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논문 기본 정보

자료유형
학술저널
저자정보
이두일 (공주대학교) 이상현 (공주대학교) 정형세 (국립기상과학원) 김연희 (국립기상과학원)
저널정보
한국기상학회 대기 대기 Vol.31 No.5
발행연도
2021.12
수록면
473 - 488 (16page)

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초록· 키워드

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A new physical/statistical diagnostic downscale model has been developed for use to improve near-surface air temperature forecasts. The model includes a series of physical and statistical correction methods that account for un-resolved topographic and land-use effects as well as statistical bias errors in a low-resolution atmospheric model. Operational temperature forecasts of the Local Data Assimilation and Prediction System (LDAPS) were downscaled at 100 m resolution for three months, which were used to validate the model’s physical and statistical correction methods and to compare its performance with the forecasts of the Korea Meteorological Administration Post-processing (KMAP) system. The validation results showed positive impacts of the un-resolved topographic and urban effects (topographic height correction, valley cold air pool effect, mountain internal boundary layer formation effect, urban land-use effect) in complex terrain areas. In addition, the statistical bias correction of the LDAPS model were efficient in reducing forecast errors of the near-surface temperatures. The new high-resolution downscale model showed better agreement against Korean 584 meteorological monitoring stations than the KMAP, supporting the importance of the new physical and statistical correction methods. The new physical/statistical diagnostic downscale model can be a useful tool in improving near-surface temperature forecasts and diagnostics over complex terrain areas.

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Abstract
1. 서론
2. 지상 기온 상세화 모형
3. 모형 검증
4. 요약 및 결론
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