455 citations · 2.1k across the 56 of their papers we have counts for
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Extreme Event Prediction with Multi-agent Reinforcement Learning-based Parametrization of Atmospheric and Oceanic Turbulence
Rambod Mojgani, Daniel Waelchli, Yifei Guan +2
Global climate models (GCMs) are the main tools for understanding and predicting climate change. However, due to limited numerical resolutions, these models suffer from major struc…
Data Imbalance, Uncertainty Quantification, and Generalization via Transfer Learning in Data-driven Parameterizations: Lessons from the Emulation of Gravity Wave Momentum Transport in WACCM
Y. Qiang Sun, Hamid A. Pahlavan, Ashesh Chattopadhyay +6
Neural networks (NNs) are increasingly used for data-driven subgrid-scale parameterization in weather and climate models. While NNs are powerful tools for learning complex nonlinea…
Explainable Offline-Online Training of Neural Networks for Parameterizations: A 1D Gravity Wave-QBO Testbed in the Small-data Regime
Hamid A. Pahlavan, Pedram Hassanzadeh, M. Joan Alexander
There are different strategies for training neural networks (NNs) as subgrid-scale parameterizations. Here, we use a 1D model of the quasi-biennial oscillation (QBO) and gravity wa…
Interpretable structural model error discovery from sparse assimilation increments using spectral bias-reduced neural networks: A quasi-geostrophic turbulence test case
Rambod Mojgani, Ashesh Chattopadhyay, Pedram Hassanzadeh
Earth system models suffer from various structural and parametric errors in their representation of nonlinear, multi-scale processes, leading to uncertainties in their long-term pr…
Learning Closed-form Equations for Subgrid-scale Closures from High-fidelity Data: Promises and Challenges
Karan Jakhar, Yifei Guan, Rambod Mojgani +2
There is growing interest in discovering interpretable, closed-form equations for subgrid-scale (SGS) closures/parameterizations of complex processes in Earth systems. Here, we app…
Challenges of learning multi-scale dynamics with AI weather models: Implications for stability and one solution
Ashesh Chattopadhyay, Y. Qiang Sun, Pedram Hassanzadeh
Long-term stability and physical consistency are critical properties for AI-based weather models if they are going to be used for subseasonal-to-seasonal forecasts or beyond, e.g.,…
Redox evolution of the crystallizing terrestrial magma ocean and its influence on atmosphere outgassing
Maxime Maurice, Rajdeep Dasgupta, Pedram Hassanzadeh
Magma oceans are episodes of large-scale melting of the mantle of terrestrial planets. The energy delivered by the Moon-forming impact induced a deep magma ocean on the young Earth…
Using Neural Networks to Learn the Jet Stream Forced Response from Natural Variability
Charlotte Connolly, Elizabeth A. Barnes, Pedram Hassanzadeh +1
Two distinct features of anthropogenic climate change, warming in the tropical upper troposphere and warming at the Arctic surface, have competing effects on the mid-latitude jet s…