AI for Atmospheric Systems

We are building a generative model for cloud structures conditioned on environmental conditions, integrating both high-resolution satellite data and reanalysis data. By being able to generate and reconstruct cloud structures under varying environmental conditions, the model will allow us to reduce uncertainties in future climate predictions and facilitate the detection and quantification of cloud feedbacks across the satellite record. This purely data-driven approach to constraining cloud feedbacks will be complementary to existing work that relies on physics-based models.

Bio:
“I am an Encode AI Fellow in the Climate Processes group led by Prof Philip Stier. As part of the Encode fellowship, we are building a generative model for cloud structures with the primary goal is to constrain cloud climate feedbacks. Before starting the fellowship, I was conducting research on compression algorithms for climate and weather data funded by the Embed2Scale project.

I completed my PhD as part of the EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems here in Oxford. In my research I developed novel Bayesian inference algorithms for models defined through probabilistic programs.”