English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Weather extremes are a major societal and economic hazard, claiming thousandsof lives and causing billions of dollars in damage every year. Under climatechange, their impact and intensity are expected to worsen significantly.Unfortunately, general circulation models (GCMs), which are currently theprimary tool for climate projections, cannot characterize weather extremesaccurately. To address this, we present a multi-resolution deep-learningframework that, firstly, corrects a GCM's biases by matching low-order and tailstatistics of its output with observations at coarse scales; and secondly,increases the level of detail of the debiased GCM output by reconstructing thefiner scales as a function of the coarse scales. We use the proposed frameworkto generate statistically realistic realizations of the climate over WesternEurope from a simple GCM corrected using observational atmospheric reanalysis.We also discuss implications for probabilistic risk assessment of naturaldisasters in a changing climate.

A Multi-Scale Deep Learning Framework for Projecting Weather Extremes