  Climate change has led to an increase in frequency of extreme weather events.
Early warning 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. Numerical weather prediction (NWP) has traditionally been used to
make physics based forecasting, and recently deep learning based approaches
have been used to reduce turn-around time for nowcasting. In this work,
recently proposed Swin-UNETR (Swin UNEt TRansformer) is used for precipitation
nowcasting for ten different regions of Europe. Swin-UNETR utilizes a U-shaped
network within which a swin transformer-based encoder extracts multi-scale
features from multiple input channels of satellite image, while CNN-based
decoder makes the prediction. Trained model is capable of nowcasting not only
for the regions for which data is available, but can also be used for new
regions for which data is not available.
