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

양아련; 오수빈; 김주완; 이승우; 김춘지; 박수현

doi:10.14191/atmos.2021.31.2.185

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 원문보기

대기 = Atmosphere, v.31 no.2, 2021년, pp.185 - 198

양아련 (봄인 사이언스 컨설팅) , 오수빈 (봄인 사이언스 컨설팅) , 김주완 (공주대학교 대기과학과) , 이승우 (기상청 수치모델링센터 수치자료응용과) , 김춘지 (봄인 사이언스 컨설팅) , 박수현 (봄인 사이언스 컨설팅)

Abstract ▼ AI-Helper

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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표/그림 (12)

표 Table 1. Description of the cases used in this study.
그림 Fig. 1. 3-Dimensional schematic diagram of the spatial downscailing method.
그림 Fig. 2. Distribution maps of surface temperature (unit: K) predicted by LENS of (a) before spatial downscaling (SD) method and (b) after SD method on 0000 UTC 12 July 2015 (forecasting time: 48 hours) and (c) topography above sea level (m) of the Korean peninsula. (d), (e), and (f) are same as (a), (b), and (c) but for selected area (35.5~36.0ºN, 127.5~128.0ºE) indicated by the rectangles with black line in (a), (b), and (c).
그림 Fig. 4. Distribution maps of 1.5 m temperature (unit: ºC) of (a) AWS observation (OBS) denoted by circles with topography above sea level (unit: m, gray shading) and (b) BI and (c) SD30 for h09 of case 1.
그림 Fig. 3. (a) MSE and (b) r performance of AWS observation verse BI, SD10, SD20, and SD30 with different forecasting times for case 1.
그림 Fig. 5. Distribution maps of 1.5 m temperature (unit: ºC) of (a) BI and (b) SD30 with AWS observations denoted by circles, and (c) topography above sea level (unit: m) of the selected area (37.0~38.0ºN, 128.0~129.0ºE) for h09 of case 1.
그림 Fig. 6. Distribution maps of 1.5 m temperature (unit: ºC) differences between (a) AWS observaion (OBS) and BI, (b) OBS and SD30, and (c) BI and SD30 for h09 in case 1. The shading denotes topography above sea level (unit: m).
그림 Fig. 7. Same as Fig. 4 but for case 2.
그림 Fig. 8. Same as Fig. 4 but for h66 of case 2.
그림 Fig. 9. Same as Fig. 3 but (a) and (b) for case 3 and (c) and (d) for case 4.
그림 Fig. 10. Distribution maps of 3 hours accumulated rainfall [unit: mm (3 hr)] of (a) AWS observations (OBS), (b) BI, (c) SD10, (d) SD20, and (e) SD30 for h34 in case 3.
그림 Fig. 11. Same as Fig. 10 but for h27 in case 4.

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동의어: Packet Collision Rate

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