  Precipitation nowcasting, the high-resolution forecasting of precipitation up
to two hours ahead, supports the real-world socio-economic needs of many
sectors reliant on weather-dependent decision-making. State-of-the-art
operational nowcasting methods typically advect precipitation fields with
radar-based wind estimates, and struggle to capture important non-linear events
such as convective initiations. Recently introduced deep learning methods use
radar to directly predict future rain rates, free of physical constraints.
While they accurately predict low-intensity rainfall, their operational utility
is limited because their lack of constraints produces blurry nowcasts at longer
lead times, yielding poor performance on more rare medium-to-heavy rain events.
To address these challenges, we present a Deep Generative Model for the
probabilistic nowcasting of precipitation from radar. Our model produces
realistic and spatio-temporally consistent predictions over regions up to 1536
km x 1280 km and with lead times from 5-90 min ahead. In a systematic
evaluation by more than fifty expert forecasters from the Met Office, our
generative model ranked first for its accuracy and usefulness in 88% of cases
against two competitive methods, demonstrating its decision-making value and
ability to provide physical insight to real-world experts. When verified
quantitatively, these nowcasts are skillful without resorting to blurring. We
show that generative nowcasting can provide probabilistic predictions that
improve forecast value and support operational utility, and at resolutions and
lead times where alternative methods struggle.
