Machine learning emulation of a local-scale UK climate model

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Abstract

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 samples of high-resolution rainfall 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.
Original languageEnglish
Publication statusPublished - 29 Nov 2022
EventTackling Climate Change with Machine Learning workshop at NeurIPS 2022 - Online
Duration: 9 Dec 20229 Dec 2022
https://www.climatechange.ai/events/neurips2022

Workshop

WorkshopTackling Climate Change with Machine Learning workshop at NeurIPS 2022
Period9/12/229/12/22
Internet address

Bibliographical note

8 pages, 5 figures, Tackling Climate Change with Machine Learning workshop at NeurIPS 2022

Structured keywords

  • Interactive Artificial Intelligence CDT

Keywords

  • physics.ao-ph
  • cs.LG

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