Augmented Convolutional LSTMs for Generation of High-Resolution Climate
Change Projections
- URL: http://arxiv.org/abs/2009.11279v1
- Date: Wed, 23 Sep 2020 17:52:09 GMT
- Title: Augmented Convolutional LSTMs for Generation of High-Resolution Climate
Change Projections
- Authors: Nidhin Harilal, Udit Bhatia, Mayank Singh
- Abstract summary: We present auxiliary informed-temporal neural architecture for statistical downscaling.
Current study performs daily downscaling of precipitation variable from an ESM output at 1.15 degrees (115 km) to 0.25 degrees (25 km) over the world's most climatically diversified country, India.
- Score: 1.7503398807380832
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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 0.25 degrees (25 km) over the world's most
climatically diversified country, India. We showcase significant improvement
gain against three popular state-of-the-art baselines with a better ability to
predict extreme events. To facilitate reproducible research, we make available
all the codes, processed datasets, and trained models in the public domain.
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