Characterising and improving understanding of mesoscale convective systems over south-east Asia using machine learning

使用机器学习表征和提高对东南亚中尺度对流系统的理解

• 批准号: 2886050
• 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: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2886050
• 关键词: Characterising; improving; understanding; mesoscale; convective

South-east Asia experiences some of the world's most severe convective storms, causing flooding and landslides which endanger human life, agriculture and infrastructure. There is, therefore, a strong socio-economic need to improve our understanding of the occurrence of such storms and their underlying physical mechanisms, to aid forecasters. A recent effort to track mesoscale convective systems (MCSs) in geostationary satellite data has provided a 5-year data set of MCSs over the entire south-east Asia region (Fig. 1, left). The PhD candidate will update this data set with recent satellite observations and use machine learning techniques to discover different types of MCSs based on properties such as their geometry and lifetime. A statistical survey of these MCS types will reveal which MCSs are most associated with high-impact weather. The student will further investigate machine learning techniques to reveal which satellite-observed features of an MCS are of greatest importance in determining its type and its contribution to high-impact weather such as heavy precipitation. Convection-permitting simulations will be used alongside observations to investigate the underlying dynamics of each type of MCS, to determine whether they behave according to existing MCS theories. The project addresses the following research questions:What morphology of MCSs can be found over south-east Asia?What are the typical storm-scale and large-scale conditions of the different morphologies of MCSs?Which morphologies are most common and where and when do they occur?Which morphologies of storms are associated with high-impact weather?How do the large-scale flow conditions modulate the storm characteristics?How are the different morphologies of storms represented in state-of-the-art convection permitting MetUM simulations?How do the different morphologies of storms match up with existing theories on the dynamics of MCSs?How can the information gained in this project aid local forecasters? Can the new insights improve nowcasting?The main observational data sets used for the project are brightness temperature from the Himawari satellite and precipitation from the Global Precipitation Measurement (GPM) mission. Convection-permitting and global MetUM simulations at various grid spacing will also be used to study the storm dynamics.

东南亚经历了一些世界上最严重的对流风暴，造成洪水和山体滑坡，危及人类生命、农业和基础设施。因此，有一个强烈的社会经济需要，以提高我们对这种风暴的发生及其潜在的物理机制的理解，以帮助预报员。最近一项利用对地静止卫星数据跟踪中尺度对流系统的工作提供了整个东南亚地区5年的中尺度对流系统数据集（图1，左）。博士候选人将使用最近的卫星观测更新此数据集，并使用机器学习技术根据其几何形状和寿命等特性发现不同类型的MCS。对这些MCS类型的统计调查将揭示哪些MCS与高影响天气最相关。学生将进一步研究机器学习技术，以揭示MCS的哪些卫星观测特征在确定其类型及其对强降水等高影响天气的贡献方面最重要。允许对流的模拟将与观测一起使用，以调查每种MCS的潜在动力学，以确定它们是否根据现有的MCS理论表现。该项目解决了以下研究问题：什么样的形态MCSs可以发现在东南亚？不同形态的MCS的典型风暴尺度和大尺度条件是什么？哪些形态是最常见的，它们发生在哪里和什么时候？哪些风暴形态与高影响天气有关？大尺度流动条件如何调节风暴特征？在最先进的对流允许MetUM模拟中，风暴的不同形态是如何表现的？风暴的不同形态如何与现有的MCS动力学理论相匹配？本项目中获得的信息如何帮助当地预报员？新的见解能改善即时预报吗？该项目使用的主要观测数据集是来自Himawari卫星的亮温和来自全球降水测量使命的降水量。还将使用各种网格间距的对流允许和全球MetUM模拟来研究风暴动力学。