The rationale behind the success of multi-model ensembles in seasonal forecasting - II. Calibration and combination
The rationale behind the success of multi-model ensembles in seasonal forecasting - II. Calibration and combination
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DOI：
10.1111/j.1600-0870.2005.00104.x
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发表时间：
2005-05-01
影响因子：
2
通讯作者：
Palmer, TN
中科院分区：
文献类型：
--
作者：
Doblas-Reyes, FJ;Hagedorn, R;Palmer, TN
DEMETER多模式集合系统用于研究通过校准单模式集合并将它们组合起来发布多模式预测可以实现的季节可预测性的增强。评估了确定性和概率预测的预测质量，并将其与所有单一模型权重相等的简单多模型集成的技能进行了比较。校准和组合均采用交叉验证进行。使用典型相关分析进行模型调整，并使用方差膨胀来提高可靠性。结果表明，只要训练时间序列足够长，模式调整和通货膨胀都能提高单模式组合的热带预报技巧。温带地区也发现了一些改进，尽管主要是由于与通货膨胀有关的可靠性增加。由于简单的多模型集成相对较高的可靠性，校准的有益影响要小于单模型集成。原始的单模型预测也使用网格点多元线性回归线性组合，以创建一个优化的多模型系统。结果表明，由于回归系数缺乏鲁棒性，简单多模型集合的预测质量一般难以用多元线性回归来提高。在校准的情况下，更长的时间序列将优选实现显著的预测质量改进。在热带地区，使用目标地区模式异常的主成分作为预测因子的多元线性回归表明，即使在可用的样本量下，技术也有了实质性的提高。讨论了这些结果在操作上下文中的含义。
The DEMETER multi-model ensemble system is used to investigate the enhancement in seasonal predictability that can be achieved by calibrating single-model ensembles and combining them to issue multi-model predictions. The forecast quality of both deterministic and probabilistic predictions is assessed and compared to the skill of a simple multi-model ensemble where all the single models are equally weighted. Both calibration and combination are carried out using cross-validation. Single-model seasonal ensembles are calibrated using canonical correlation analysis for model adjustment and variance inflation for reliability enhancement. Results indicate that both model adjustment and inflation increase the skill of tropical predictions for single-model ensembles, provided that the training time series are long enough. Some improvements are also found for extratropical areas, although mostly due to an increase of reliability associated with the inflation. The beneficial impact of calibration is smaller for the simple multi-model than for the single-model ensembles due to the relatively high reliability of the former. The raw single-model predictions are also linearly combined using grid-point multiple linear regression to create an optimized multi-model system. Results indicate that the forecast quality of the simple multi-model ensemble is generally difficult to improve using multiple linear regression due to the lack of robustness of the regression coefficients. As in the case of the calibration, longer time series would be preferred to achieve a significant forecast quality improvement. Over the tropics, a multiple linear regression, that uses the principal components of the model anomalies for the target area as predictors indicates a substantial gain in skill even with the available sample size. The implications of these results in an operational context are discussed.