Advanced Search
Impact Factor: 5.8

Volume 22 Issue 3

May  2005

Article Contents
Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2005 >  22(3): 415-427

Assimilation and Simulation of Typhoon Rusa (2002) Using the WRF System

  • 1.

    Chinese Academy of Meteorological Sciences, Beijing 100081;National Center for Atmospheric Research, Boulder, Colorado 80307, USA;Shanghai Weather Forecast Center, Shanghai 200030,National Center for Atmospheric Research, Boulder, Colorado 80307, USA,National Center for Atmospheric Research, Boulder, Colorado 80307, USA,National Center for Atmospheric Research, Boulder, Colorado 80307, USA,Chinese Academy of Meteorological Sciences, Beijing 100081,Shanghai Weather Forecast Center, Shanghai 200030


doi: 10.1007/BF02918755

  • Using the recently developed Weather Research and Forecasting (WRF) 3DVAR and the WRF model, numerical experiments are conducted for the initialization and simulation of typhoon Rusa (2002).The observational data used in the WRF 3DVAR are conventional Global Telecommunications System (GTS) data and Korean Automatic Weather Station (AWS) surface observations. The Background Error Statistics (BES) via the National Meteorological Center (NMC) method has two different resolutions, that is, a 210-km horizontal grid space from the NCEP global model and a 10-kn horizontal resolution from Korean operational forecasts. To improve the performance of the WRF simulation initialized from the WRF 3DVAR analyses, the scale-lengths used in the horizontal background error covariances via recursive filter are tuned in terms of the WRF 3DVAR control variables, streamfunction, velocity potential, unbalanced pressure and specific humidity. The experiments with respect to different background error statistics and different observational data indicate that the subsequent 24-h the WRF model forecasts of typhoon Rusa's track and precipitation are significantly impacted upon the initial fields. Assimilation of the AWS data with the tuned background error statistics obtains improved predictions of the typhoon track and its precipitation.
  • [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] 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
    [3] ZHENG Xiaogu, WU Guocan, ZHANG Shupeng, LIANG Xiao, DAI Yongjiu, LI Yong, , 2013: Using Analysis State to Construct a Forecast Error Covariance Matrix in Ensemble Kalman Filter Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1303-1312.  doi: 10.1007/s00376-012-2133-5
    [4] ZHENG Jing, Jun LI, Timothy J. SCHMIT, Jinlong LI, Zhiquan LIU, 2015: The Impact of AIRS Atmospheric Temperature and Moisture Profiles on Hurricane Forecasts: Ike (2008) and Irene (2011), ADVANCES IN ATMOSPHERIC SCIENCES, 32, 319-335.  doi: 10.1007/s00376-014-3162-z
    [5] Seung-Woo LEE, Dong-Kyou LEE, 2011: Improvement in Background Error Covariances Using Ensemble Forecasts for Assimilation of High-Resolution Satellite Data, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 758-774.  doi: 10.1007/s00376-010-0145-6
    [6] Lei LIU, Guihua WANG, Ze ZHANG, Huizan WANG, 2022: Effects of Drag Coefficients on Surface Heat Flux during Typhoon Kalmaegi (2014), ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1501-1518.  doi: 10.1007/s00376-022-1285-1
    [7] Shibo GAO, Haiqiu YU, Chuanyou REN, Limin LIU, Jinzhong MIN, 2021: Assimilation of Doppler Radar Data with an Ensemble 3DEnVar Approach to Improve Convective Forecasting, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 132-146.  doi: 10.1007/s00376-020-0081-z
    [8] ZHAO Ying, WANG Bin, 2008: Numerical Experiments for Typhoon Dan Incorporating AMSU-A Retrieved Data with 3DVM, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 692-703.  doi: 10.1007/s00376-008-0692-2
    [9] ZENG Zhihua, DUAN Yihong, LIANG Xudong, MA Leiming, Johnny Chung-leung CHAN, 2005: The Effect of Three-Dimensional Variational Data Assimilation of QuikSCAT Data on the Numerical Simulation of Typhoon Track and Intensity, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 534-544.  doi: 10.1007/BF02918486
    [10] ZHANG Xiaoyan, WANG Bin, JI Zhongzhen, Qingnong XIAO, ZHANG Xin, 2003: Initialization and Simulation of a Typhoon Using 4-Dimensional Variational Data Assimilation-Research on Typhoon Herb(1996), ADVANCES IN ATMOSPHERIC SCIENCES, 20, 612-622.  doi: 10.1007/BF02915504
    [11] Lin DENG, Wenhua GAO, Yihong DUAN, Yuqing WANG, 2019: Microphysical Properties of Rainwater in Typhoon Usagi (2013): A Numerical Modeling Study, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 510-526.  doi: 10.1007/s00376-019-8170-6
    [12] LIU Juanjuan, WANG Bin, 2011: Rainfall Assimilation Using a New Four-Dimensional Variational Method: A Single-Point Observation Experiment, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 735-742.  doi: 10.1007/s00376-010-0061-9
    [13] SHEN Feifei, MIN Jinzhong, 2015: Assimilating AMSU-A Radiance Data with the WRF Hybrid En3DVAR System for Track Predictions of Typhoon Megi (2010), ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1231-1243.  doi: 10.1007/s00376-014-4239-4
    [14] FU Weiwei, ZHOU Guangqing, WANG Huijun, 2004: Ocean Data Assimilation with Background Error Covariance Derived from OGCM Outputs, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 181-192.  doi: 10.1007/BF02915704
    [15] LIU Juanjuan, WANG Bin, WANG Shudong, 2010: The Structure of Background-error Covariance in a Four-dimensional Variational Data Assimilation System: Single-point Experiment, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 1303-1310.  doi: 10.1007/s00376-010-9067-6
    [16] 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
    [17] Bohua Huang, James L. Kinter III, Paul S. Schopf, 2002: Ocean Data Assimilation Using Intermittent Analyses and Continuous Model Error Correction, ADVANCES IN ATMOSPHERIC SCIENCES, 19, 965-992.  doi: 10.1007/s00376-002-0059-z
    [18] Xiaogu ZHENG, 2009: An Adaptive Estimation of Forecast Error Covariance Parameters for Kalman Filtering Data Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 154-160.  doi: 10.1007/s00376-009-0154-5
    [19] Guifu ZHANG, Jidong GAO, Muyun DU, 2021: Parameterized Forward Operators for Simulation and Assimilation of Polarimetric Radar Data with Numerical Weather Predictions, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 737-754.  doi: 10.1007/s00376-021-0289-6
    [20] Chaoqun MA, Tijian WANG, Zengliang ZANG, Zhijin LI, 2018: Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 813-825.  doi: 10.1007/s00376-017-7179-y
PDF

