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

诊断数值天气预报中的罗斯贝波列误差 (RW-FCST)

• 批准号: 60884022
• 负责人: Professor Dr. Volkmar Wirth
• 金额: --
• 依托单位: Institut für Physik der Atmosphäre
• 依托单位国家: 德国
• 项目类别: Research Units
• 财政年份: 2008
• 资助国家: 德国
• 起止时间: 2007-12-31 至 2013-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/60884022?language=en
• 关键词: Diagnosing; Rossby; wave; train; errors

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.

众所周知，欧洲的恶劣天气偶尔与长寿命的波浪列有关。这表明在一周甚至更长的时间尺度上有潜在的可预测性——这种潜力今天还远远没有被利用。作为改进预报的先决条件，本项目旨在诊断当前天气预报系统在这种长寿命波列的产生、传播和衰减方面的缺陷。在第一阶段，我们也证明了在气候学意义上，欧洲的恶劣天气与明显的长寿命波列有关。我们开发了一种新的诊断方法来量化波浪列的预测技能。该项目将在第二阶段继续推进和扩展分析。首先，我们将应用我们的新诊断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，它与包括我们自己的诊断在内的更传统的诊断不同，并部分补充。我们将把我们的诊断方法应用于预报集合，以便了解这些集合如何能够很好地量化关于长寿命波列的预报不确定性。总的来说，该项目追求与RW-GUIDE相似的目标，但使用的方法截然不同，而且在很大程度上是互补的。