Severe-weather prediction using convective-scale ensembles

使用对流规模集合进行灾害性天气预测

• 批准号: 418372-2012
• 负责人: Kirshbaum, Daniel
• 金额: $ 2.33万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=573619
• 关键词: Severe; weather; prediction; using; convective

Convective storms represent a key element of the climate system, with impacts on scales ranging from microscopic aerosol particles up to the global circulation. On the regional scale they provide valuable rainfall that sustains life and agriculture but also inflict severe weather including hailstorms and flash-flooding. A process of such critical importance demands accurate prediction from operational weather centres. However, because of their highly turbulent, chaotic, and multi-scale nature, these storms are tremendously difficult to accurately predict. Even small uncertainties in numerical weather prediction models cause storms to form in the wrong place, at the wrong time, or to not develop at all. Although some level of forecast uncertainty is unavoidable, convection forecasts can still improve by diminishing the magnitude of this uncertainty and by adopting probabilistic methods that quantify its impact on the model outcome. An emerging method for achieving the above goals is convective-scale ensembles, which are only now becoming computationally feasible. These consist of numerous forecasts that capture the spread of potential outcomes arising from model-based uncertainties. The ensemble framework also allows observations, notably from precipitation radars, to be naturally assimilated into the model to diminish errors and improve storm predictability. In addition to their practical benefits, these ensembles allow for a deeper understanding of the mechanisms and sensitivities of convective storms. By providing numerous realizations of a given event, they allow for the exposure of subtle process interactions that would otherwise be imperceptible. In this five-year project, a convective-scale ensemble system will be developed and applied to improve the physical understanding and prediction of convective storms over Canada. This will be undertaken in collaboration with Environment Canada, which will maximize the resources available for research progress and facilitate the transfer of our findings into operational practice.

对流风暴是气候系统的一个关键要素，其影响范围从微观气溶胶颗粒到全球环流。 在区域范围内，它们提供宝贵的降雨，维持生命和农业，但也造成恶劣天气，包括冰雹和山洪暴发。 如此重要的一个过程需要业务气象中心的准确预测。 然而，由于其高度湍流，混乱和多尺度的性质，这些风暴非常难以准确预测。 即使是数值天气预报模型中很小的不确定性，也会导致风暴在错误的地点、错误的时间形成，或者根本不发展。 虽然某种程度的预报不确定性是不可避免的，对流预报仍然可以通过减少这种不确定性的大小，并通过采用概率方法，量化其对模式结果的影响。 一种实现上述目标的新兴方法是对流尺度集合，它现在才变得计算可行。 这些预测包括许多预测，这些预测捕捉了基于模型的不确定性所产生的潜在结果的传播。 集合框架还允许观测，特别是来自降水雷达的观测，被自然地同化到模式中，以减少误差并提高风暴的可预报性。 除了它们的实际好处，这些合奏允许更深入地了解对流风暴的机制和敏感性。 通过提供给定事件的许多实现，它们允许暴露微妙的过程交互，否则这些交互将无法察觉。 在这个为期五年的项目中，将开发并应用对流尺度集合系统，以提高对加拿大上空对流风暴的物理理解和预测。 这项工作将与加拿大环境部合作进行，加拿大环境部将最大限度地利用可用于研究进展的资源，并促进将我们的研究结果转化为实际操作。