http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Mohamad Javad Alizadeh (검색결과 1 건)
- 무료
- 기관 내 무료
- 유료
-
A Weibull Distribution Based Technique for Downscaling of Climatic Wind Field
Mohamad Javad Alizadeh,Mohamad Reza Kavianpour,Bahareh Kamranzad,Amir Etemad-Shahidi 한국기상학회 2019 Asia-Pacific Journal of Atmospheric Sciences Vol.55 No.4
This study proposes a simple approach based on Weibull distribution parameters for downscaling climatic wind speed and direction. In this method, the Weibull parameters of a Global Climate Model (GCM) are modified using Weibull parameters of the reference data (ECMWF). To correct the wind direction, the downscaling technique was applied to the eastward and northward wind components. All the wind components were simply transformed to positive values in order to fit a Weibull distribution. The unbiased wind speed was calculated by integrating the corrected wind components. Moreover, other models were considered to directly modify the wind speed (not wind components) using the same methodology. Performance and ability of the proposed approach were evaluated against the existing statistical downscaling techniques such as Multiplicative Shift Method (MSM), quantile mapping and support vector regression. In the models, the 6-h GCM wind component/speed was the sole predictor and the ECMWFreanalysis wind data was considered as the predictand. It is demonstrated that direct application of the proposed method on the wind speed slightly gives better estimation of the predictand rather than its application on wind components. The results indicate theWeibull distribution based method outperforms the other techniques for wind direction and magnitude. The method provides sound predictions for a wide range of wind speed from low to high values. By using the proposed downscaling technique for wind components, wind direction can be adjusted accordingly.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
