2017/03/08 by Thomas Vandal, Vandal, Thomas, Evan Kodra +10 · 11 citations
Computer Science · Earth and Planetary Sciences · Environmental Science · #Advanced Image Processing Techniques #Climate variability and models #Computer Vision and Pattern Recognition (cs.CV) #Cryospheric studies and observations #FOS: Computer and information sciences #Meteorological Phenomena and Simulations #cs.CV
paper · pdf · doi:10.48550/arxiv.1703.03126
9 pages, 5 Figures, 2 Tables
arxiv created 2017/03/09 · openalex publication_date 2017/03/09 · arxiv updated 2017/03/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.