  We present an ensemble prediction system using a Deep Learning Weather
Prediction (DLWP) model that recursively predicts key atmospheric variables
with six-hour time resolution. This model uses convolutional neural networks
(CNNs) on a cubed sphere grid to produce global forecasts. The approach is
computationally efficient, requiring just three minutes on a single GPU to
produce a 320-member set of six-week forecasts at 1.4{\deg} resolution.
Ensemble spread is primarily produced by randomizing the CNN training process
to create a set of 32 DLWP models with slightly different learned weights.
Although our DLWP model does not forecast precipitation, it does forecast total
column water vapor, and it gives a reasonable 4.5-day deterministic forecast of
Hurricane Irma. In addition to simulating mid-latitude weather systems, it
spontaneously generates tropical cyclones in a one-year free-running
simulation. Averaged globally and over a two-year test set, the ensemble mean
RMSE retains skill relative to climatology beyond two-weeks, with anomaly
correlation coefficients remaining above 0.6 through six days. Our primary
application is to subseasonal-to-seasonal (S2S) forecasting at lead times from
two to six weeks. Current forecast systems have low skill in predicting one- or
2-week-average weather patterns at S2S time scales. The continuous ranked
probability score (CRPS) and the ranked probability skill score (RPSS) show
that the DLWP ensemble is only modestly inferior in performance to the European
Centre for Medium Range Weather Forecasts (ECMWF) S2S ensemble over land at
lead times of 4 and 5-6 weeks. At shorter lead times, the ECMWF ensemble
performs better than DLWP.
