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IoT 기반 기온, 상대습도, 강수 탐지 관측 품질평가 - 2020년 여름 서울을 중심으로 - KCI 등재

Quality Check of IoT-based Surface Air Temperature, Relative Humidity, and Precipitation Detection Observations: Focusing on Seoul in 2020 Summer

  • 언어KOR
  • URLhttps://db.koreascholar.com/Article/Detail/411456
  • DOIhttps://doi.org/10.14383/cri.2021.16.3.247
서비스가 종료되어 열람이 제한될 수 있습니다.
기후연구 (Journal of Climate Research)
건국대학교 기후연구소 (KU Climate Research Institute)
초록

This study evaluates the quality of surface air temperature, relative humidity, and precipitation detection observed by 22 internet of thing (IoT)-based mini-weather stations in Seoul in 2020 summer. The automatic weather station (AWS) closest to each IoT-based station is used as reference. The IoT-based observations show surface air temperature and relative humidity are about 0.2-4.0°C higher and about -1--22% lower than the AWS observations, respectively. However, they exhibit temporal variability similar to the AWS observations on both diurnal and daily time scales, with daily correlations greater than 0.90 for temperature and 0.82 for relative humidity. Given these strong linear relationships, it show that temperature and relative humidity biases can be effectively corrected by applying a simple bias correction method. For IoT-based precipitation detection, we found that precipitation conductivity value (PCV) during precipitation events is well separated from that during non-precipitation events, providing a basis for distinguishing precipitation events from non-precipitation events. When the PCV threshold is set to 250 for precipitation detection, the highest critical success index and the bias score index close to one, suitable for operational precipitation detection, are obtained. These results demonstrate that IoT-based mini-weather stations can successfully measure surface air temperature, relative humidity, and precipitation detection with appropriate bias corrections.

목차
Abstract
1. 서론
2. 자료 및 연구방법
    1) 자료 및 분석 기간
    2) 시공간 일치
    3) 통계분석
3. 연구 결과
    1) 기온과 상대습도에 대한 품질검사
    2) 강수 전기 전도도를 이용한 강수유무 정보산출 가능성 평가
4. 요약 및 결론
Reference
저자
  • 오석근(서울대학교 기초과학연구원) | Seok-Geun Oh (Research Institute of Basic Sciences, Seoul National University) Correspondence
  • 손석우(서울대학교 지구환경과학부) | Seok-Woo Son (School of Earth and Enviornmental Sciences, Seoul National University)
  • 김선영(국립기상과학원 인공지능기상연구팀) | Sunyoung Kim (AI Meteorological Research Team, National Institute of Meteorological Sciences)
  • 박준상(국립기상과학원 인공지능기상연구팀) | Junsang Park (AI Meteorological Research Team, National Institute of Meteorological Sciences)
  • 이종원((주)옵저버) | Jong-Won Lee (Observer Foundation)