  Designing early warning system for precipitation requires accurate short-term
forecasting system. Climate change has led to an increase in frequency of
extreme weather events, and hence such systems can prevent disasters and loss
of life. Managing such events remain a challenge for both public and private
institutions. Precipitation nowcasting can help relevant institutions to better
prepare for such events as they impact agriculture, transport, public health
and safety, etc. Physics-based numerical weather prediction (NWP) is unable to
perform well for nowcasting because of large computational turn-around time.
Deep-learning based models on the other hand are able to give predictions
within seconds. We use recently proposed NowcastNet, a physics-conditioned deep
generative network, to forecast precipitation for different regions of Europe
using satellite images. Both spatial and temporal transfer learning is done by
forecasting for the unseen regions and year. Model makes realistic predictions
and is able to outperform baseline for such a prediction task.
