2 papers
physics.ao-ph2025
Physically Interpretable Emulation of a Moist Convecting Atmosphere with a Recurrent Neural Network
Qiyu Song, Zhiming Kuang
Data-driven convective parameterization aims to accurately represent convective adjustments to large-scale forcings in a computationally economic manner. While previous studies hav…
physics.ao-ph2025
Stable Machine-Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection-Permitting Simulations
Zeyuan Hu, Akshay Subramaniam, Zhiming Kuang +6
Modern climate projections often suffer from inadequate spatial and temporal resolution due to computational limitations, resulting in inaccurate representations of sub-grid proces…