Sub-seasonal forecasting with a large ensemble of deep-learning weather
prediction models
Abstract
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° 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.