  The short-term prediction of precipitation is critical in many areas of life.
Recently, a large body of work was devoted to forecasting radar reflectivity
images. The radar images are available only in areas with ground weather
radars. Thus, we aim to predict high-resolution precipitation from
lower-resolution satellite radiance images. A neural network called
WeatherFusionNet is employed to predict severe rain up to eight hours in
advance. WeatherFusionNet is a U-Net architecture that fuses three different
ways to process the satellite data; predicting future satellite frames,
extracting rain information from the current frames, and using the input
sequence directly. Using the presented method, we achieved 1st place in the
NeurIPS 2022 Weather4Cast Core challenge. The code and trained parameters are
available at \url{https://github.com/Datalab-FIT-CTU/weather4cast-2022}.
