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Observing System Experiment Based on the Korean Integrated Model for Upper Air Sounding Data in the Seoul Capital Area during 2020 Intensive Observation Period

2020년 수도권 라디오존데 집중관측 자료의 한국형모델 기반 관측 영향 평가

  • Hwang, Yoonjeong (Numerical Modeling Center, Korea Meteorological Administration) ;
  • Ha, Ji-Hyun (Numerical Modeling Center, Korea Meteorological Administration) ;
  • Kim, Changhwan (Numerical Modeling Center, Korea Meteorological Administration) ;
  • Choi, Dayoung (Korea Institute of Atmospheric Prediction Systems) ;
  • Lee, Yong Hee (High Impact Weather Research Department, National Institute of Meteorological Sciences)
  • 황윤정 (기상청 수치모델링센터 수치자료응용과) ;
  • 하지현 (기상청 수치모델링센터 수치자료응용과) ;
  • 김창환 (기상청 수치모델링센터 수치자료응용과) ;
  • 최다영 (차세대수치에보모델개발사업단) ;
  • 이용희 (국립기상과학원 재해기상연구부)
  • Received : 2021.07.16
  • Accepted : 2021.09.14
  • Published : 2021.09.30

Abstract

To improve the predictability of high-impact weather phenomena around Seoul, where a larger number of people are densely populated, KMA conducted the intensive observation from 22 June to 20 September in 2020 over the Seoul area. During the intensive observation period (IOP), the dropsonde from NIMS Atmospheric Research Aircraft (NARA) and the radiosonde from KMA research vessel Gisang1 were observed in the Yellow Sea, while, in the land, the radiosonde observation data were collected from Icheon and Incheon. Therefore, in this study, the effects of radiosonde and dropsonde data during the IOP were investigated by Observing System Experiment (OSE) based on Korean Integrated Model (KIM). We conducted two experiments: CTL assimilated the operational fifteen kinds of observations, and EXP assimilated not only operational observation data but also intensive observation data. Verifications over the Korean Peninsula area of two experiments were performed against analysis and observation data. The results showed that the predictability of short-range forecast (1~2 day) was improved for geopotential height at middle level and temperature at lower level. In three precipitation cases, EXP improved the distribution of precipitation against CTL. In typhoon cases, the predictability of EXP for typhoon track was better than CTL, although both experiments simulated weaker intensity as compared with the observed data.

Keywords

References

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