Probabilistic Predictions from Deterministic Atmospheric River Forecasts with Deep Learning

In this paper, Will Chapman uses a deep learning (DL) based post-processing framework to generate probabilistic forecasts of atmospheric rivers from single member numerical weather prediction (NWP) forecasts. They show that the DL forecasts are reliable and sharp, and compete well with dynamically generated NWP ensembles.