3 papers
physics.ao-ph2025
Earth's Infrared Background: A Physics-Based Null Hypothesis for the Global-Scale Subannual Variability of Outgoing Longwave Radiation
Ofer Shamir, Edwin P. Gerber
Much of the Outgoing Longwave Radiation (OLR) emitted to space can be described as a noisy "background" of random variability. A rigorous characterization of this background provid…
physics.ao-ph2024
Overcoming set imbalance in data driven parameterization: A case study of gravity wave momentum transport
L. Minah Yang, Edwin P. Gerber
Machine learning for the parameterization of subgrid-scale processes in climate models has been widely researched and adopted in a few models. A key challenge in developing data-dr…
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…