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Volume 27 Issue 5

Sep.  2010

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Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2010 >  27(5): 1025-1042

Radar Data Assimilation for the Simulation of Mesoscale Convective Systems

  • 1.

    School of Earth and Environmental Sciences, Seoul National University, Seoul, Korea,School of Earth and Environmental Sciences, Seoul National University, Seoul, Korea,School of Earth and Environmental Sciences, Seoul National University, Seoul, Korea,School of Earth and Environmental Sciences, Seoul National University, Seoul, Korea,School of Earth and Environmental Sciences, Seoul National University, Seoul, Korea


doi: 10.1007/s00376-010-9162-8

  • A heavy rainfall case related to Mesoscale Convective Systems (MCSs) over the Korean Peninsula was selected to investigate the impact of radar data assimilation on a heavy rainfall forecast. The Weather Research and Forecasting (WRF) three-dimensional variational (3DVAR) data assimilation system with tuning of the length scale of the background error covariance and observation error parameters was used to assimilate radar radial velocity and reflectivity data. The radar data used in the assimilation experiments were preprocessed using quality-control procedures and interpolated/thinned into Cartesian coordinates by the SPRINT/CEDRIC packages. Sensitivity experiments were carried out in order to determine the optimal values of the assimilation window length and the update frequency used for the rapid update cycle and incremental analysis update experiments. The assimilation of radar data has a positive influence on the heavy rainfall forecast. Quantitative features of the heavy rainfall case, such as the maximum rainfall amount and Root Mean Squared Differences (RMSDs) of zonal/meridional wind components, were improved by tuning of the length scale and observation error parameters. Qualitative features of the case, such as the maximum rainfall position and time series of hourly rainfall, were enhanced by an incremental analysis update technique. The positive effects of the radar data assimilation and the tuning of the length scale and observation error parameters were clearly shown by the 3DVAR increment.
  • [1] 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
    [2] 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
    [3] LI Shan, RONG Xingyao, LIU Yun, LIU Zhengyu, Klaus FRAEDRICH, 2013: Dynamic Analogue Initialization for Ensemble Forecasting, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1406-1420.  doi: 10.1007/s00376-012-2244-z
    [4] JIANG Yuan, LIU Liping, 2014: A Test Pattern Identification Algorithm and Its Application to CINRAD/SA(B) Data, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 331-343.  doi: 10.1007/s00376-013-2315-9
    [5] Ji-Hyun HA, Hyung-Woo KIM, Dong-Kyou LEE, 2011: Observation and Numerical Simulations with Radar and Surface Data Assimilation for Heavy Rainfall over Central Korea, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 573-590.  doi: 10.1007/s00376-010-0035-y
    [6] Ui-Yong BYUN, Jinkyu HONG, Song-You HONG, Hyeyum Hailey SHIN, 2015: Numerical Simulations of Heavy Rainfall over Central Korea on 21 September 2010 Using the WRF Model, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 855-869.  doi: 10.1007/s00376-014-4075-6
    [7] Jiwon Hwang, Dong-Hyun Cha, Donghyuck Yoon, Tae-Young Goo, Sueng-Pil Jung, 2024: Effects of Initial and Boundary Conditions on Heavy Rainfall Simulation over the Yellow Sea and the Korean Peninsula: Comparison of ECMWF and NCEP Analysis Data Effects and Verification with Dropsonde Observation, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-024-3232-9
    [8] 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
    [9] ZHOU Lingli, DU Huiliang, ZHAI Guoqing, WANG Donghai, 2013: Numerical Simulation of the Sudden Rainstorm Associated with the Remnants of Typhoon Meranti (2010), ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1353-1372.  doi: 10.1007/s00376-012-2127-3
    [10] Xingchao CHEN, Kun ZHAO, Juanzhen SUN, Bowen ZHOU, Wen-Chau LEE, 2016: Assimilating Surface Observations in a Four-Dimensional Variational Doppler Radar Data Assimilation System to Improve the Analysis and Forecast of a Squall Line Case, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 1106-1119.  doi: 10.1007/s00376-016-5290-0
    [11] 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
    [12] 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
    [13] Iman ROUSTA, Mehdi DOOSTKAMIAN, Esmaeil HAGHIGHI, Hamid Reza GHAFARIAN MALAMIRI, Parvane YARAHMADI, 2017: Analysis of Spatial Autocorrelation Patterns of Heavy and Super-Heavy Rainfall in Iran, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 1069-1081.  doi: 10.1007/s00376-017-6227-y
    [14] 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
    [15] 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
    [16] Xiuzhen LI, Wen ZHOU, Yongqin David CHEN, 2016: Detecting the Origins of Moisture over Southeast China: Seasonal Variation and Heavy Rainfall, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 319-329.  doi: 10.1007/s00376-015-4197-5
    [17] Chang-Kyun PARK, Minhee CHANG, Chang-Hoi HO, Kyung-Ja HA, Jinwon KIM, Byung-Ju SOHN, 2021: Two Types of Diurnal Variations in Heavy Rainfall during July over Korea, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 2201-2211.  doi: 10.1007/s00376-021-1178-8
    [18] WU Liji, HUANG Ronghui, HE Haiyan, SHAO Yaping, WEN Zhiping, 2010: Synoptic Characteristics of Heavy Rainfall Events in Pre-monsoon Season in South China, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 315-327.  doi: 10.1007/s00376-009-8219-z
    [19] WANG Shuzhou, YU Entao, WANG Huijun, 2012: A Simulation Study of a Heavy Rainfall Process over the Yangtze River Valley Using the Two-Way Nesting Approach, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 731-743.  doi: 10.1007/s00376-012-1176-y
    [20] Huizhen YU, Zhiyong MENG, 2022: The Impact of Moist Physics on the Sensitive Area Identification for Heavy Rainfall Associated Weather Systems, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 684-696.  doi: 10.1007/s00376-021-0278-9
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    • Manuscript History

