455 citations · 2.1k across the 56 of their papers we have counts for
10 papers · 1 filter
Benchmarking Regional Thermodynamic Trends in an AI emulator, ACE2, and a hybrid model, NeuralGCM
Katharine Rucker, Ian Baxter, Pedram Hassanzadeh +2
AI models have emerged as potential complements to physics-based models, but their skill in capturing observed regional climate trends with important societal impacts has not been…
Benchmarking atmospheric circulation variability in an AI emulator, ACE2, and a hybrid model, NeuralGCM
Ian Baxter, Hamid Pahlavan, Pedram Hassanzadeh +2
Physics-based atmosphere-land models with prescribed sea surface temperature have notable successes but also biases in their ability to represent atmospheric variability compared t…
Deep learning the sources of MJO predictability: a spectral view of learned features
Lin Yao, Da Yang, James P. C. Duncan +4
The Madden-Julian oscillation (MJO) is a planetary-scale, intraseasonal tropical rainfall phenomenon crucial for global weather and climate; however, its dynamics and predictabilit…
AI-boosted rare event sampling to characterize extreme weather
Amaury Lancelin, Alex Wikner, Laurent Dubus +5
Weather extremes pose major societal risks, especially in a changing climate, but due to their rarity, they are difficult to study using limited observations or complex climate mod…
Reframing Generative Models for Physical Systems using Stochastic Interpolants
Anthony Zhou, Alexander Wikner, Amaury Lancelin +2
Generative models have recently emerged as powerful surrogates for physical systems, demonstrating increased accuracy, stability, and/or statistical fidelity. Most approaches rely…
An Analytical and AI-discovered Stable, Accurate, and Generalizable Subgrid-scale Closure for Geophysical Turbulence
Karan Jakhar, Yifei Guan, Pedram Hassanzadeh
By combining AI and fluid physics, we discover a closed-form closure for 2D turbulence from small direct numerical simulation (DNS) data. Large-eddy simulation (LES) with this clos…
Hierarchical Implicit Neural Emulators
Ruoxi Jiang, Xiao Zhang, Karan Jakhar +4
Neural PDE solvers offer a powerful tool for modeling complex dynamical systems, but often struggle with error accumulation over long time horizons and maintaining stability and ph…
Predicting Beyond Training Data via Extrapolation versus Translocation: AI Weather Models and Dubai's Unprecedented 2024 Rainfall
Y. Qiang Sun, Pedram Hassanzadeh, Tiffany Shaw +1
Artificial intelligence (AI) models have transformed weather forecasting, but their skill for gray swan extremes is unclear. Here, we analyze GraphCast, AIFS, and FuXi forecasts of…
Fourier analysis of the physics of transfer learning for data-driven subgrid-scale models of ocean turbulence
Moein Darman, Pedram Hassanzadeh, Laure Zanna +1
Transfer learning (TL) is a powerful tool for enhancing the performance of neural networks (NNs) in applications such as weather and climate prediction and turbulence modeling. TL…
Semi-analytical eddy-viscosity and backscattering closures for 2D geophysical turbulence
Yifei Guan, Pedram Hassanzadeh
Physics-based eddy-viscosity and backscattering closures are widely used for large-eddy simulation (LES) of geophysical turbulence, but their key parameters are often chosen empiri…