When applied to precipitation on large forecast domains, the probability‐matched ensemble mean (PM mean) can exhibit biases and artifacts due to using distributions from widely‐varying precipitation regimes. Recent studies have investigated localized PM (LPM) means, which apply the PM mean over local areas surrounding individual points or local patches, the latter requiring far fewer computational resources. In this study, point‐wise and patch‐wise LPM means are evaluated for 18‐24‐h precipitation forecasts of a quasi‐operational ensemble of 10 Finite‐Volume Cubed‐Sphere (FV3) forecast members. Point‐wise and patch‐wise LPM means exhibited similar forecast performance, outperforming PM and simple means in terms of fractions skill score and variance spectra, while exhibiting superior bias characteristics when light smoothing was applied. Based on the results, an LPM mean using local patches of 60 × 60 km and calculation domains of 180 × 180 km is well suited for operational warm‐season precipitation forecasting over the Contiguous United States.

中文翻译：

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集合共识降水预测的逐点和逐点局部概率匹配均值算法的比较和验证

当将其应用于大型预测域的降水时，由于使用了范围广泛的降水体制中的分布，因此概率匹配的集合均值（PM均值）可能会显示偏差和伪影。最近的研究研究了局部PM（LPM）手段，该手段将PM手段应用于围绕单个点或局部面的局部区域，后者需要更少的计算资源。在本研究中，对10个有限体积立方球体（FV3）预报成员的准业务集合的18-24h降水预报评估了点向和斑块LPM均值。就分数技巧得分和方差谱而言，逐点和逐块LPM手段表现出相似的预测性能，优于PM和简单手段，而在应用光平滑时表现出出众的偏置特性。