42 citations · 58 across the 5 of their papers we have counts for
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physics.ao-ph2023
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…
physics.flu-dyn2023★ 15 cited
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.,…