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Statistical treatment of convolutional neural network super-resolution of inland surface wind for subgrid-scale variability quantification

by   Daniel Getter, et al.

Machine learning models are frequently employed to perform either purely physics-free or hybrid downscaling of climate data. However, the majority of these implementations operate over relatively small downscaling factors of about 4–6x. This study examines the ability of convolutional neural networks (CNN) to downscale surface wind speed data from three different coarse resolutions (25km, 48km, and 100km side-length grid cells) to 3km and additionally focuses on the ability to recover subgrid-scale variability. Within each downscaling factor, namely 8x, 16x, and 32x, we consider models that produce fine-scale wind speed predictions as functions of different input features: coarse wind fields only; coarse wind and fine-scale topography; and coarse wind, topography, and temporal information in the form of a timestamp. Furthermore, we train one model at 25km to 3km resolution whose fine-scale outputs are probability density function parameters through which sample wind speeds can be generated. All CNN predictions performed on one out-of-sample data outperform classical interpolation. Models with coarse wind and fine topography are shown to exhibit the best performance compared to other models operating across the same downscaling factor. Our timestamp encoding results in lower out-of-sample generalizability compared to other input configurations. Overall, the downscaling factor plays the largest role in model performance.


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