Improving data-driven global weather prediction using deep convolutional
neural networks on a cubed sphere
Abstract
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
currently does not compete with 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.