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Title: Generating High Resolution Climate Change Projections through Single Image Super-Resolution: An Abridged Version

The impacts of climate change are felt by most critical systems, such as infrastructure, ecological systems, and power-plants. However, contemporary Earth System Models (ESM) are run at spatial resolutions too coarse for assessing effects this localized. Local scale projections can be obtained using statistical downscaling, a technique which uses historical climate observations to learn a low-resolution to high-resolution mapping. The spatio-temporal nature of the climate system motivates the adaptation of super-resolution image processing techniques to statistical downscaling. In our work, we present DeepSD, a generalized stacked super resolution convolutional neural network (SRCNN) framework with multi-scale input channels for statistical downscaling of climate variables. A comparison of DeepSD to four state-of-the-art methods downscaling daily precipitation from 1 degree (~100km) to 1/8 degrees (~12.5km) over the Continental United States. Furthermore, a framework using the NASA Earth Exchange (NEX) platform is discussed for downscaling more than 20 ESM models with multiple emission scenarios.

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Award ID(s):
1735505
NSF-PAR ID:
10111432
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
International Joint Conferences on Artificial Intelligence Organization
Page Range / eLocation ID:
5389 to 5393
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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