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: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=596323
• 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.

对流风暴是气候系统的关键要素，对从微观气溶胶颗粒到全球循环的尺度产生影响。在区域规模上，它们提供了有价值的降雨，可以维持生命和农业，但也造成了恶劣的天气，包括冰雹和洪水。如此重要的过程需要从运营天气中心进行准确的预测。但是，由于它们的高度动荡，混乱和多尺度的性质，这些风暴极为难以准确预测。即使在数值天气预测模型中，即使是小的不确定性也会导致在错误的时间，或根本不发展的地方形成风暴。尽管不可避免的是某种水平的预测不确定性，但对流预测仍然可以通过减少这种不确定性的幅度并采用概率方法来改善其对模型结果的影响。