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Machine learning emulation of a local-scale UK climate model
Henry Addison · Elizabeth Kendon · Suman Ravuri · Peter Watson · Laurence Aitchison
Event URL: https://www.climatechange.ai/papers/neurips2022/21 »

Climate change is causing the intensification of rainfall extremes. Precipitation projections with high spatial resolution are important for society to prepare for these changes, e.g. to model flooding impacts. Physics-based simulations for creating such projections are very computationally expensive. This work demonstrates the effectiveness of diffusion models, a form of deep generative models, for generating much more cheaply realistic high resolution rainfall samples for the UK conditioned on data from a low resolution simulation. We show for the first time a machine learning model that is able to produce realistic high-resolution rainfall predictions based on a physical model that resolves atmospheric convection, a key process behind extreme rainfall. By adding self-learnt, location-specific information to low resolution relative vorticity, quantiles and time-mean of the samples match well their counterparts from the high-resolution simulation.

Author Information

Henry Addison (University of Bristol)
Elizabeth Kendon (Met Office Hadley Centre)
Suman Ravuri (DeepMind)
Peter Watson (Bristol University)
Laurence Aitchison (University of Bristol)

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