Korean Journal of Agricultural and Forest Meteorology (한국농림기상학회지)

Korean Society of Agricultural and Forest Meteorology (한국농림기상학회)
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Estimation and Evaluation of Reanalysis Air Temperature based on Mountain Meteorological Observation

산악기상정보 융합 기반 재분석 기온 데이터의 추정 및 검증

  • Sunghyun, Min (Forest ICT Research Center, National Institute of Forest Science) ;
  • Sukhee, Yoon (Korea Association of Forest Enviro-conservation Technology) ;
  • Myongsoo, Won (Forest ICT Research Center, National Institute of Forest Science) ;
  • Junghwa, Chun (Forest ICT Research Center, National Institute of Forest Science) ;
  • Keunchang, Jang (Forest ICT Research Center, National Institute of Forest Science)
  • 민성현 (국립산림과학원 산림ICT 연구센터) ;
  • 윤석희 (한국치산기술협회) ;
  • 원명수 (국립산림과학원 산림ICT 연구센터) ;
  • 천정화 (국립산림과학원 산림ICT 연구센터) ;
  • 장근창 (국립산림과학원 산림ICT 연구센터)
  • Received : 2021.12.01
  • Accepted : 2022.12.28
  • Published : 2022.12.30
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Abstract

This study estimated and evaluated the high resolution (1km) gridded mountain meteorology data of daily mean, maximum and minimum temperature based on ASOS (Automated Surface Observing System), AWS (Automatic Weather Stations) and AMOS (Automatic Mountain Meteorology Observation System) in South Korea. The ASOS, AWS, and AMOS meteorology data which were located above 200m was classified as mountainous area. And the ASOS, AWS, and AMOS meteorology data which were located under 200m was classified as non-mountainous area. The bias-correction method was used for correct air temperature over complex mountainous area and the performance of enhanced daily coefficients based on the AMOS and mountainous area observing meteorology data was evaluated using the observed daily mean, maximum and minimum temperature. As a result, the evaluation results show that RMSE (Root Mean Square Error) of air temperature using the enhanced coefficients based on the mountainous area observed meteorology data is smaller as 30% (mean), 50% (minimum), and 37% (maximum) than that of using non-mountainous area observed meteorology data. It indicates that the enhanced weather coefficients based on the AMOS and mountain ASOS can estimate mean, maximum, and minimum temperature data reasonably and the temperature results can provide useful input data on several climatological and forest disaster prediction studies.

본 연구는 국내의 ASOS 및 AWS와 AMOS 관측 값을 사용하여 1km 고해상도의 산악기상 격자 값을 추정하고 평가하였다. 해발고도 200m이상을 산악지역으로 정의하고 ASOS, AWS, AMOS 기상관측소를 산악기상이 반영된 기상데이터와 산악기상이 반영되지 않는 기상데이터로 나누었다. 2013년에서 2020년까지 산악기상 데이터를 적용하고 편의보정기법(bias correction method)방법을 통하여 산악기상 적용에 따른 보정계수를 산출하고 적용하여 보정계수 및 산악기상 데이터가 반영된 고해상도 산악기상기온 격자 데이터를 생성하였다. 추정된 산악기상기온 격자데이터는 검증지점의 기상 기온 실측 값과 비교하여 평가하였다. 산악기상 데이터 반영 및 보정계수가 반영된 산악기상 고해상도 격자 기온은 산악기상이 반영되지 않는 격자기온보다 RMSE가 34%(평균기온), 50%(최저기온), 31%(최고기온)가 감소하였다. 이는 산악기상 정보기반과 산악기상 보정계수를 적용이 국내 산악기상고해상도 격자 생성에 있어서 정확도를 크게 개선시킬 수 있음을 시사하였다. 이러한 1km 고해상도의 기온 격자데이터는 추후 기후변화에 대한 산림생태계 변화 및 산림재해 모델의 검증을 위한 데이터로 매우 유용하게 활용될 수 있을 것이라 사료된다.

Keywords

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