A review of recent and emerging machine learning applications for climate variability and weather phenomena
Climate variability and weather phenomena can cause extremes and pose significant risk to
society and ecosystems, making continued advances in our physical understanding of such …
society and ecosystems, making continued advances in our physical understanding of such …
Enhancing Regional Climate Downscaling through Advances in Machine Learning
Despite the sophistication of global climate models (GCMs), their coarse spatial resolution
limits their ability to resolve important aspects of climate variability and change at the local …
limits their ability to resolve important aspects of climate variability and change at the local …
Emulating aerosol optics with randomly generated neural networks
Atmospheric aerosols have a substantial impact on climate and remain one of the largest
sources of uncertainty in climate prediction. Accurate representation of their direct radiative …
sources of uncertainty in climate prediction. Accurate representation of their direct radiative …
[HTML][HTML] Downscaling atmospheric chemistry simulations with physically consistent deep learning
Recent advances in deep convolutional neural network (CNN)-based super resolution can
be used to downscale atmospheric chemistry simulations with substantially higher accuracy …
be used to downscale atmospheric chemistry simulations with substantially higher accuracy …
Strictly enforcing invertibility and conservation in CNN-based super resolution for scientific datasets
Recently, deep convolutional neural networks (CNNs) have revolutionized image “super
resolution”(SR), dramatically outperforming past methods for enhancing image resolution …
resolution”(SR), dramatically outperforming past methods for enhancing image resolution …
Statistical Treatment of Convolutional Neural Network Superresolution of Inland Surface Wind for Subgrid-Scale Variability Quantification
Abstract Machine learning models have been employed to perform either physics-free data-
driven or hybrid dynamical downscaling of climate data. Most of these implementations …
driven or hybrid dynamical downscaling of climate data. Most of these implementations …