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

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.