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Development of GK2A Convective Initiation Algorithm for Localized Torrential Rainfall Monitoring

국지성 집중호우 감시를 위한 천리안위성 2A호 대류운 전조 탐지 알고리즘 개발

  • Park, Hye-In (Satellite Planning Division, National Meteorological Satellite Center, Korea Meteorological Administration) ;
  • Chung, Sung-Rae (Satellite Operation Division, National Meteorological Satellite Center, Korea Meteorological Administration) ;
  • Park, Ki-Hong (Satellite Planning Division, National Meteorological Satellite Center, Korea Meteorological Administration) ;
  • Moon, Jae-In (Chuncheon Weather Station, Gangwon Regional Office of Meteorology, Korea Meteorological Administration)
  • 박혜인 (기상청 국가기상위성센터 위성기획과) ;
  • 정성래 (기상청 국가기상위성센터 위성운영과) ;
  • 박기홍 (기상청 국가기상위성센터 위성기획과) ;
  • 문재인 (기상청 강원지방기상청 춘천기상대)
  • Received : 2021.07.09
  • Accepted : 2021.10.25
  • Published : 2021.12.31

Abstract

In this paper, we propose an algorithm for detecting convective initiation (CI) using GEO-KOMPSAT-2A/advanced meteorological imager data. The algorithm identifies clouds that are likely to grow into convective clouds with radar reflectivity greater than 35 dBZ within the next two hours. This algorithm is developed using statistical and qualitative analysis of cloud characteristics, such as atmospheric instability, cloud top height, and phase, for convective clouds that occurred on the Korean Peninsula from June to September 2019. The CI algorithm consists of four steps: 1) convective cloud mask, 2) cloud object clustering and tracking, 3) interest field tests, and 4) post-processing tests to remove non-convective objects. Validation, performed using 14 CI events that occurred in the summer of 2020 in Korean Peninsula, shows a total probability of detection of 0.89, false-alarm ratio of 0.46, and mean lead-time of 39 minutes. This algorithm can be useful warnings of rapidly developing convective clouds in future by providing information about CI that is otherwise difficult to predict from radar or a numerical prediction model. This CI information will be provided in short-term forecasts to help predict severe weather events such as localized torrential rainfall and hail.

Keywords

Acknowledgement

연구는 기상청 R&D 프로그램 「기상위성 예보지원 및 융합서비스 기술개발」(KMA2020-00120)의 지원으로 수행되었습니다.

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