Title: DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution,
Abstract: 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. Depending on statistical
modeling choices, downscaled projections have been shown to vary significantly
terms of accuracy and reliability. 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 for
statistical downscaling of climate variables. DeepSD augments SRCNN with
multi-scale input channels to maximize predictability in statistical
downscaling. We provide a comparison with Bias Correction Spatial
Disaggregation as well as three Automated-Statistical Downscaling approaches in
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.