Machine learning emulation of a local-scale UK climate model
- URL: http://arxiv.org/abs/2211.16116v1
- Date: Tue, 29 Nov 2022 11:44:35 GMT
- Title: Machine learning emulation of a local-scale UK climate model
- Authors: Henry Addison, Elizabeth Kendon, Suman Ravuri, Laurence Aitchison,
Peter AG Watson
- Abstract summary: We show for the first time a machine learning model that is able to produce realistic samples of high-resolution 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.
- Score: 22.374171443798037
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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