This example is an implementation of the coupled Ocean-Atmosphere DLWP model.

The goal is to train an AI model that can emulate the state of the atmosphere and predict global weather over a certain time span. The Deep Learning Weather Prediction (DLWP) model uses deep CNNs for globally gridded weather prediction. DLWP CNNs directly map u(t) to its future state u(t+Δt) by learning from historical observations of the weather, with Δt set to 6 hr. The Deep Learning Ocean Model (DLOM) that is designed to couple with deep learning weather prediction (DLWP) model. The DLOM forecasts sea surface temperature (SST). DLOMs use deep learning techniques as in DLWP models but are configured with different architectures and slower time stepping. DLOMs and DLWP models are trained to learn atmosphere-ocean coupling.

To train the coupled DLWP model, run

python train.py --config-name config_hpx32_coupled_dlwp

To train the coupled DLOM model, run

python train.py --config-name config_hpx32_coupled_dlom