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Designing early warning system for precipitation requires accurate short-termforecasting system. Climate change has led to an increase in frequency ofextreme weather events, and hence such systems can prevent disasters and lossof life. Managing such events remain a challenge for both public and privateinstitutions. Precipitation nowcasting can help relevant institutions to betterprepare for such events as they impact agriculture, transport, public healthand safety, etc. Physics-based numerical weather prediction (NWP) is unable toperform well for nowcasting because of large computational turn-around time.Deep-learning based models on the other hand are able to give predictionswithin seconds. We use recently proposed NowcastNet, a physics-conditioned deepgenerative network, to forecast precipitation for different regions of Europeusing satellite images. Both spatial and temporal transfer learning is done byforecasting for the unseen regions and year. Model makes realistic predictionsand is able to outperform baseline for such a prediction task.

Skilful Precipitation Nowcasting Using NowcastNet