Purely data-driven medium-range weather forecasting achieves comparable skill to physical models at similar resolution

Numerical weather prediction has traditionally been based on physical models of the atmosphere. Recently, however, the rise of deep learning has created increased interest in purely data-driven medium-range weather forecasting with first studies exploring the feasibility of such an approach. Here, we train a significantly larger model than in previous studies to predict geopotential, temperature and precipitation up to 5 days ahead and achieve comparable skill to a physical model run at similar horizontal resolution. Crucially, we pretrain our models on historical climate model output before fine-tuning them on the reanalysis data. We also analyze how the neural network creates its predictions and find that, with some exceptions, it is compatible with physical reasoning. Our results indicate that, given enough training data, data-driven models can compete with physical models. At the same time, there is likely not enough data to scale this approach to the resolutions of current operational models.