Advancing Parsimonious Deep Learning Weather Prediction using the
  HEALPix Mesh
Matthias Karlbauer Nathaniel Cresswell-Clay Raul A. Moreno Dale R. Durran Thorsten Kurth Martin V. Butz
We present a parsimonious deep learning weather prediction model on the
Hierarchical Equal Area isoLatitude Pixelization (HEALPix) to forecast seven
atmospheric variables for arbitrarily long lead times on a global approximately
110 km mesh at 3h time resolution. In comparison to state-of-the-art machine
learning weather forecast models, such as Pangu-Weather and GraphCast, our
DLWP-HPX model uses coarser resolution and far fewer prognostic variables. Yet,
at one-week lead times its skill is only about one day behind the
state-of-the-art numerical weather prediction model from the European Centre
for Medium-Range Weather Forecasts. We report successive forecast improvements
resulting from model design and data-related decisions, such as switching from
the cubed sphere to the HEALPix mesh, inverting the channel depth of the U-Net,
and introducing gated recurrent units (GRU) on each level of the U-Net
hierarchy. The consistent east-west orientation of all cells on the HEALPix
mesh facilitates the development of location-invariant convolution kernels that
are successfully applied to propagate global weather patterns across our
planet. Without any loss of spectral power after two days, the model can be
unrolled autoregressively for hundreds of steps into the future to generate
stable and realistic states of the atmosphere that respect seasonal trends, as
showcased in one-year simulations. Our parsimonious DLWP-HPX model is
research-friendly and potentially well-suited for sub-seasonal and seasonal
forecasting.