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降水概率特征对概率降水预报检验的影响研究

The influence of observational probability of precipitation on the verification of probability of forecasted precipitation

  • 摘要: 利用2015—2017年6—8月ECMWF集合预报系统(EPS)降水预报资料和国家级气象站逐小时降水观测资料,以EPS在我国中东部地区的集合预报检验为例,首先分析了集合预报系统对不同量级降水预报的可靠性和预报偏差样本特征;然后,基于观测事件是否出现分别讨论了不同观测概率对集合预报BS评分(BS)和集合降水预报区分度的影响。结果表明:(1)EPS在中雨量级降水预报中的表现最好;对小雨量级降水,易预报偏大,且偏大样本主要出现在0~2 mm小降水中;对暴雨以上量级降水,主要以预报偏小为主,且85 mm以上降水几乎全部漏报。(2)实况降水出现时,随降水量级增大,概率预报较实况偏小程度逐渐增大;实况降水未出现时,随着降水量级增大,概率预报较实况偏大程度逐渐减小。(3)通过分开计算观测概率为0及观测概率为1时概率预报的BS,可从一定程度上消除因样本分布不均导致BS无法客观评估概率降水预报能力的缺陷,同时可从该检验直接得到预报误差的主要原因是预报不足还是预报过度。

     

    Abstract: Based on the precipitation forecast data of ECMWF ensemble prediction system (EPS) and the hourly precipitation observational data of the national meteorological stations between June and August from 2015 to 2017, taking the verification of ensemble prediction in the central and eastern China from the EPS as an example, we first analyzed the reliability and deviation sample characteristics of the ensemble prediction system on the precipitation forecast at different levels. Then, based on whether the observed events occur, the effects of different observational conditional probabilities on the ensemble forecast BS score (BS) and ensemble precipitation forecast discrimination, respectively, were discussed. The results show that (1) EPS has the best performance in forecasting moderate rainfall, but it overestimates the precipitation of light rain level, and most overestimation appears in the small precipitation range of 0-2 mm. For precipitation above heavy rain, EPS forecasted precipitation is dominated by smaller precipitation, and misses almost all of the precipitation above 85 mm. (2) When precipitation is observed, the negative bias of forecasted probability against the observation gradually increases with the increase of precipitation level. When precipitation is not observed, the positive bias of forecasted probability against the observation gradually decreases with the increase of precipitation level. Through calculating the BS of forecasted probability separately for the observational probability of 0 or 1, the defects, that the BS cannot be used to evaluate objectively the forecast ability of probabilistic precipitation due to uneven sample distribution, can be eliminated to some extent. In addition, it can be directly obtained from the validation that the main reason of prediction bias is due to insufficient or excessive forecast.

     

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