Augmented Convolutional LSTMs for Generation of High-Resolution Climate Change Projections

Augmented Convolutional LSTMs for Generation of High-Resolution Climate Change Projections

Projection of changes in extreme indices of climate variables such as temperature and precipitation are critical to assess the potential impacts of climate change on human-made and natural systems, including critical infrastructures and ecosystems. While impact assessment and adaptation planning rel...

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Bibliographic Details
Main Authors: Nidhin Harilal, Mayank Singh, Udit Bhatia
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Climate statistical downscaling
deep learning
daily precipitation
super-resolution
LSTMs
recurrent neural networks
Online Access:https://ieeexplore.ieee.org/document/9348885/

Internet

https://ieeexplore.ieee.org/document/9348885/

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