Atmosphere (대기)

Korean Meteorological Society (한국기상학회)
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The Effect of Radar Data Assimilation in Numerical Models on Precipitation Forecasting

수치모델에서 레이더 자료동화가 강수 예측에 미치는 영향

  • Ji-Won Lee (Dept. of Atmospheric Sciences, Kyungpook National University) ;
  • Ki-Hong Min (Dept. of Atmospheric Sciences, Kyungpook National University)
  • 이지원 (경북대학교 대학원 대기과학과) ;
  • 민기홍 (경북대학교 대학원 대기과학과)
  • Received : 2023.07.16
  • Accepted : 2023.10.05
  • Published : 2023.11.30
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Abstract

Accurately predicting localized heavy rainfall is challenging without high-resolution mesoscale cloud information in the numerical model's initial field, as precipitation intensity and amount vary significantly across regions. In the Korean Peninsula, the radar observation network covers the entire country, providing high-resolution data on hydrometeors which is suitable for data assimilation (DA). During the pre-processing stage, radar reflectivity is classified into hydrometeors (e.g., rain, snow, graupel) using the background temperature field. The mixing ratio of each hydrometeor is converted and inputted into a numerical model. Moreover, assimilating saturated water vapor mixing ratio and decomposing radar radial velocity into a three-dimensional wind vector improves the atmospheric dynamic field. This study presents radar DA experiments using a numerical prediction model to enhance the wind, water vapor, and hydrometeor mixing ratio information. The impact of radar DA on precipitation prediction is analyzed separately for each radar component. Assimilating radial velocity improves the dynamic field, while assimilating hydrometeor mixing ratio reduces the spin-up period in cloud microphysical processes, simulating initial precipitation growth. Assimilating water vapor mixing ratio further captures a moist atmospheric environment, maintaining continuous growth of hydrometeors, resulting in concentrated heavy rainfall. Overall, the radar DA experiment showed a 32.78% improvement in precipitation forecast accuracy compared to experiments without DA across four cases. Further research in related fields is necessary to improve predictions of mesoscale heavy rainfall in South Korea, mitigating its impact on human life and property.

Keywords

Acknowledgement

이 논문은 2020학년도 경북대학교 국립대학육성사업 지원비에 의하여 연구되었습니다.

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