  We present a significantly-improved data-driven global weather forecasting
framework using a deep convolutional neural network (CNN) to forecast several
basic atmospheric variables on a global grid. New developments in this
framework include an offline volume-conservative mapping to a cubed-sphere
grid, improvements to the CNN architecture, and the minimization of the loss
function over multiple steps in a prediction sequence. The cubed-sphere
remapping minimizes the distortion on the cube faces on which convolution
operations are performed and provides natural boundary conditions for padding
in the CNN. Our improved model produces weather forecasts that are indefinitely
stable and produce realistic weather patterns at lead times of several weeks
and longer. For short- to medium-range forecasting, our model significantly
outperforms persistence, climatology, and a coarse-resolution dynamical
numerical weather prediction (NWP) model. Unsurprisingly, our forecasts are
worse than those from a high-resolution state-of-the-art operational NWP
system. Our data-driven model is able to learn to forecast complex surface
temperature patterns from few input atmospheric state variables. On annual time
scales, our model produces a realistic seasonal cycle driven solely by the
prescribed variation in top-of-atmosphere solar forcing. Although it is
currently less accurate than operational weather forecasting models, our
data-driven CNN executes much faster than those models, suggesting that machine
learning could prove to be a valuable tool for large-ensemble forecasting.
