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Volume 28 Issue 3

May  2011

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Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2011 >  28(3): 573-590

Observation and Numerical Simulations with Radar and Surface Data Assimilation for Heavy Rainfall over Central Korea

  • 1.

    Atmospheric Sciences Program, School of Earth and Environmental Sciences, Seoul National University, Seoul 151--747, Korea,Atmospheric Sciences Program, School of Earth and Environmental Sciences, Seoul National University, Seoul 151--747, Korea and Atmospheric Sciences Program, School of Earth and Environmental Sciences, Seoul National University, Seoul 151--747, Korea


doi: 10.1007/s00376-010-0035-y

  • This study investigated the impact of multiple-Doppler radar data and surface data assimilation on forecasts of heavy rainfall over the central Korean Peninsula; the Weather Research and Forecasting (WRF) model and its three-dimensional variational data assimilation system (3DVAR) were used for this purpose. During data assimilation, the WRF 3DVAR cycling mode with incremental analysis updates (IAU) was used. A maximum rainfall of 335.0 mm occurred during a 12-h period from 2100 UTC 11 July 2006 to 0900 UTC 12 July 2006. Doppler radar data showed that the heavy rainfall was due to the back-building formation of mesoscale convective systems (MCSs). New convective cells were continuously formed in the upstream region, which was characterized by a strong southwesterly low-level jet (LLJ). The LLJ also facilitated strong convergence due to horizontal wind shear, which resulted in maintenance of the storms. The assimilation of both multiple-Doppler radar and surface data improved the accuracy of precipitation forecasts and had a more positive impact on quantitative forecasting (QPF) than the assimilation of either radar data or surface data only. The back-building characteristic was successfully forecasted when the multiple-Doppler radar data and surface data were assimilated. In data assimilation experiments, the radar data helped forecast the development of convective storms responsible for heavy rainfall, and the surface data contributed to the occurrence of intensified low-level winds. The surface data played a significant role in enhancing the thermal gradient and modulating the planetary boundary layer of the model, which resulted in favorable conditions for convection.
  • [1] HOU Tuanjie, Fanyou KONG, CHEN Xunlai, LEI Hengchi, HU Zhaoxia, 2015: Evaluation of Radar and Automatic Weather Station Data Assimilation for a Heavy Rainfall Event in Southern China, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 967-978.  doi: 10.1007/s00376-014-4155-7
    [2] 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
    [3] 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
    [4] Jo-Han LEE, Dong-Kyou LEE, Hyun-Ha LEE, Yonghan CHOI, Hyung-Woo KIM, 2010: Radar Data Assimilation for the Simulation of Mesoscale Convective Systems, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 1025-1042.  doi: 10.1007/s00376-010-9162-8
    [5] XU Zhifang, GE Wenzhong, DANG Renqing, Toshio IGUCHI, Takao TAKADA, 2003: Application of TRMM/PR Data for Numerical Simulations with Mesoscale Model MM5, ADVANCES IN ATMOSPHERIC SCIENCES, 20, 185-193.  doi: 10.1007/s00376-003-0003-x
    [6] Cheng Minghu, He Huizhong, Mao Dongyan, Qi Yanjun, Cui Zhehu, Zhou Fengxian, 2001: Study of 1998 Heavy Rainfall over the Yangtze River Basin Using TRMM Data, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 387-396.  doi: 10.1007/BF02919317
    [7] Liping LUO, Ming XUE, Xin XU, Lijuan LI, Qiang ZHANG, Ziqi FAN, 2024: Understanding Simulated Causes of Damaging Surface Winds in a Derecho-Producing Mesoscale Convective System near the East China Coast based on Convection-Permitting Simulations, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-024-3314-8
    [8] SUN Jianhua, ZHAO Sixiong, XU Guangkuo, MENG Qingtao, 2010: Study on a Mesoscale Convective Vortex Causing Heavy Rainfall during the Mei-yu Season in 2003, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 1193-1209.  doi: 10.1007/s00376-009-9156-6
    [9] 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
    [10] Hongqin ZHANG, Xiangjun TIAN, Wei CHENG, Lipeng JIANG, 2020: System of Multigrid Nonlinear Least-squares Four-dimensional Variational Data Assimilation for Numerical Weather Prediction (SNAP): System Formulation and Preliminary Evaluation, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 1267-1284.  doi: 10.1007/s00376-020-9252-1
    [11] Fabien CARMINATI, Stefano MIGLIORINI, 2021: All-sky Data Assimilation of MWTS-2 and MWHS-2 in the Met Office Global NWP System., ADVANCES IN ATMOSPHERIC SCIENCES, 38, 1682-1694.  doi: 10.1007/s00376-021-1071-5
    [12] Banglin ZHANG, Vijay TALLAPRAGADA, Fuzhong WENG, Jason SIPPEL, Zaizhong MA, 2016: Estimation and Correction of Model Bias in the NASA/GMAO GEOS5 Data Assimilation System: Sequential Implementation, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 659-672.  doi: 10.1007/ s00376-015-5155-y
    [13] Fabien CARMINATI, Nigel ATKINSON, Brett CANDY, Qifeng LU, 2021: Insights into the Microwave Instruments Onboard the Fengyun 3D Satellite: Data Quality and Assimilation in the Met Office NWP System, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 1379-1396.  doi: 10.1007/s00376-020-0010-1
    [14] SUN Jianhua, ZHANG Xiaoling, QI Linlin, ZHAO Sixiong, 2005: An Analysis of a Meso-β System in a Mei-yu Front Using the Intensive Observation Data During CHeRES 2002, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 278-289.  doi: 10.1007/BF02918517
    [15] Magnus LINDSKOG, Adam DYBBROE, Roger RANDRIAMAMPIANINA, 2021: Use of Microwave Radiances from Metop-C and Fengyun-3 C/D Satellites for a Northern European Limited-area Data Assimilation System, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 1415-1428.  doi: 10.1007/s00376-021-0326-5
    [16] ZHAO Sixiong, BEI Naifang, SUN Jianhua, 2007: Mesoscale Analysis of a Heavy Rainfall Event over Hong Kong During a Pre-rainy Season in South China, ADVANCES IN ATMOSPHERIC SCIENCES, 24, 555-572.  doi: 10.1007/s00376-007-0555-2
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    [18] Zhiyong MENG, Eugene E. CLOTHIAUX, 2022: Contributions of Fuqing ZHANG to Predictability, Data Assimilation, and Dynamics of High Impact Weather: A Tribute, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 676-683.  doi: 10.1007/s00376-021-1362-x
    [19] Jidong GAO, Keith BREWSTER, Ming XUE, 2006: A Comparison of the Radar Ray Path Equations and Approximations for Use in Radar Data Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 190-198.  doi: 10.1007/s00376-006-0190-3
    [20] Yong LI, Siming LI, Yao SHENG, Luheng WANG, 2018: Data Assimilation Method Based on the Constraints of Confidence Region, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 334-345.  doi: 10.1007/s00376-017-7045-y
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    • Manuscript History

