Ongoing projects
Uncertainty Quantification for Parametrisation, including machine learning approaches
A key theme is the development of 'stochastic parametrisations' - these are representations of processes such as clouds and thunderstorms which explicitly account for uncertainties. This means our weather and climate predictions become Monte-Carlo type simulations, where we see how uncertainties in the small-scale processes influence the rest of the climate system.
Peter is working on using machine learning tools such as recurrent neural networks to predict the ideal stochastic perturbations within a machine-learnt parameterisation of sub-grid physics.
Zhixiao is working on developing a stochastic, data-driven parameterisation of organised convective storms to be used in the UK Met Office's operational weather forecasting system.
Greta is working on developing a machine learning stochastic parameterization for the convection trigger in climate models, directly trained on observations.
Laura is breaking down uncertainties associated with machine learning parameterisations by source: epistemic uncertainty (from uncertainty in the machine learning model) and aleatoric uncertainty (from uncertainty in the training dataset).
Edward works on the Model Uncertainty- Model Intercomparison Project. It compares small-scale process representations across several international modelling centres to better characterise uncertainties in these processes
Model validation
A complementary approach to improving model approximations is increasing the resolution of our simulators, so that they explicitly capture more processes. But just how good are these high-resolution computer models? To answer this question, we develop novel approaches to compare models with observations.
Lilli is evaluating how clouds and convection are represented in global km-scale climate models, using satellite observations and a variety of methods including fractal analysis and machine learning.
Simon is working to understand the benefits of high-resolution modelling with machine learning-based data analysis. A key focus is on the relative roles of atmosphere and ocean resolution
Kristian tries to understand how and why increased model resolution affects the representation and predictability of midlatitude dynamics, such as blocking and weather regimes.
Predictability
Salah is working on applying coarse-graining analysis to atmospheric data and simulations to gain a deeper understanding of the dynamics and predictability of the weather.
Emma: ML for flood prediction
Edward works on predictability and deep convection as continuation of PhD (Wavestoweather project) and masters research, which occasionally also involves a student or internship project.
Machine Learning for Earth-system prediction
Bradley is working on a world module approach to modelling ENSO for adaptability to out-of-distribution data and improved mechanistic understanding - Intelligent Earth CDT mini-project.
Bobby is investigating how machine learning models can be used to improve the quality of earth system model simulations, specifically focused on making the initialisation of the ocean more efficient by using machine learnt emulators.
Andrew is working on data-driven dynamical modelling of large-scale climate variability and precipitation in the midlatitudes with the goal of improving process understanding.
Maren is currently doing a mini-project with the Atmospheric Processes group in which she investigates whether machine learning-based weather prediction models generalize spatially.