Developing data-driven weather and climate models

Traditional weather and climate models are very effective at producing predictions of the ocean and atmosphere, but require vast amounts of compute power and time to produce their forecasts. We look to develop alternative data-driven models, which learn the dynamics of these systems through machine learning techniques in order to provide forecasts in a faster and more computationally efficient manner. This would enable researchers to run more experiments, giving us better appreciation of uncertainty in our predictions and improving our understanding of the earth system and human impacts on it.

Who's involved


Our congratulations to @torben_sell on the recent approval of his PhD thesis “Advanced Bayesian Monte Carlo Methods… View on Twitter