Nowcasting of convection in simple atmospheric models

简单大气模型中对流的临近预报

• 批准号: 2633308
• 金额: --
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2633308
• 关键词: Nowcasting; convection; simple; atmospheric; models

This project will develop theory for the "nowcasting" of convective storms in the atmosphere, using simple mathematical models. Nowcasting involves the use of current observations of a storm, and a forward-projection of the system for a short time into the future, using data-science methods including Machine Learning to extrapolate the current behaviour. This project will explore the limits of prediction which can be achieved by these methods, and will innovate ways to optimise the predictions through the choice of input data ("observations") and strategies for training an algorithm. In particular, we will explore how our understanding of the fluid dynamics of the system in question, for instance the relationship between waves and convection in the fluid, can be used to make accurate nowcasting predictions without a full solution of the governing equations.We will also have the opportunity to test the simple models against data from the new generation of very high-resolution atmospheric simulations with the Met Office's operational weather and climate prediction model, and against satellite data from Africa. Insight from these theoretical studies can be used to improve operational algorithms which are currently being generated in the National Centre for Atmospheric Science at Leeds and used to issue warnings of severe storms in Africa.

该项目将使用简单的数学模型开发大气中对流风暴“临近预报”的理论。临近预报涉及使用当前对风暴的观测，以及使用包括机器学习在内的数据科学方法来推断当前的行为，并对系统在短时间内进行前向预测。该项目将探索通过这些方法可以实现的预测的局限性，并将通过选择输入数据（“观察”）和训练算法的策略来创新优化预测的方法。特别是，我们将探索如何利用我们对所讨论系统的流体动力学的理解，例如流体中的波浪和对流之间的关系，在不完全求解控制方程的情况下做出准确的临近预报。我们还将有机会根据英国气象局业务天气和气候预测模型的新一代极高分辨率大气模拟的数据以及来自非洲的卫星数据来测试简单模型。这些理论研究的见解可用于改进利兹国家大气科学中心目前正在生成的操作算法，并用于发布非洲严重风暴的警报。