  This paper presents a deep learning architecture for nowcasting of
precipitation almost globally every 30 min with a 4-hour lead time. The
architecture fuses a U-Net and a convolutional long short-term memory (LSTM)
neural network and is trained using data from the Integrated MultisatellitE
Retrievals for GPM (IMERG) and a few key precipitation drivers from the Global
Forecast System (GFS). The impacts of different training loss functions,
including the mean-squared error (regression) and the focal-loss
(classification), on the quality of precipitation nowcasts are studied. The
results indicate that the regression network performs well in capturing light
precipitation (below 1.6 mm/hr), but the classification network can outperform
the regression network for nowcasting of precipitation extremes (>8 mm/hr), in
terms of the critical success index (CSI).. Using the Wasserstein distance, it
is shown that the predicted precipitation by the classification network has a
closer class probability distribution to the IMERG than the regression network.
It is uncovered that the inclusion of the physical variables can improve
precipitation nowcasting, especially at longer lead times in both networks.
Taking IMERG as a relative reference, a multi-scale analysis in terms of
fractions skill score (FSS), shows that the nowcasting machine remains skillful
(FSS > 0.5) at the resolution of 10 km compared to 50 km for GFS. For
precipitation rates greater than 4~mm/hr, only the classification network
remains FSS-skillful on scales greater than 50 km within a 2-hour lead time.
