Abstract:We present a first application of a fast super resolution convolutional neural network (FSRCNN) approach for downscaling climate simulations. Unlike other SR approaches, FSRCNN uses the same input feature dimensions as the low resolution input. This allows it to have smaller convolution layers, avoiding over-smoothing, and reduced computational costs. We further adapt FSRCNN to feature additional convolution layers after the deconvolution layer, we term FSRCNN-ESM. We use highresolution (0.25°) monthly average… Show more
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