A Multi-Scale Deep Learning Framework for Projecting Weather Extremes
- URL: http://arxiv.org/abs/2210.12137v1
- Date: Fri, 21 Oct 2022 17:47:05 GMT
- Title: A Multi-Scale Deep Learning Framework for Projecting Weather Extremes
- Authors: Antoine Blanchard, Nishant Parashar, Boyko Dodov, Christian Lessig,
Themistoklis Sapsis
- Abstract summary: Weather extremes are a major societal and economic hazard, claiming thousands of lives and causing billions of dollars in damage every year.
General circulation models (GCMs), which are currently the primary tool for climate projections, cannot characterize weather extremes accurately.
We present a multi-resolution deep-learning framework that corrects a GCM's biases by matching low-order and tail statistics of its output with observations at coarse scales.
We use the proposed framework to generate statistically realistic realizations of the climate over Western Europe from a simple GCM corrected using observational atmospheric reanalysis.
- Score: 3.3598755777055374
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Weather extremes are a major societal and economic hazard, claiming thousands
of lives and causing billions of dollars in damage every year. Under climate
change, their impact and intensity are expected to worsen significantly.
Unfortunately, general circulation models (GCMs), which are currently the
primary tool for climate projections, cannot characterize weather extremes
accurately. To address this, we present a multi-resolution deep-learning
framework that, firstly, corrects a GCM's biases by matching low-order and tail
statistics of its output with observations at coarse scales; and secondly,
increases the level of detail of the debiased GCM output by reconstructing the
finer scales as a function of the coarse scales. We use the proposed framework
to generate statistically realistic realizations of the climate over Western
Europe from a simple GCM corrected using observational atmospheric reanalysis.
We also discuss implications for probabilistic risk assessment of natural
disasters in a changing climate.
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