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.
- Award ID(s):
- 1345052
- NSF-PAR ID:
- 10039222
- Date Published:
- Journal Name:
- Climate Informatics Workshop Proceedings
- Volume:
- NCAR Technical Notes: NCAR/TN-529+PROC
- Page Range / eLocation ID:
- 161 pp.
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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