传统的降水检验中多采用TS评分等分类检验方法,这些方法由于使用点对点的比较方式,使得降水评估受到空间及时间微小差异所带来的"双重惩罚",无法客观反映降水预报的真实能力.邻域空间检验方法(FSS)通过比较不同邻域尺度内降水发生概率以获取降水的综合评估信息,是弥补传统技巧评分"双重惩罚"问题的有效手段.利用GRAPES区域模式2016年7—8月逐日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,考察邻域空间检验方法在降水检验中的效果及与传统技巧评分的差异,最终获取结合空间及传统分类检验方法的降水评估方案.结果表明,TS评分在较小量级降水阈值条件下可获取与FSS相似的评估效果,但在较高量级阈值下评分特征不稳定,而FSS评分在不同阈值条件下均可表现出相似的变化趋势,在降水预报较弱时段依然存在明显的评估差异,易于区分逐日强降水预报特征.通过对多雨(南部)、少雨(北部)分区的检验发现,FSS评分在南部区域对强降水的描述能力明显优于TS评分,在强降水相对缺乏的地区(北部),TS评分对强降水描述能力甚弱,邻域空间检验方法则可有效呈现逐日强降水预报差异.%Methods for dichotomous forecasts(yeso)(Threat Score et al.)are always used in the verification of precipitation forecasts.But these standard methods are affected by the well-known double penalty problem.In a point to point comparison,small displacements in space or time with regard to the observation causes double-penalty to the forecast.Spatial techniques as the popular scientific and diagnostic meth-ods have been recently developed to alleviate this problem.The neighborhood spatial verification method Fraction Skill Score(FSS)directly compares the fractional coverage of events in windows surrouding the observations and forecasts. Based on daily forecast products from GRAPES regional model and QPE products from national meteorological information center during July and August in 2016,we study the ef-fects of FSS in precipitation verification and its difference from traditional skill score.It shows that the TS can get a similar trend to FSS with low precipitation threshold and becomes unstable with high threshold.The FSS show a similar trend with different thresholds,and it still rep-resents a significant difference in the period of weak forecast that should distinguish features of daily heavy rainfall forecast.By partitioning verification for North and South,FSS score in the southern region shows the ability to describe the strong rainfall significantly better than the TS score.In the area with relatively severe precipitation(north),the TS score has a low ability to describe the heavy rainfall,and the neighbor-hood spatial test method can effectively show the difference of daily precipitation forecast.
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