科学学院/中尺度灾害性天气教育部实验室, 南京 210093 kkchu@nju.edu.cn 
刘浩铄 南京大学 大气科学学院/中尺度灾害性天气教育部实验室, 南京 210093  
张熠 南京大学 大气科学学院/中尺度灾害性天气教育部实验室, 南京 210093  
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中文摘要:
      本文针对2015年2月12日到16日发生在东亚的一次预报过度的温带气旋开展了资料同化及资料影响性观测等研究。此次温带气旋的发展与各个主要数值模式的预报相差甚大,并未出现预期中的爆发性增长。针对此次过程,本文采用WRF模式及其变分同化系统开展了模拟与同化试验,主要同化了NCEP全球地面和高空观测资料,修正了此次温带气旋过度预报的问题。经过同化后,减弱了系统的气旋性环流,同时南北温差的减弱也导致了环境场的斜压性的减弱,使得气旋爆发性增长延后,强度减弱,更符合实际观测。与此同时,本文还利用WRF伴随模式WRFPLUS和观测资料影响性模式WRFDA-FSO开展了观测资料影响性的研究,并发现三类资料SOUND,SYNOP,GEOAMV在减小预报误差中的作用最大,进一步的敏感性试验表明仅同化这三种资料可以取得更为理想的预报。
英文摘要:
      An over-forecasetd extratropical cyclone during 12-16 February 2015 in East Asia was studied by Date assimilation and Observation impact. The development of extratropical cyclone is different from some Numerical Weather Prediction Centers'simulations because of non-exist explosive development. By means of WRF and Date Assimilation model, 3DVAR experiment assimilated NCEP ADP Global Upper Air and Surface Weather Observations and reduced forecast error. With the decrease of cyclonic circulation and atmospheric north-south temperature difference which reduces baroclinity, the moment of intensification is delayed and intense is weaker than NWPC's simulation. Meanwhile, 12-hours observation impact was evaluated by WRF adjoint model WRFPLUS and WRFDA-FSO. For each observation type, the total contribution of reducing forecast error was greatest for SOUND, SYNOP, GEOAMV. Sensitive experiment which assimilated three above observations provides more ideal prediction and improves simulation result.
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