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...

Бүрэн тодорхойлолт

Номзүйн дэлгэрэнгүй

Үндсэн зохиолчид:	Yannic Lops, Gemma J Anderson, Donald D Lucas, Indrasis Chakraborty, Daniel Galea
Формат:	Artigo
Хэл сонгох:	Inglês
Хэвлэсэн:	IOP Publishing 2025-01-01
Цуврал:	Machine Learning: Science and Technology
Нөхцлүүд:	uncertainty quantification probabilistic deep learning downscaling near-surface temperature intracell variability
Онлайн хандалт:	https://doi.org/10.1088/2632-2153/ae281e
Шошгууд:	Шошго нэмэх Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!