455 citations · 2.1k across the 56 of their papers we have counts for
6 papers · 1 filter
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…