Simulated annealing with seasonal sliced Wasserstein distance selects climate year subsets that are 2.5-3.5 times more representative than ENTSO-E practice and achieve 4-5 times effective sample size.
Cannon, Yoann Robin, and Denis Allard
2 Pith papers cite this work. Polarity classification is still indexing.
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SerpentFlow aligns large-scale wind patterns across GCM and observational domains then uses flow-matching to generate consistent fine-scale multivariate wind fields, outperforming standard bias correction in spatial coherence and robustness.
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Bridging the climate to energy data gap: simulated annealing for representative climate year selection
Simulated annealing with seasonal sliced Wasserstein distance selects climate year subsets that are 2.5-3.5 times more representative than ENTSO-E practice and achieve 4-5 times effective sample size.
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Generative Unsupervised Downscaling of Climate Models via Domain Alignment: Application to Wind Fields
SerpentFlow aligns large-scale wind patterns across GCM and observational domains then uses flow-matching to generate consistent fine-scale multivariate wind fields, outperforming standard bias correction in spatial coherence and robustness.