主办单位:中国气象局沈阳大气环境研究所
国际刊号:ISSN 1673-503X
国内刊号:CN 21-1531/P

气象与环境学报 ›› 2022, Vol. 38 ›› Issue (6): 10-19.doi: 10.3969/j.issn.1673-503X.2022.06.002

• 论文 • 上一篇    下一篇

常规观测同化对吉林一次短时强降水过程数值预报的影响

曲美慧1,2(),朱文刚3,4,*(),赵淑红5,朱晓彤1,2,涂钢1,2,高枞亭1,2   

  1. 1. 吉林省气象科学研究所, 吉林长春 130062
    2. 长白山气象与气候变化吉林省重点实验室, 吉林长春 130062
    3. 山东省气象防灾减灾重点实验室, 山东济南 250031
    4. 山东省气象科学研究所, 山东济南 250031
    5. 长白县气象局, 吉林白山 134400
  • 收稿日期:2021-01-25 出版日期:2022-12-28 发布日期:2022-12-27
  • 通讯作者: 朱文刚 E-mail:279995024@qq.com;zhu122812@163.com
  • 作者简介:曲美慧, 女, 1988年生, 工程师, 主要从事数值模式和资料同化应用研究, E-mail: 279995024@qq.com
  • 基金资助:
    吉林省气象局科研课题(2015008);山东省气象科学研究所面上项目(SDQXKF2015M03);吉林省科技发展计划项目(20180101016JC)

Influence of conventional observation assimilation on numerical prediction of a short-time heavy precipitation process in Jilin province

Mei-hui QU1,2(),Wen-gang ZHU3,4,*(),Shu-hong ZHAO5,Xiao-tong ZHU1,2,Gang TU1,2,Zong-ting GAO1,2   

  1. 1. Jilin Meteorological Sciences Institute, Changchun 130062, China
    2. Jilin Province Key Laboratory of Changbai Mountain Meteorology & Climate Change, Changchun 130062, China
    3. Key Laboratory for Meteorological Disaster Prevention and Mitigation of Shandong Province, Ji'nan 250031, China
    4. Shandong Institute of Meteorological Sciences, Ji'nan 250031, China
    5. Changbai Meteorological Service, Baishan 134400, China
  • Received:2021-01-25 Online:2022-12-28 Published:2022-12-27
  • Contact: Wen-gang ZHU E-mail:279995024@qq.com;zhu122812@163.com

摘要:

基于WRF(Weather Research Forecast)模式及其3D-Var(Three-Dimensional Variational assimilation)变分同化系统, 对2017年7月13日吉林省一次短时强降水过程进行飞机报、自动站、常规探空和GPS水汽资料的数值预报试验, 开展了不同常规观测同化对模式初始场和预报场的影响研究。结果表明: 常规观测的同化能改善初始湿度场、温度场, 使初始时刻吉林省中部湿度增加、实况雨带南北两侧温差增强, 能更好地预报物理量的演变过程。不同常规观测资料同化对湿度场、垂直速度、风场和对流有效位能的预报影响有差异。同化飞机报资料增加了降水落区的水汽含量、相对湿度, 风切变与强垂直运动区域重叠, 有利于冷暖气流交汇和输送。同化自动站资料能增加降水中心及吉林东南区域的水汽含量, 而温度场以减弱为主。同化GPS水汽资料对降水中心风切变预报的影响最显著, 对温度场调整不明显。同化探空资料, 垂直速度和风场的预报发生偏移。24h累积降水TS评分、漏报率、空报率大多优于未同化, 小雨、中雨、大暴雨量级的站点TS评分显著提高。整体来看同化飞机报资料对降水的预报与实况最接近, 尤其是大于100mm的降水落区、强度, 大雨及以上量级降水TS评分更高。

关键词: WRF-3VDAR, 资料同化, 短时强降水, 数值预报

Abstract:

Based on the Weather Research Forecast (WRF) Model and its 3D-Var (three-dimensional variational assimilation) system, the numerical prediction experiments of water vapor data from aircraft, automatic station, conventional sounding and GPS during a short-time heavy rainfall in Jilin province on July 13, 2017, were carried out to study the influence of variant conventional observation assimilation on the initial field and forecast field of the model.The results show that the assimilation of conventional observation can improve the initial humidity field and temperature field, increase the humidity in the central part of Jilin province at the initial moment, and strengthen the temperature difference between the north and south sides of the real rain belt, and better predict the evolution process of the physical quantity.The influence of different conventional observation data assimilation on the prediction of humidity field, vertical velocity, wind field, and convective effective potential energy appears discrepantly.Assimilation of aircraft data increases regional overlaps between the strong vertical movement and the moisture, relative humidity, and wind shear in the precipitation area, being conducive to the intersection and transport of warm and cold airmass.Assimilation of automatic station data can increase the vapor in the precipitation center and southeast Jilin province, while the temperature field is mainly weakened.The assimilation of GPS vapor data has the most significant influence on the central wind shear forecast of precipitation, but has a less significant effect on the temperature field adjustment.The prediction of vertical velocity and wind field shows deviations due to the assimilation of radiosonde data.The TS scores, missing report rates, and empty report rates of 24-hour accumulated precipitation are better than those of unassimilated precipitation, and the TS scores of light rain, moderate rain, and heavy rainstorm are significantly improved.In general, the forecast of precipitation from the assimilated aircraft data is the closest to the real situation.Higher TS scores appear in the prediction of heavy and the above rain, especially in the area and intensity of 100-milli meter precipitation.

Key words: WRF-3VDAR, Data assimilation, Short-time heavy precipitation, Numerical weather forecasting

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