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Statistical Science

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Data Science Methodology for Weather and Climate

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Research Group Overview

This research group focuses on theory and computational aspects of data science approaches to climate and weather modelling. This includes research in uncertainty quantification, data assimilation, probabilistic numerics, multi-fidelity/multi-resolution modelling, ensemble modelling and machine learning for climate.

The research group is a key component of the Met Office Academic Partnership, a cross-department initiative at UCL led by Prof. Serge Guillas.

Research Group Members

Name  Keywords
Francois-Xavier Briol (Research Group Lead)Bayesian probabilistic numerical methods, Gaussian processes, Machine learning, Uncertainty quantification
Alexandros BeskosData assimilation, Particle filters
Richard ChandlerClimatology, Hydrology, Multimodel ensembles, Downscaling and weather generation, Risk and uncertainty
Marc DeisenrothData assimilation, Gaussian processes, Machine learning
Serge Guillas (Met Office Partnership Lead)Gaussian processes, Uncertainty quantification (emulation, calibration) of complex computer models
Matt KusnerCausal Inference, Machine Learning
Paul NorthropExtreme Values, Hydrology, Multimodel ensembles
Ricardo SilvaCausal Inference, Machine learning