Advances in data assimilation to improve the quality of weather and environmental forecasts

数据同化方面的进步提高了天气和环境预报的质量

• 批准号: 357091-2008
• 负责人: Gauthier, Pierre
• 金额: $ 1.46万
• 依托单位: Université du Québec à Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2011
• 资助国家: 加拿大
• 起止时间: 2011-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=483104
• 关键词: Advances; data; assimilation; improve; quality

Data assimilation is concerned with using information from all available observations obtained from a variety of instruments to estimate the current state of the atmosphere to improve the quality of weather forecasts. Based on statistical estimation principles, an a priori estimate of the atmosphere is provided by a short term forecast obtained from a numerical weather prediction (NWP) model. Its accuracy is represented by the background-error covariances, which depend on the meteorological situation. In cases associated with the onset of atmospheric instability, the initial conditions need to embed the dynamical structure that can trigger the development of a weather system. This proposal is concerned in making advances in data assimilation by introducing flow-dependent structures within the assimilation process that are in a position to improve the quality of forecasts of high impact weather events in the short, medium and long range. This will also be applied to adaptive data selection to improve the use of the vast amount of satellite data received daily at operational weather centres.

数据同化与使用从各种工具获得的所有可用观察结果的信息有关，以估计大气的当前状态以提高天气预报的质量。基于统计估计原理，通过从数值天气预测（NWP）模型获得的短期预测提供了对大气的先验估计。它的准确性由背景越协方差，取决于气象状况。如果与大气不稳定的发作相关，则需要嵌入可以触发天气系统发展的动力结构。该建议涉及通过在同化过程中引入流动相关结构来提高数据同化的进展，该结构可以提高短，中和远距离的高影响力天气事件的预​​测质量。这也将应用于自适应数据选择，以改善在操作天气中心每天收到的大量卫星数据的使用。