Toward Replacing Current NWP with Deep Learning Weather Prediction and Extensions to a Full Earth-System Model

October 26, 2023

Dale Durran

Hosted by Libby Barnes

Abstract

We compare the performance of a global deep-learning weather-prediction (DLWP) model with
reanalysis data and forecasts from the European Center for Medium Range Weather Forecasts
(ECMWF).
The model is trained using ECMWF ReAnalysis 5 (ERA5) data with convolutional neural
networks (CNNs) on a HEALPix mesh using a loss function that minimizes forecast error over a
single 24-hour period. The model predicts seven 2D shells of atmospheric data on an equal-area
pixelization at resolutions of roughly 200 km.
Notably, our model can be iterated forward indefinitely to produce forecasts at 3-hour temporal
resolution for any lead time. We present case studies showing the extent to which the model is
able to reproduce the dynamical evolution of atmospheric systems and its ability to learn “model
physics” to forecast two-meter temperature and precipitation.
Extensions to a full earth-system model are presented using similar deep learning architecture to
forecast sea surface temperatures. The SST model can be stably stepped forward for a year and
shows skill in forecasting El Ninos.