  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. To accelerate
progress in this area, the WeatherBench benchmark challenge was defined. Here,
we train a deep residual convolutional neural network (Resnet) to predict
geopotential, temperature and precipitation at 5.625 degree resolution up to 5
days ahead. To avoid overfitting and improve forecast skill, we pretrain the
model using historical climate model output before fine-tuning on reanalysis
data. The resulting forecasts outperform previous submissions to WeatherBench
and are comparable in skill to a physical baseline at similar resolution. We
also analyze how the neural network creates its predictions and find that, with
some exceptions, it is compatible with physical reasoning. Finally, we perform
scaling experiments to estimate the potential skill of data-driven approaches
at higher resolutions.
