Historically trained ML weather emulators quantify fast precipitation changes from CO2 perturbations and produce results that agree with Earth System Models.
Spherical fourier neural operators: Learning stable dynamics on the sphere
6 Pith papers cite this work, alongside 53 external citations. Polarity classification is still indexing.
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PyMieDiff provides a new open-source PyTorch library for fully differentiable Mie scattering from layered spheres, with autograd support for efficiencies, angular patterns, and near-fields.
Training climate emulators on random-CO2 runs that break SST–CO2 correlation, plus an energy constraint, yields a data-efficient model that works on AMIP+4K and abrupt 4xCO2 cases prior ACE models mishandled.
WLNO augments LNO with a parallel Haar wavelet branch and learnable gate to capture multi-scale spatial features, outperforming LNO on five PDE benchmarks especially those with sharp structures.
SFNO surrogate matches or exceeds HUX on several solar-wind metrics while remaining trainable on additional data.
A standard U-Net with MAE pre-training plus short CRPS fine-tuning and MC Dropout matches GenCast and IFS ENS probabilistic skill at 1.5° while cutting training and inference cost by over 10×.
citing papers explorer
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Examining Fast Radiatively Driven Responses Using Machine-Learning Weather Emulators
Historically trained ML weather emulators quantify fast precipitation changes from CO2 perturbations and produce results that agree with Earth System Models.
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PyMieDiff: A differentiable Mie scattering library
PyMieDiff provides a new open-source PyTorch library for fully differentiable Mie scattering from layered spheres, with autograd support for efficiencies, angular patterns, and near-fields.
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Disentangling the effects of sea surface temperature and CO$_2$ in global machine learned weather-climate emulators
Training climate emulators on random-CO2 runs that break SST–CO2 correlation, plus an energy constraint, yields a data-efficient model that works on AMIP+4K and abrupt 4xCO2 cases prior ACE models mishandled.
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WLNO: Wavelet-Laplace Neural Operator for Solving Partial Differential Equations
WLNO augments LNO with a parallel Haar wavelet branch and learnable gate to capture multi-scale spatial features, outperforming LNO on five PDE benchmarks especially those with sharp structures.
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Toward Data-Driven Surrogates of the Solar Wind with Spherical Fourier Neural Operator
SFNO surrogate matches or exceeds HUX on several solar-wind metrics while remaining trainable on additional data.
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U-Cast: A Surprisingly Simple and Efficient Frontier Probabilistic AI Weather Forecaster
A standard U-Net with MAE pre-training plus short CRPS fine-tuning and MC Dropout matches GenCast and IFS ENS probabilistic skill at 1.5° while cutting training and inference cost by over 10×.