This study proposes a hybrid approach to improving subseasonal prediction skills by bridging a conventional statistical model and a dynamical ensemble forecast system. Based on the perfect prognosis method, the phase of the Arctic Oscillation (AO) from the European Centre for Medium-range Weather Forecasts ensemble forecast system is used as a predictor in a composite based statistical model to predict the wintertime surface air temperature in the Northern Hemisphere. The hybrid model, which employs AO phases predicted by the dynamical model for weeks 1–4, generally outperforms the conventional statistical model for lead times of weeks 2–6. The improved skill score is due to the high accuracy of the AO forecast from the dynamical model and the strong lagged connection between the AO and temperature. This study thus lays the groundwork for the potential use of combining climate variability, statistical relation, and dynamical forecasting.

中文翻译：

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通过将统计模型与北极涛动动态预报相结合来增强次季节温度预测

本研究提出了一种混合方法，通过将传统统计模型和动态集合预报系统相结合来提高次季节预报技能。基于完美的预测方法，来自欧洲中期天气预报中心集合预报系统的北极涛动 (AO) 相位被用作基于复合统计模型的预测器，以预测北方冬季地表气温。半球。混合模型采用动态模型预测的第 1-4 周的 AO 阶段，通常优于第 2-6 周交付周期的传统统计模型。技能分数的提高是由于动力学模型对AO预测的准确性高以及AO与温度之间的强滞后联系。