次季节天气型预报中的机会窗口：一种统计-动力方法
Windows of opportunity in subseasonal weather regime forecasting: A statistical-dynamical approach
AI总结：
本研究利用MJO和SPV位相信息并结合神经网络，提出统计-动力方法以改进三周提前期的欧洲天气型活动预报，较ECMWF提升2.9%的准确率，尤其改善了格陵兰阻塞和欧洲阻塞预报。
AI中文摘要：
MJO（热带季节内振荡）和SPV（平流层极涡）是温带地区次季节可预报性的重要来源。已有研究表明，MJO与SPV的联合相互作用能够调节NAO（北大西洋涛动）的优势位相以及天气型的出现，这对欧洲天气具有重要影响。然而，在三周提前期上改进延伸期数值天气预报（NWP）的研究仍相对不足。本研究探讨了MJO和SPV位相如何影响格陵兰阻塞（GL）活动，并将大气状态信息整合到神经网络中，以提升第三周天气型活动预报能力。我们利用ECMWF再分析资料和回报资料定义了一个天气型活动度量指标。在再分析资料中，我们发现MJO第7、8和1位相以及弱SPV位相之后GL活动增强，表明存在与先前研究一致的气候学机会窗口。然而，ECMWF预报技巧仅在MJO第8和第1位相以及弱SPV位相中有所提升，揭示出略有不同的模式机会窗口。接下来，我们探索将这些发现应用于后处理工具中。基于MJO/SPV-NAO关系的气候学预报为延伸期GL活动预报提供了一种纯统计方法，且不依赖于NWP模式。值得注意的是，以MJO为条件的气候学预报在与观测GL活动进行对比评估时表现出清晰的信号。统计-动力模型利用神经网络将历史大气状态数据与NWP导出的天气型度量指标相结合，在所考虑的所有天气型中均改善了天气型活动预报，与ECMWF相比，在预报主导天气型方面实现了2.9%的绝对准确率提升。这对欧洲区域的阻塞形势尤其有益，因为NWP模式在该区域通常表现不佳。大气条件预报和神经网络预报可作为NWP模式之外有价值的决策支持工具，从而增强S2S（次季节到季节）预测的可靠性。
英文摘要：
MJO and SPV are prominent sources of subseasonal predictability in the Extratropics. With relevance for European weather it has been shown that the joint interaction of MJO and the SPV can modulate the preferred phase of the NAO and the occurrence of weather regimes. However, improving extended-range NWP at three-week lead times remain under-explored. This study investigates how MJO and SPV phases affect Greenland Blocking (GL) activity and integrates atmospheric state information into a neural network to enhance week-three weather regime activity forecasts. We define a weather regime activity metric using ECMWF reanalysis and reforecasts. In reanalyses we find increased GL activity following MJO phases 7,8 and 1, as well as weak SPV phases, indicating climatological windows of opportunity in line with previous studies. However, ECMWF forecast skill improves only in MJO phases 8 and 1 and weak SPV phases, identifying somewhat different model windows of opportunities. Next we explore using these findings in post-processing tools. Climatological forecasts based on MJO/SPV-NAO relationships provide a purely statistical approach to extended-range GL activity forecasting, independent of NWP models. Notably, MJO conditioned climatological forecasts show clear signals when evaluated against observed GL activity. Statistical-dynamical models, using neural networks that combine historical atmospheric state data with NWP-derived weather regime metrics improve weather regime activity forecasts across all regimes considered, achieving an absolute accuracy increase of 2.9% in forecasting the dominant weather regime compared to ECMWF. This is particularly beneficial to Blocking in the European domain, where NWP models often underperform. Atmospheric conditioned and neural network forecasts serve as valuable decision-support tools alongside NWP models, enhancing the reliability of S2S predictions.
