Trenary L, DelSole T, Tippett M K, Pegion K
Department of Atmospheric, Oceanic, and Earth Sciences George Mason University Fairfax VA USA.
Center for Ocean-Land-Atmosphere Studies Fairfax VA USA.
J Adv Model Earth Syst. 2018 Apr;10(4):1074-1086. doi: 10.1002/2017MS001204. Epub 2018 Apr 20.
The mean square error (MSE) of a lagged ensemble of monthly forecasts of the Niño 3.4 index from the Climate Forecast System (CFSv2) is examined with respect to ensemble size and configuration. Although the real-time forecast is initialized 4 times per day, it is possible to infer the MSE for arbitrary initialization frequency and for burst ensembles by fitting error covariances to a parametric model and then extrapolating to arbitrary ensemble size and initialization frequency. Applying this method to real-time forecasts, we find that the MSE consistently reaches a minimum for a lagged ensemble size between one and eight days, when four initializations per day are included. This ensemble size is consistent with the 8-10 day lagged ensemble configuration used operationally. Interestingly, the skill of both ensemble configurations is close to the estimated skill of the infinite ensemble. The skill of the weighted, lagged, and burst ensembles are found to be comparable. Certain unphysical features of the estimated error growth were tracked down to problems with the climatology and data discontinuities.
针对气候预测系统（CFSv2）中尼诺3.4指数月度预测的滞后集合，研究了其均方误差（MSE）与集合规模和配置的关系。尽管实时预测每天初始化4次，但通过将误差协方差拟合到参数模型，然后外推到任意集合规模和初始化频率，可以推断出任意初始化频率和突发集合的MSE。将该方法应用于实时预测，我们发现当每天包含4次初始化时，对于1至8天的滞后集合规模，MSE始终达到最小值。这个集合规模与业务中使用的8至10天滞后集合配置一致。有趣的是，两种集合配置的技巧都接近无限集合的估计技巧。加权、滞后和突发集合的技巧被发现是可比的。估计误差增长的某些非物理特征被追溯到气候学和数据不连续性问题。
