DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution

By T. Vandal, Evan Kodra, Sangram Ganguly, A. Michaelis, R. Nemani, Auroop Ratan Ganguly. Knowledge Discovery and Data Mining, 2017

373 citations recorded by the source corpus.

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.

Subfields: Atmospheric Science, Computer Vision and Pattern Recognition, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/71b9e91d1cf0c3c388194d3f76a8e19bb5cf93c2

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Citations in the corpus

Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems (2020, background): “Recently, artificial neural networks have shown a lot of promise for this problem, given their ability to model non-linear relationships [249, 273].”
Machine Learning for the Geosciences: Challenges and Opportunities (2017, background): “local scales [92], and classifying objects such as trees and buildings in high-resolution satellite images [93].”
ClimaX: A foundation model for weather and climate (2023, neutral): “We compare ClimaX with Cli-ViT and the two CNN baselines, UNet and ResNet, as most recent deep downscaling methods (Vandal et al., 2017; Rodrigues et al., 2018; H¨ohlein et al., 2020; Vandal et al., 2019; Liu et al., 2020) are based on convolution.”
A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists (2017, background): “Vandal et al. (2017) proposed a generalized stacked superresolution CNN framework for statistical downscaling of climate variables.”