General Circulation Models
Introduction
General Circulation Models (GCMs) are a class of models which use a combination of numerical solvers and tuned representations for small scale processes.
Neural GCM
Neural GCM is a GCM which uses a neural network to represent the small scale processes. It is competitive with ML models on 10 days forecasts, and competitive with IFS on 15 days forecasts.
Uses a fully differentiable hybrid GCM of the atmosphere, with a model split into two main subcomponents:
- A Differentiable Dynamical Core (DDC) which solves the equations of motion (dynamic equations);
- A Learned Physics module, which learns to parametrize a set of physical processes (physics equations) with a neural network.
End-to-end training of GCMs
Uses extended backpropagation between the DDC and the Learned Physics module.
Three loss functions:
- MSE for accuracy: Takes into account the layer lead time over the forecast horizon. Double penalty problem: wrong features at long lead times are penalized more than wrong features at short lead times.
- Squared Loss: Encourages spectrum to match the data.
- MSE for bias: Batch average mean amplitude of the bias.
Trained on three days rollout data. Remained stable for year-long simulations.
Stochastic GCM
Introduces randomness to be able to produce ensambles of forecasts.
Loss is CRPS (Continuous Ranked Probability Score) = Mean absolute error + Variance in ensamble spread