Diagnosing Rossby wave train errors in numerical weather forecasts (RW-FCST)

欧洲上空的恶劣天气偶尔会与长期存在的波列有关。这意味着在一周甚至更长的时间尺度上具有潜在的可预测性-这种潜力今天还远远没有得到利用。作为改进预报的先决条件，该项目旨在诊断当前天气预报系统中有关长寿命波列的生成、传播和衰减的缺陷。在第一阶段，我们证明了在气候意义上，欧洲的恶劣天气也与独特的长寿命波列有关。我们开发了一种新的诊断来量化关于波列的预测技能。该项目将在第2阶段继续进行，大幅推进和扩展分析。首先，我们将应用我们的新诊断来系统地量化长数据集的预测误差。此外，我们将探索和发展所谓的“波活动通量”诊断的效用，这是不同于和部分补充更传统的诊断，包括我们自己的一个。我们将把我们的诊断应用于预报的集合，以了解这些集合在多大程度上能够量化关于长寿命波列的预报不确定性。总的来说，这个项目追求与RW-GUIDE相似的目标，但使用了截然不同的方法，并且在很大程度上是互补的。

Severe weather over Europe is known to be occasionally associated with long-lived wave trains. This suggests potential predictability on the time-scale of a week and even longer — a potential which is far from being exploited today. As a prerequisite for improved forecasts this project aims to diagnose deficiencies in current weather forecast systems regarding the generation, propagation, and decay of such long-lived wave trains. During phase 1 we demonstrated that also in a climatologicalsense severe weather over Europe is associated with distinct long-lived wave trains. We developed a novel diagnostic to quantify predictive skill regarding wave trains. The project will be continued during phase 2 by significantly advancing and extending the analysis. First, we shall apply our new diagnostic to systematically quantify forecast errors in long data sets. In addition, we shall explore and develop the utility of so-called “wave activity flux” diagnostics, which are distinct from and partly complementary to the more conventional diagnostics including our own one. We shall apply our diagnostics to ensembles of forecasts in order to learn how well these are able to quantify forecast uncertainty regarding long-lived wave trains. Overall, this project pursues similar goals as RW-GUIDE, but uses vastly different and largely complementary methods.