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

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dc.contributor.author Harilal, Nidhin
dc.contributor.author Singh, Mayank
dc.contributor.author Bhatia, Udit
dc.coverage.spatial United States of America
dc.date.accessioned 2021-02-17T05:10:05Z
dc.date.available 2021-02-17T05:10:05Z
dc.date.issued 2021-02
dc.identifier.citation Harilal, Nidhin; Singh, Mayank and Bhatia, Udit, “Augmented convolutional LSTMs for generation of high-resolution climate change projections”, IEEE Access, DOI: 10.1109/ACCESS.2021.3057500, vol. 9, pp. 25208-25218, Feb. 2021. en_US
dc.identifier.issn 2169-3536
dc.identifier.uri https://ieeexplore.ieee.org/document/9348885
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/6276
dc.description.abstract 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 rely on high-resolution projections (typically in the order of a few kilometers), state-of-the-art Earth System Models (ESMs) are available at spatial resolutions of few hundreds of kilometers. Current solutions to obtain high-resolution projections of ESMs include downscaling approaches that consider the information at a coarse-scale to make predictions at local scales. Complex and non-linear interdependence among local climate variables (e.g., temperature and precipitation) and large-scale predictors (e.g., pressure fields) motivate the use of neural network-based super-resolution architectures. In this work, we present auxiliary variables informed spatio-temporal neural architecture for statistical downscaling. The current study performs daily downscaling of precipitation variable from an ESM output at 1.15 degrees ( 115 km) to � degrees (25 km) over the one of the most climatically diversified countries, India. We showcase significant improvement gain against two popular state-of-the-art baselines with a better ability to predict statistics of extreme events. To facilitate reproducible research, we make available all the codes, processed datasets, and trained models in the public domain.
dc.description.statementofresponsibility by Nidhin Harilal, Mayank Singh and Udit Bhatia
dc.language.iso en_US en_US
dc.publisher Institute of Electrical and Electronics Engineers en_US
dc.subject Meteorology en_US
dc.subject Atmospheric modeling en_US
dc.subject Spatial resolution en_US
dc.subject Earth en_US
dc.subject Climate change en_US
dc.subject Biological system modeling en_US
dc.subject Adaptation models en_US
dc.title Augmented Convolutional LSTMs for Generation of High-Resolution Climate Change Projections en_US
dc.type Article en_US
dc.relation.journal IEEE Access


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