고해상도 수치예측자료 생산을 위한 경도-역거리 제곱법(GIDS) 기반의 공간 규모 상세화 기법 활용

Implementation of Spatial Downscaling Method Based on Gradient and Inverse Distance Squared (GIDS) for High-Resolution Numerical Weather Prediction Data

  • 양아련 (봄인 사이언스 컨설팅) ;
  • 오수빈 (봄인 사이언스 컨설팅) ;
  • 김주완 (공주대학교 대기과학과) ;
  • 이승우 (기상청 수치모델링센터 수치자료응용과) ;
  • 김춘지 (봄인 사이언스 컨설팅) ;
  • 박수현 (봄인 사이언스 컨설팅)
  • Yang, Ah-Ryeon (BomIn Science Consulting) ;
  • Oh, Su-Bin (BomIn Science Consulting) ;
  • Kim, Joowan (Department of Atmospheric Science, Kongju National University) ;
  • Lee, Seung-Woo (Numerical Data Application Division, Numerical Modeling Center, Korea Meteorological Administration) ;
  • Kim, Chun-Ji (BomIn Science Consulting) ;
  • Park, Soohyun (BomIn Science Consulting)
  • 투고 : 2021.02.01
  • 심사 : 2021.04.04
  • 발행 : 2021.06.30


In this study, we examined a spatial downscaling method based on Gradient and Inverse Distance Squared (GIDS) weighting to produce high-resolution grid data from a numerical weather prediction model over Korean Peninsula with complex terrain. The GIDS is a simple and effective geostatistical downscaling method using horizontal distance gradients and an elevation. The predicted meteorological variables (e.g., temperature and 3-hr accumulated rainfall amount) from the Limited-area ENsemble prediction System (LENS; horizontal grid spacing of 3 km) are used for the GIDS to produce a higher horizontal resolution (1.5 km) data set. The obtained results were compared to those from the bilinear interpolation. The GIDS effectively produced high-resolution gridded data for temperature with the continuous spatial distribution and high dependence on topography. The results showed a better agreement with the observation by increasing a searching radius from 10 to 30 km. However, the GIDS showed relatively lower performance for the precipitation variable. Although the GIDS has a significant efficiency in producing a higher resolution gridded temperature data, it requires further study to be applied for rainfall events.



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