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Korean Meteorological Society (한국기상학회)
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Development and Evaluation of Urban Canopy Model Based on Unified Model Input Data Using Urban Building Information Data in Seoul

서울 건물정보 자료를 활용한 UM 기반의 도시캐노피 모델 입력자료 구축 및 평가

  • Kim, Do-Hyoung (Applied Meteorology Research Division, National Institute of Meteorological Sciences) ;
  • Hong, Seon-Ok (Applied Meteorology Research Division, National Institute of Meteorological Sciences) ;
  • Byon, Jae-Yong (Applied Meteorology Research Division, National Institute of Meteorological Sciences) ;
  • Park, HyangSuk (Applied Meteorology Research Division, National Institute of Meteorological Sciences) ;
  • Ha, Jong-Chul (Applied Meteorology Research Division, National Institute of Meteorological Sciences)
  • 김도형 (국립기상과학원 용용기상연구과) ;
  • 홍선옥 (국립기상과학원 용용기상연구과) ;
  • 변재영 (국립기상과학원 용용기상연구과) ;
  • 박향숙 (국립기상과학원 용용기상연구과) ;
  • 하종철 (국립기상과학원 용용기상연구과)
  • Received : 2019.07.12
  • Accepted : 2019.09.24
  • Published : 2019.11.30
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Abstract

The purpose of this study is to build urban canopy model (Met Office Reading Urban Surface Exchange Scheme, MORUSES) based to Unified Model (UM) by using urban building information data in Seoul, and then to compare the improving urban canopy model simulation result with that of Seoul Automatic Weather Station (AWS) observation site data. UM-MORUSES is based on building information database in London, we performed a sensitivity experiment of UM-MOURSES model using urban building information database in Seoul. Geographic Information System (GIS) analysis of 1.5 km resolution Seoul building data is applied instead of London building information data. Frontal-area index and planar-area index of Seoul are used to calculate building height. The height of the highest building in Seoul is 40m, showing high in Yeoido-gu, Gangnam-gu and Jamsil-gu areas. The street aspect ratio is high in Gangnam-gu, and the repetition rate of buildings is lower in Eunpyeong-gu and Gangbuk-gu. UM-MORUSES model is improved to consider the building geometry parameter in Seoul. It is noticed that the Root Mean Square Error (RMSE) of wind speed is decreases from 0.8 to 0.6 m s-1 by 25 number AWS in Seoul. The surface air temperature forecast tends to underestimate in pre-improvement model, while it is improved at night time by UM-MORUSES model. This study shows that the post-improvement UM-MORUSES model can provide detailed Seoul building information data and accurate surface air temperature and wind speed in urban region.

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References

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