  We introduce a transformer-based neural network to generate high-resolution
(3km) synthetic radar reflectivity fields at scale from geostationary satellite
imagery. This work aims to enhance short-term convective-scale forecasts of
high-impact weather events and aid in data assimilation for numerical weather
prediction over the United States. Compared to convolutional approaches, which
have limited receptive fields, our results show improved sharpness and higher
accuracy across various composite reflectivity thresholds. Additional case
studies over specific atmospheric phenomena support our quantitative findings,
while a novel attribution method is introduced to guide domain experts in
understanding model outputs.
