1.Heihe Weather Office of Heilongjiang Province;2.National Meteorological Center;3.Chengdu University of Information Technology
National Natural Science Foundation of China (41905091)，National Key Research and Development Program of China (2017YFA0604500) ，China Meteorological Administration (CMA) Special Public Welfare Research Fund (GYHY201506002)
常规降水检验受空间及时间微小差异所带来的"双重惩罚"严重影响，邻域空间检验FSS(Fraction Skill Score)方法在确定性预报中已体现出弥补这一不足的明显优势。随着集合预报分辨率的不断提高，集合降水预报同样存在与确定性预报相似的问题，本文将FSS方法拓展至集合预报领域，构建适用于集合预报的降水空间检验指标EFSS(Ensemble Fraction Skill Score)，利用ECMWF(European Centre for Medium-Range Weather Forecasts)集合预报模式2018年夏季降水预报产品及国家气象信息中心提供的格点化降水融合产品进行分析发现，EFSS评分不受集合成员数影响，可获取一致性的评估结论。通过与适用于集合预报的常规技巧评分EETS (Ensemble Equitable Threat Score)对比分析发现，常规技巧评分受限于评分过低而无法有效反映强降水过程间差异性特征，EFSS方法则可有效提升强降水预报检验辨识度。
The traditional precipitation skill scores are affected by the well-known double penalty problem caused by slight spatial or temporal mismatches between forecast and observations. The FSS (Fraction skill score) as a popular scientific and diagnostic spatial technique has been proposed for deterministic simulations, while it shows significant advantage in solving this problem. With the ensemble forecast resolution increasing, ensemble precipitation forecasts also have similar problems with deterministic forecasts. In this paper, a new ensemble precipitation verification skill score with spatial technique EFSS (Ensemble Fraction Skill Score) is developed based on extending FSS from deterministic into ensemble forecasts. Using daily forecast products from ECMWF (European Centre for Medium-Range Weather Forecasts) ensemble forecasts and QPE products from national meteorological information center during June and August in 2018, the scoring consistency and the difference with traditional skill score in operational application have been analyzed. It shows that EFSS is not affected by the ensemble members and the consistent evaluation conclusions can be obtained. By comparison with EETS (Ensemble Equitable Threat Score), which is suitable for ensemble forecasting extending from deterministic traditional skill score, it shows that the traditional skill scoring is limited by low skill to effectively assess the different characteristics of heavy precipitation processes. However, EFSS can effectively improve the identification of heavy precipitation forecast verification.
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