Machine learning for climate physics and simulations
We discuss the emerging advances and opportunities at the intersection of machine
learning (ML) and climate physics, highlighting the use of ML techniques, including …
learning (ML) and climate physics, highlighting the use of ML techniques, including …
Huge ensembles part i: Design of ensemble weather forecasts using spherical fourier neural operators
Studying low-likelihood high-impact extreme weather events in a warming world is a
significant and challenging task for current ensemble forecasting systems. While these …
significant and challenging task for current ensemble forecasting systems. While these …
Interpretable structural model error discovery from sparse assimilation increments using spectral bias‐reduced neural networks: A quasi‐geostrophic turbulence test …
Earth system models suffer from various structural and parametric errors in their
representation of nonlinear, multi‐scale processes, leading to uncertainties in their long …
representation of nonlinear, multi‐scale processes, leading to uncertainties in their long …
Improving global weather and ocean wave forecast with large artificial intelligence models
The rapid advancement of artificial intelligence technologies, particularly in recent years,
has led to the emergence of several large parameter artificial intelligence weather forecast …
has led to the emergence of several large parameter artificial intelligence weather forecast …