ML-LES: Modeling cold-pool dynamics with graph-based neural network at hecto-meter grid-spacings

Machine learning–based weather prediction models have recently surpassed traditional numerical weather prediction systems on many skill metrics at regional and global scales, yet there is limited progress towards models operating on hectometric-scale resolutions. This setting is challenging both due to the cost of generating high-quality training data and the complex dynamics of important small-scale processes.

We introduce a graph neural network with Large-Eddy Simulation (LES) capabilities, to operate at hectometer horizontal resolution and sub-hourly time steps. Using 42 days of high-resolution realistic model output for the trade-wind regime over the western Atlantic, we train and evaluate the network on its ability to reproduce key mesoscale processes, with particular emphasis on cold-pool dynamics and convective triggering.

Cold pools are a crucial driver of low-level thermodynamic variability and cloudiness, and thus provide a stringent physical consistency test for models targeting hectometer scales, as they require accurate coupling between the cloud layer and the surface. Through a targeted ablation study, we quantify the relative importance of different input variables for reproducing surface temperature perturbations associated with cold pools, offering guidance for future parameterization and data selection strategies.

Finally, we show that the model can deterministically predict the evolution of cold pools over multiple successive generations, indicating that graph-based LES emulators can robustly capture the nonlinear feedbacks governing mesoscale organization in shallow convective regimes.