Working with the Met Office on modelling solar atmosphere and Space Weather events related to the sun, funded by the Chinese Scholarship Council. Tina graduated from the University of Bath in 2018 with an MSc Applied Maths, where she started her Space Weather project with the Met Office. The research is a combination of magnetohydrodynamics in PDEs, stats modeling, high performance computing and Machine Learning. Before the MSc, she worked in strategic consulting for 10 months in India and in private equity as an investment manager for nearly 3 years in China. Her Master’s research placement was on developing new methodology and index for Space Weather forecast verification as well as building new models to improve the current forecast by Met Office and NASA. She speaks three languages and is a professional artist in Chinese ink-and-wash painting. Besides, Tina enjoys dancing, reading, programming and learning French and Latin.

Research project title:
The role of precursors of active regions in space weather forecasting: reliably predicting CMEs and SEPs before their occurrence with the help of machine learning

Supervisor(s):
Chris Budd, Apala Majumdar, Silvia Gazzola, Tom Fincham Haines

Project description:
The Met Office produces real-time operational space weather forecasts: severe space weather has appeared on the UK National Risk Register since 2011. Space weather can have major impacts on UK and international critical infrastructure (e.g. the electricity grid, satellites, aviation, Global Navigation Satellite System (GNSS) positioning, navigation and timing, radio communications) and on human health. The Met Office is constantly striving to produce improved space weather forecasts to meet their customers’ needs, but this can be very challenging. The difficulty of solar weather forecasting is due to the uncertainty of solar movement and speed of arrival of an observed event (a few minutes or up to a few days before arrival on Earth). The focus of this project is to develop improved solar weather modelling forecasts based on the analysis of solar and near-Earth space observation data. New data from the spacecraft Parker will be available for research and there is no doubt that Parker can introduce many new results. Part of the approach will use machine learning to train proper models for better forecasting results. Additionally there is the possibility of combining methodologies in applicable probability, statistics theories and methods (for example time series analysis, Markov process and Bayesian simulations) and maths modelling methods in areas of numerical PDEs or stochastic PDEs. Most of those aspects are new and based on several recent technology break-troughs. The output of this project is to establish new methods in tracking space weather forecasts and seek new conclusions from observations by those new technologies.