논문 상세보기
pp.1141-1154
Numerical experiments were carried out to investigate the effect of data assimilation of observational data on weather and PM (particulate matter) prediction. Observational data applied to numerical experiment are aircraft observation, satellite observation, upper level observation, and AWS (automatic weather system) data. In the case of grid nudging, the prediction performance of the meteorological field is largely improved compared with the case without data assimilations because the overall pressure distribution can be changed. So grid nudging effect can be significant when synoptic weather pattern strongly affects Korean Peninsula. Predictability of meteorological factors can be expected to improve through a number of observational data assimilation, but data assimilation by single data often occurred to be less predictive than without data assimilation. Variation of air pressure due to observation nudging with high prediction efficiency can improve prediction accuracy of whole model domain. However, in areas with complex terrain such as the eastern part of the Korean peninsula, the improvement due to grid nudging were only limited. In such cases, it would be more effective to aggregate assimilated data.
1. 서 론
2. 재료 및 방법
2.1. 연구 대상 지역
2.2. 관측자료
2.3. 자료동화 방법
2.4. 수치모델과 실험 설계
3. 결과 및 고찰
3.1. 종관 분석
3.2. 관측 자료동화별 예측 향상도 분석
3.3. 자료동화에 따른 기상 예측 향상도 통계분석
3.4. 강원 및 영남지역 기상장 향상성 분석
4. 결 론
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