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

东南亚经历了一些世界上最严重的对流风暴，引发了洪水和山体滑坡，危及人类生命、农业和基础设施。因此，迫切需要提高我们对这类风暴的发生及其潜在物理机制的了解，以帮助预报员。最近在地球静止卫星数据中追踪中尺度对流系统的努力提供了整个东南亚区域的中尺度对流系统的5年数据集(图1，左)。这位博士生将用最近的卫星观测更新这个数据集，并使用机器学习技术根据MCS的几何形状和寿命等属性发现不同类型的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，以调查每种类型MCS的潜在动力学，以确定它们的行为是否符合现有的MCS理论。该项目涉及以下研究问题：在东南亚可以发现什么形态的MCSs？不同形态的MCSs的典型风暴尺度和大尺度条件是什么？哪些形态最常见，在哪里和何时发生？哪些风暴形态与高影响天气有关？大尺度气流条件如何调制风暴特征？不同的风暴形态如何在最先进的对流中表现出来，以便进行MetUM模拟？不同的风暴形态如何与现有的MCSs动力学理论相匹配？从这个项目中获得的信息如何帮助当地的预报人员？新的洞察力能改善短时预报吗？该项目使用的主要观测数据集是来自喜马威卫星的亮度温度和来自全球降水测量(GPM)任务的降雨量。不同网格间距下的对流允许和全球MetUM模拟也将被用来研究风暴动力学。

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.