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Volume 29 Issue 2

Mar.  2012

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Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2012 >  29(2): 344-358

Radar Data Assimilation of the GRAPES Model and Experimental Results in a Typhoon Case

  • 1.

    Shanghai Meteorological Center, Shanghai 200030;State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081;Chongqing Meteorological Bureau, Chongqing 401147;Nanjing University of Information Science & Technology, Nanjing 210044


doi: 10.1007/s00376-011-1063-y

  • Constructing β-mesoscale weather systems in initial fields remains a challenging problem in a mesoscale numerical weather prediction (NWP) model. Without vertical velocity matching the β-mesoscale weather system, convection activities would be suppressed by downdraft and cooling caused by precipitating hydrometeors. In this study, a method, basing on the three-dimensional variational (3DVAR) assimilation technique, was developed to obtain reasonable structures of β-mesoscale weather systems by assimilating radar data in a next-generation NWP system named GRAPES (the Global and Regional Assimilation and Prediction System) of China. Single-point testing indicated that assimilating radial wind significantly improved the horizontal wind but had little effect on the vertical velocity, while assimilating the retrieved vertical velocity (taking Richardson's equation as the observational operator) can greatly improve the vertical motion. Experiments on a typhoon show that assimilation of the radial wind data can greatly improve the prediction of the typhoon track, and can ameliorate precipitation to some extent. Assimilating the retrieved vertical velocity and rainwater mixing ratio, and adjusting water vapor and cloud water mixing ratio in the initial fields simultaneously, can significantly improve the tropical cyclone rainfall forecast but has little effect on typhoon path. Joint assimilating these three kinds of radar data gets the best results. Taking into account the scale of different weather systems and representation of observational data, data quality control, error setting of background field and observation data are still requiring further in-depth study.
  • [1] FU Weiwei, 2012: Altimetric Data Assimilation by EnOI and 3DVAR in a Tropical Pacific Model: Impact on the Simulation of Variability, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 823-837.  doi: 10.1007/s00376-011-1022-7
    [2] Yujie PAN, Mingjun WANG, 2019: Impact of the Assimilation Frequency of Radar Data with the ARPS 3DVar and Cloud Analysis System on Forecasts of a Squall Line in Southern China, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 160-172.  doi: 10.1007/s00376-018-8087-5
    [3] Rong KONG, Ming XUE, Edward R. MANSELL, Chengsi LIU, Alexandre O. FIERRO, 2024: Assimilation of GOES-R Geostationary Lightning Mapper Flash Extent Density Data in GSI 3DVar, EnKF, and Hybrid En3DVar for the Analysis and Short-Term Forecast of a Supercell Storm Case, ADVANCES IN ATMOSPHERIC SCIENCES, 41, 263-277.  doi: 10.1007/s00376-023-2340-2
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    [5] CHU Kekuan, TAN Zhemin, Ming XUE, 2007: Impact of 4DVAR Assimilation of Rainfall Data on the Simulation of Mesoscale Precipitation Systems in a Mei-yu Heavy Rainfall Event, ADVANCES IN ATMOSPHERIC SCIENCES, 24, 281-300.  doi: 10.1007/s00376-007-0281-9
    [6] Baofeng JIAO, Lingkun RAN, Na LI, Ren CAI, Tao QU, Yushu ZHOU, 2023: Comparative Analysis of the Generalized Omega Equation and Generalized Vertical Motion Equation, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 856-873.  doi: 10.1007/s00376-022-1435-5
    [7] Ji-Hyun HA, Dong-Kyou LEE, 2012: Effect of Length Scale Tuning of Background Error in WRF-3DVAR System on Assimilation of High-Resolution Surface Data for Heavy Rainfall Simulation, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 1142-1158.  doi: 10.1007/s00376-012-1183-z
    [8] Zhaorong ZHUANG, Nusrat YUSSOUF, Jidong GAO, 2016: Analyses and Forecasts of a Tornadic Supercell Outbreak Using a 3DVAR System Ensemble, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 544-558.  doi: 10.1007/s00376-015-5072-0
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    [13] Dongmei XU, Zhiquan LIU, Shuiyong FAN, Min CHEN, Feifei SHEN, 2021: Assimilating All-sky Infrared Radiances from Himawari-8 Using the 3DVar Method for the Prediction of a Severe Storm over North China, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 661-676.  doi: 10.1007/s00376-020-0219-z
    [14] Dongmei XU, Feifei SHEN, Jinzhong MIN, Aiqing SHU, 2021: Assimilation of GPM Microwave Imager Radiance for Track Prediction of Typhoon Cases with the WRF Hybrid En3DVAR System, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 983-993.  doi: 10.1007/s00376-021-0252-6
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    • Manuscript History

    Manuscript received: 10 March 2012
    Manuscript revised: 10 March 2012
    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

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    Radar Data Assimilation of the GRAPES Model and Experimental Results in a Typhoon Case

    • 1. Shanghai Meteorological Center, Shanghai 200030;State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081;Chongqing Meteorological Bureau, Chongqing 401147;Nanjing University of Information Science & Technology, Nanjing 210044
    • Manuscript received: 2012-03-10
    • Manuscript revised: 2012-03-10

    Abstract: Constructing β-mesoscale weather systems in initial fields remains a challenging problem in a mesoscale numerical weather prediction (NWP) model. Without vertical velocity matching the β-mesoscale weather system, convection activities would be suppressed by downdraft and cooling caused by precipitating hydrometeors. In this study, a method, basing on the three-dimensional variational (3DVAR) assimilation technique, was developed to obtain reasonable structures of β-mesoscale weather systems by assimilating radar data in a next-generation NWP system named GRAPES (the Global and Regional Assimilation and Prediction System) of China. Single-point testing indicated that assimilating radial wind significantly improved the horizontal wind but had little effect on the vertical velocity, while assimilating the retrieved vertical velocity (taking Richardson's equation as the observational operator) can greatly improve the vertical motion. Experiments on a typhoon show that assimilation of the radial wind data can greatly improve the prediction of the typhoon track, and can ameliorate precipitation to some extent. Assimilating the retrieved vertical velocity and rainwater mixing ratio, and adjusting water vapor and cloud water mixing ratio in the initial fields simultaneously, can significantly improve the tropical cyclone rainfall forecast but has little effect on typhoon path. Joint assimilating these three kinds of radar data gets the best results. Taking into account the scale of different weather systems and representation of observational data, data quality control, error setting of background field and observation data are still requiring further in-depth study.

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