455 citations · 2.1k across the 56 of their papers we have counts for
59 papers
AI Emulation of Stochastic Sudden Stratospheric Warming with Interpretable Latent Structure
C. Daniel Boscu, Daniel Hernandez, Fabio Alvarez Ventura +4
Rare weather regime transitions pose a challenge for data-driven modeling due to class imbalance. In this study, we develop a probabilistic deep learning emulator for a prototypica…
Can we create a `race to the top' for weather forecasts to inform smallholder farmer decisions?
Colin Aitken, Michael K. Tippett, Pedram Hassanzadeh +9
Artificial-intelligence weather prediction (AIWP) models have made it possible to produce high-quality tailored forecasts with limited computational resources. This advance has the…
Missing the Butterfly and Predicting the Past: Features or Bugs of Accurate AI Weather Models?
Pedram Hassanzadeh, Weidong Li, Y. Qiang Sun +4
AI weather prediction (AIWP) models rival physics-based models, yet the sources of their unexpected forecast accuracy and the degree of their physical fidelity remain unclear. Here…
Eigenanalysis framework for autoregressive neural emulators of multi-scale chaotic dynamics
Conrad Ainslie, Pedram Hassanzadeh, Michael W. Mahoney +1
Neural autoregressive models have rapidly emerged as powerful emulators of high-dimensional chaotic systems, yet their long-term instability and error growth remain poorly understo…
Rigorous uncertainty quantification of probabilistic AI weather forecasts with conformal prediction
Anna Asch, Raphael Rossellini, Pedram Hassanzadeh +1
Probabilistic weather forecasting is undergoing rapid transformation with artificial intelligence (AI). In traditional numerical weather prediction, computing power can limit how w…
Designing probabilistic AI monsoon forecasts to inform agricultural decision-making
Colin Aitken, Rajat Masiwal, Adam Marchakitus +7
Hundreds of millions of farmers make high-stakes decisions under uncertainty about future weather. Forecasts can inform these decisions, but available choices and their risks and b…
Prediction of Extreme Events in Multiscale Simulations of Geophysical Turbulence using Reinforcement Learning
Yifei Guan, Lucas Amoudruz, Sergey Litvinov +4
Accurate subgrid-scale closures are essential for weather/climate models, where predicting extreme events is critical. Traditional closures have structural errors, e.g., producing…
Decision-oriented benchmarking to transform AI weather forecast access: Application to the Indian monsoon
Rajat Masiwal, Colin Aitken, Adam Marchakitus +9
Artificial intelligence weather prediction (AIWP) models now often outperform traditional physics-based models on common metrics while requiring orders-of-magnitude less computing…
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…
Can AI weather models predict out-of-distribution gray swan tropical cyclones?
Y. Qiang Sun, Pedram Hassanzadeh, Mohsen Zand +3
Predicting gray swan weather extremes, which are possible but so rare that they are absent from the training dataset, is a major concern for AI weather models and long-term climate…
Online learning of eddy-viscosity and backscattering closures for geophysical turbulence using ensemble Kalman inversion
Yifei Guan, Pedram Hassanzadeh, Tapio Schneider +4
Different approaches to using data-driven methods for subgrid-scale closure modeling have emerged recently. Most of these approaches are data-hungry, and lack interpretability and…
Machine Learning for the Physics of Climate
Annalisa Bracco, Julien Brajard, Henk A. Dijkstra +3
An exponential growth in computing power, which has brought more sophisticated and higher resolution simulations of the climate system, and an exponential increase in observations…
On the importance of learning non-local dynamics for stable data-driven climate modeling: A 1D gravity wave-QBO testbed
Hamid A. Pahlavan, Pedram Hassanzadeh, M. Joan Alexander
Machine learning (ML) techniques, especially neural networks (NNs), have shown promise in learning subgrid-scale parameterizations for climate models. However, a major problem with…
Volatile atmospheres of lava worlds
Maxime Maurice, Rajdeep Dasgupta, Pedram Hassanzadeh
A magma ocean (MO) is thought to be a ubiquitous stage in the early evolution of rocky planets and exoplanets. During the lifetime of the MO, exchanges between the interior and ext…
Machine learning for climate physics and simulations
Ching-Yao Lai, Pedram Hassanzadeh, Aditi Sheshadri +3
We discuss the emerging advances and opportunities at the intersection of machine learning (ML) and climate physics, highlighting the use of ML techniques, including supervised, un…