Fast Numerical and Machine Learning Approaches for Satellite Data in Weather Prediction

Extreme weather events are becoming increasingly frequent due to climate change, making accurate weather forecasting more critical than ever for protecting lives and livelihoods. This PhD project at the University of Reading aims to develop innovative, fast numerical and machine learning approaches to maximize the use of high-resolution satellite data in weather prediction. The research will focus on improving data assimilation—the process of blending millions of Earth observations with physics-based forecast models to initialize the next weather forecast. Currently, operational weather forecasting services are unable to utilize a large proportion of available satellite observations, primarily due to limitations in fast numerical techniques. As a student, you will work at the intersection of numerical linear algebra and machine learning, developing new methods to accelerate operational forecasting and enable the assimilation of denser observational data. The project will begin with idealized models and datasets, allowing for rapid progress and skill development. You will also gain exposure to real-world forecasting challenges through regular seminars and meetings with collaborators from national weather centers. You will be affiliated with the National Centre for Earth Observation (NCEO), providing access to cutting-edge research on space-based satellite instruments and data. The Data Assimilation Research Centre (DARC) at Reading offers a supportive, interdisciplinary environment with established collaborations with national and international weather services and academic groups in China, Japan, and the USA. Opportunities for placements or research visits in the UK or abroad may be available. This position is part of the Mathematics for Future Climate Centre for Doctoral Training (CDT), which provides comprehensive training in climate science, physical sciences, scientific computing, statistics, and data analysis. The CDT also offers extensive personal and professional development opportunities and fosters a multi-disciplinary outlook through interactions with a wide network of academic, research, and industrial/policy partners. Funding is provided through a full UKRI stipend and home-level PhD tuition fees, supported by the EPSRC Centre for Doctoral Training in the Mathematics for our Future Climate. Applicants should have a strong background in mathematics, physics, computer science, engineering, or a related discipline, with quantitative skills and experience in scientific computing, data analysis, or machine learning. International applicants may need to demonstrate English language proficiency. Applications are accepted year-round. For more information, visit the project page or contact the department.