A key limitation of weather and climate forecasts is that cloud and turbulent processes are very complex, and remain crudely represented in weather and climate models due to lack of an underlying theory. It is now possible to simulate clouds in great detail on supercomputers, and vast amounts of satellite data are now available. This interdisciplinary project will apply methods from statistical physics, which are only beginning to be used in the environmental sciences, to better exploit such data, advance our basic understanding, and produce more useful models for weather and climate changes.
An undergraduate degree in physics would be a great background for this student, but degrees in other quantitative fields, including physical or computer sciences or engineering, could also be suitable. We encourage students with knowledge of university-level mathematics, statistics or statistical mechanics, and/or dynamical systems who are keen to learn about weather and climate; or, students with knowledge of atmosphere/climate physics who are keen to advance their quantitative skills. The position would involve data analysis, modelling and/or theory, and likely involve computation with large datasets. The most important quality is curiosity and a desire to apply basic methods from physical sciences to solve tough problems in environmental science!