  High-resolution nowcasting is an essential tool needed for effective
adaptation to climate change, particularly for extreme weather. As Deep
Learning (DL) techniques have shown dramatic promise in many domains, including
the geosciences, we present an application of DL to the problem of
precipitation nowcasting, i.e., high-resolution (1 km x 1 km) short-term (1
hour) predictions of precipitation. We treat forecasting as an image-to-image
translation problem and leverage the power of the ubiquitous UNET convolutional
neural network. We find this performs favorably when compared to three commonly
used models: optical flow, persistence and NOAA's numerical one-hour HRRR
nowcasting prediction.
