1 citations · 1 across the 2 of their papers we have counts for
2 papers
physics.ao-ph2024★ 1 cited
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…
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…