Get Citation+

shu
Export:  

Share Article

Article Metrics

Article Views: 1562 Times

PDF downloads: 1724 Times

Cited by: Times
  • 加载中

    • Manuscript History

    Manuscript received: 10 May 2005
    Manuscript revised: 10 May 2005
    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

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Assimilation and Simulation of Typhoon Rusa (2002) Using the WRF System

    • 1. Chinese Academy of Meteorological Sciences, Beijing 100081;National Center for Atmospheric Research, Boulder, Colorado 80307, USA;Shanghai Weather Forecast Center, Shanghai 200030,National Center for Atmospheric Research, Boulder, Colorado 80307, USA,National Center for Atmospheric Research, Boulder, Colorado 80307, USA,National Center for Atmospheric Research, Boulder, Colorado 80307, USA,Chinese Academy of Meteorological Sciences, Beijing 100081,Shanghai Weather Forecast Center, Shanghai 200030
    • Manuscript received: 2005-05-10
    • Manuscript revised: 2005-05-10

    Abstract: Using the recently developed Weather Research and Forecasting (WRF) 3DVAR and the WRF model, numerical experiments are conducted for the initialization and simulation of typhoon Rusa (2002).The observational data used in the WRF 3DVAR are conventional Global Telecommunications System (GTS) data and Korean Automatic Weather Station (AWS) surface observations. The Background Error Statistics (BES) via the National Meteorological Center (NMC) method has two different resolutions, that is, a 210-km horizontal grid space from the NCEP global model and a 10-kn horizontal resolution from Korean operational forecasts. To improve the performance of the WRF simulation initialized from the WRF 3DVAR analyses, the scale-lengths used in the horizontal background error covariances via recursive filter are tuned in terms of the WRF 3DVAR control variables, streamfunction, velocity potential, unbalanced pressure and specific humidity. The experiments with respect to different background error statistics and different observational data indicate that the subsequent 24-h the WRF model forecasts of typhoon Rusa's track and precipitation are significantly impacted upon the initial fields. Assimilation of the AWS data with the tuned background error statistics obtains improved predictions of the typhoon track and its precipitation.

      HTML

    Catalog

      Publish with AAS

      Contact us

      Links

      Copyright © 2018-2028 ADVANCES IN ATMOSPHERIC SCIENCES 京ICP备14024088号-7

      Supported by: Beijing Renhe Information Technology Co. Ltd  support: info@rhhz.net  

      /

      DownLoad:  Full-Size Img  PowerPoint
      Return
      Return