Machine Learning for Precipitation Nowcasting from Radar Images
This work addresses short-term weather prediction for climate adaptation, but it is incremental as it uses an existing method on a specific domain.
The authors tackled precipitation nowcasting by applying a UNET convolutional neural network to radar images, achieving favorable performance compared to optical flow, persistence, and NOAA's HRRR model.
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.