42 citations · 58 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
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…
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.,…