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FY-2E晴空风矢同化对台风分析和预报的影响研究
引用本文:隋新秀,王振会,鲍艳松,张庆,吴月.FY-2E晴空风矢同化对台风分析和预报的影响研究[J].热带气象学报,2018,34(6):819-831.
作者姓名:隋新秀  王振会  鲍艳松  张庆  吴月
作者单位:1.南京信息工程大学气象灾害预报预警与评估协同创新中心/中国气象局气溶胶-云-降水重点实验室,江苏 南京 210044
基金项目:国家自然科学基金41175035国家自然科学基金40475018国家重点基础研究发展计划2009CB421502江苏高校优势学科建设工程资助项目PAPD
摘    要:为了研究“二阶差分法”反演的晴空区风矢同化在台风分析和预报中的作用,以1509号台风“灿鸿”和1211号台风“海葵”为例,首先利用WRF-3DVAR系统对晴空风矢进行同化,探讨了晴空风矢的引入对模式初始场的影响。然后利用WRF模式对两个个例分别进行48 h的预报试验。通过对比控制试验和同化试验,结果表明,同化晴空风矢资料能够对初始风场和位势高度场进行合理的调整,在台风周围引导气流的作用下,台风路径与实况更靠近,从而提高了台风路径的预报效果。除此之外,同化晴空风矢对台风强度以及风场预报也有一定的改善作用,还可更准确地预报出降水的落区及雨强,提高降水预报质量。因此,晴空风矢的引入,有利于改善模式的初始场,从而提高WRF模式对台风的预报能力。 

关 键 词:天气预报    台风数值预报    三维变分同化    静止卫星    晴空导风
收稿时间:2017-11-18

STUDY ON THE APPLICATION OF AMVS IN CLOUD-FREE REGIONS BASED ON FY-2E SATELLITE IN TYPHOON ANALYSIS AND FORECAST
Abstract:Atmospheric motion vectors (AMVs) in cloud-free regions are derived by using the Second Order method. The purpose of this study is to investigate the effects of AMVs data assimilation on typhoon's initial fields and forecast fields. Taking Typhoons Chan-hom (1509) and Haikui (1211) as illustrating cases, this paper assimilates the AMVs in the WRF model by using the WRF-3Dvar system, and then discusses effects of AMVs on the assimilated initial fields. At last, with the initial fields attained by assimilation, the processes of Typhoons Chan-hom and Haikui have been studied respectively by 48-hour numerical experiments through the WRF model. Comparing the control with the assimilation experiment shows that, after assimilating the AMVs, the wind and geopotential height of initial fields around the typhoons become more reasonable, and thus the steering current guiding them to move to the correct location becomes stronger. As a result, the numerical track predictions can be improved. In addition, the assimilation of AMVs can improve the predictions of typhoon intensity and wind fields in such way that it can yield precipitation forecasts with more accurate location and intensity. Therefore, assimilating AMVs in cloud-free regions can well improve the initial fields in the WRF model, thus improving its forecasting ability. 
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