A Multi-Scale Deep Learning Framework for Projecting Weather Extremes (Papers Track) Best Paper: ML Innovation

Antoine Blanchard (MIT); Nishant Parashar (Verisk Analytics); Boyko Dodov (Verisk Analytics); Christian Lessig (Otto-von-Guericke-Universitat Magdeburg); Themis Sapsis (MIT)

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Extreme Weather Hybrid Physical Models


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.

Recorded Talk (direct link)