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Climate change has led to an increase in frequency of extreme weather events.Early warning systems can prevent disasters and loss of life. Managing suchevents remain a challenge for both public and private institutions.Precipitation nowcasting can help relevant institutions to better prepare forsuch events. Numerical weather prediction (NWP) has traditionally been used tomake physics based forecasting, and recently deep learning based approacheshave been used to reduce turn-around time for nowcasting. In this work,recently proposed Swin-UNETR (Swin UNEt TRansformer) is used for precipitationnowcasting for ten different regions of Europe. Swin-UNETR utilizes a U-shapednetwork within which a swin transformer-based encoder extracts multi-scalefeatures from multiple input channels of satellite image, while CNN-baseddecoder makes the prediction. Trained model is capable of nowcasting not onlyfor the regions for which data is available, but can also be used for newregions for which data is not available.

Precipitation Nowcasting With Spatial And Temporal Transfer Learning Using Swin-UNETR