단시간 다중모델 앙상블 바람 예측
Wind Prediction with a Short-range Multi-Model Ensemble System
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참고문헌
초록
Abstract
In this study, we examined the new ensemble training approach to reduce the systematic eror and improve pskill of wind by using the Short-range Ensemble prediction system (SENSE), which is the mesoscale multi-model ensem-ble prediction system. The SENSE has 16 ensemble members based on the MM5, WRF ARW, and WRF NM. We evaluated the skill of surface wind prediction compared with AWS (Automatic Weather Station) observation during the summer season (June - August, 206). At first stage, the corection of initial state for each member was performed with respect to the observed values, and the corrected members get the training stage to find out an adaptive weight function, which is formulated by Root Mean Square Vector Eror (RMSVE). It was found that the optimal training period was 1-day through the experiments of sensitivity to the training interval. We obtained the weighted ensemble average which reveals smaller errors of the spatial and temporal pattern of wind speed than those of the simple ensemble average.
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