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
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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.
SFNO surrogate matches or exceeds HUX on several solar-wind metrics while remaining trainable on additional data.
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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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.
- U-Cast: A Surprisingly Simple and Efficient Frontier Probabilistic AI Weather Forecaster