RainAI -- Precipitation Nowcasting from Satellite Data
Rafael Pablos Sarabia Joachim Nyborg Morten Birk Ira Assent
This paper presents a solution to the Weather4Cast 2023 competition, where
the goal is to forecast high-resolution precipitation with an 8-hour lead time
using lower-resolution satellite radiance images. We propose a simple, yet
effective method for spatiotemporal feature learning using a 2D U-Net model,
that outperforms the official 3D U-Net baseline in both performance and
efficiency. We place emphasis on refining the dataset, through importance
sampling and dataset preparation, and show that such techniques have a
significant impact on performance. We further study an alternative
cross-entropy loss function that improves performance over the standard mean
squared error loss, while also enabling models to produce probabilistic
outputs. Additional techniques are explored regarding the generation of
predictions at different lead times, specifically through Conditioning Lead
Time. Lastly, to generate high-resolution forecasts, we evaluate standard and
learned upsampling methods. The code and trained parameters are available at
https://github.com/rafapablos/w4c23-rainai.