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Korean Meteorological Society (한국기상학회)
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Evaluation of JULES Land Surface Model Based on In-Situ Data of NIMS Flux Sites

국립기상과학원 플럭스 관측 자료 기반의 JULES 지면 모델 모의 성능 분석

  • Kim, Hyeri (National Institute of Meteorological Sciences) ;
  • Hong, Je-Woo (Ecosystem-Atmosphere Process Lab, Department of Atmospheric Sciences, Yonsei University) ;
  • Lim, Yoon-Jin (National Institute of Meteorological Sciences) ;
  • Hong, Jinkyu (Ecosystem-Atmosphere Process Lab, Department of Atmospheric Sciences, Yonsei University) ;
  • Shin, Seung-Sook (National Institute of Meteorological Sciences) ;
  • Kim, Yun-Jae (National Institute of Meteorological Sciences)
  • Received : 2019.06.19
  • Accepted : 2019.08.26
  • Published : 2019.11.30
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Abstract

Based on in-situ monitoring data produced by National Institute of Meteorological Sciences, we evaluated the performance of Joint UK Land Environment Simulator (JULES) on the surface energy balance for rice-paddy and cropland in Korea with the operational ancillary data used for Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS) (CTL) and the high-resolution ancillary data from external sources (EXP). For these experiments, we employed the one-year (March 2015~February 2016) observations of eddy-covariance fluxes and soil moisture contents from a double-cropping rice-paddy in BoSeong and a cropland in AnDong. On the rice-paddy site the model performed better in the CTL experiment except for the sensible heat flux, and the latent heat flux was underestimated in both of experiments which can be inferred that the model represents flood-irrigated surface poorly. On the cropland site the model performance of the EXP experiment was worse than that of CTL experiment related to unrealistic surface type fractions. The pattern of the modeled soil moisture was similar to the observation but more variable in time. Our results shed a light on that 1) the improvement of land scheme for the flood-irrigated rice-paddy and 2) the construction of appropriate high-resolution ancillary data should be considered in the future research.

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