    Manuscript received: 10 September 2010
    Manuscript revised: 10 September 2010
    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 for the Simulation of Mesoscale Convective Systems

    • 1. School of Earth and Environmental Sciences, Seoul National University, Seoul, Korea,School of Earth and Environmental Sciences, Seoul National University, Seoul, Korea,School of Earth and Environmental Sciences, Seoul National University, Seoul, Korea,School of Earth and Environmental Sciences, Seoul National University, Seoul, Korea,School of Earth and Environmental Sciences, Seoul National University, Seoul, Korea
    • Manuscript received: 2010-09-10
    • Manuscript revised: 2010-09-10

    Abstract: A heavy rainfall case related to Mesoscale Convective Systems (MCSs) over the Korean Peninsula was selected to investigate the impact of radar data assimilation on a heavy rainfall forecast. The Weather Research and Forecasting (WRF) three-dimensional variational (3DVAR) data assimilation system with tuning of the length scale of the background error covariance and observation error parameters was used to assimilate radar radial velocity and reflectivity data. The radar data used in the assimilation experiments were preprocessed using quality-control procedures and interpolated/thinned into Cartesian coordinates by the SPRINT/CEDRIC packages. Sensitivity experiments were carried out in order to determine the optimal values of the assimilation window length and the update frequency used for the rapid update cycle and incremental analysis update experiments. The assimilation of radar data has a positive influence on the heavy rainfall forecast. Quantitative features of the heavy rainfall case, such as the maximum rainfall amount and Root Mean Squared Differences (RMSDs) of zonal/meridional wind components, were improved by tuning of the length scale and observation error parameters. Qualitative features of the case, such as the maximum rainfall position and time series of hourly rainfall, were enhanced by an incremental analysis update technique. The positive effects of the radar data assimilation and the tuning of the length scale and observation error parameters were clearly shown by the 3DVAR increment.

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