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.
The general circulation model (GCM) experiments conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) [Taylor et al., 2012], which is being conducted in preparation for the Intergovernmental Panel on Climate Change's Fifth Assessment Report, provide fundamental data sets for assessing the effects of global climate change. However, efforts to assess regional or local effects of the projected changes in climate are often impeded by the coarse spatial resolution of the GCM outputs, as well as potential local or regional biases in GCM outputs [Fowler et al., 2007].
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