    Manuscript received: 10 May 2011
    Manuscript revised: 10 May 2011
    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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    Observation and Numerical Simulations with Radar and Surface Data Assimilation for Heavy Rainfall over Central Korea

    • 1. Atmospheric Sciences Program, School of Earth and Environmental Sciences, Seoul National University, Seoul 151--747, Korea,Atmospheric Sciences Program, School of Earth and Environmental Sciences, Seoul National University, Seoul 151--747, Korea and Atmospheric Sciences Program, School of Earth and Environmental Sciences, Seoul National University, Seoul 151--747, Korea
    • Manuscript received: 2011-05-10
    • Manuscript revised: 2011-05-10

    Abstract: This study investigated the impact of multiple-Doppler radar data and surface data assimilation on forecasts of heavy rainfall over the central Korean Peninsula; the Weather Research and Forecasting (WRF) model and its three-dimensional variational data assimilation system (3DVAR) were used for this purpose. During data assimilation, the WRF 3DVAR cycling mode with incremental analysis updates (IAU) was used. A maximum rainfall of 335.0 mm occurred during a 12-h period from 2100 UTC 11 July 2006 to 0900 UTC 12 July 2006. Doppler radar data showed that the heavy rainfall was due to the back-building formation of mesoscale convective systems (MCSs). New convective cells were continuously formed in the upstream region, which was characterized by a strong southwesterly low-level jet (LLJ). The LLJ also facilitated strong convergence due to horizontal wind shear, which resulted in maintenance of the storms. The assimilation of both multiple-Doppler radar and surface data improved the accuracy of precipitation forecasts and had a more positive impact on quantitative forecasting (QPF) than the assimilation of either radar data or surface data only. The back-building characteristic was successfully forecasted when the multiple-Doppler radar data and surface data were assimilated. In data assimilation experiments, the radar data helped forecast the development of convective storms responsible for heavy rainfall, and the surface data contributed to the occurrence of intensified low-level winds. The surface data played a significant role in enhancing the thermal gradient and modulating the planetary boundary layer of the model, which resulted in favorable conditions for convection.

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