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Climate Downscaling: A Deep-Learning Based Super-resolution Model of Precipitation Data with Attention Block and Skip Connections
March 27, 2024, 4:42 a.m. | Chia-Hao Chiang, Zheng-Han Huang, Liwen Liu, Hsin-Chien Liang, Yi-Chi Wang, Wan-Ling Tseng, Chao Wang, Che-Ta Chen, Ko-Chih Wang
cs.LG updates on arXiv.org arxiv.org
Abstract: Human activities accelerate consumption of fossil fuels and produce greenhouse gases, resulting in urgent issues today: global warming and the climate change. These indirectly cause severe natural disasters, plenty of lives suffering and huge losses of agricultural properties. To mitigate impacts on our lands, scientists are developing renewable, reusable, and clean energies and climatologists are trying to predict the extremes. Meanwhile, governments are publicizing resource-saving policies for a more eco-friendly society and arousing environment awareness. …
abstract arxiv attention block change climate climate change consumption cs.ai cs.lg data fossil fuels global global warming greenhouse greenhouse gases human losses natural natural disasters precipitation resolution type
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