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Long-term stability and physical consistency are critical properties for AI-based weather models if they are going to be used for subseasonal-to-seasonal forecasts or beyond, e.g., climate change projection.
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Predictability of the loop current variation and eddy shedding process in the gulf of mexico using an artificial neural network approach
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The linear response function of an idealized atmosphere. part i: Construction using green’s functions and applications
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Size of the atmospheric blocking events: Scaling law and response to climate change
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On the spectral bias of neural networks
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Deep learning and process understanding for data-driven Earth system science
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Can machines learn to predict weather? using deep learning to predict gridded 500-hpa geopotential height from historical weather data
J. A. Weyn, D. R. Durran, and R. Caruana · 2019
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Frequency principle: Fourier analysis sheds light on deep neural networks
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Frequency bias in neural networks for input of non-uniform density
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Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere
J. A. Weyn, D. R. Durran, and R. Caruana · 2020
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A comparison of data-driven approaches to build low-dimensional ocean models
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Seasonal arctic sea ice forecasting with probabilistic deep learning
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Opportunities and challenges for machine learning in weather and climate modelling: hard, medium and soft ai
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Stabilizing machine learning prediction of dynamics: Noise and noise-inspired regularization
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On the frequency bias of generative models
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Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction models
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