Evaluating probabilistic deep learning methods for uncertainty quantification of temperature downscaling

Deep learning (DL) has emerged as a promising tool for downscaling coarse-resolution climate data to high-resolution outputs, enabling improved regional climate predictions. A critical aspect of DL-based downscaling is the incorporation of uncertainty quantification (UQ), which enhances the interpre...

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Detalhes bibliográficos

Principais autores:	Yannic Lops, Gemma J Anderson, Donald D Lucas, Indrasis Chakraborty, Daniel Galea
Formato:	Artigo
Idioma:	Inglês
Publicado em:	IOP Publishing 2025-01-01
Colecção:	Machine Learning: Science and Technology
Assuntos:	uncertainty quantification probabilistic deep learning downscaling near-surface temperature intracell variability
Acesso em linha:	https://doi.org/10.1088/2632-2153/ae281e
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