Fig. 11

The architecture of the conditioned U-Net for motion field estimation follows an encoder-decoder design augmented with skip connections. This module is jointly trained alongside the precipitation map generator, while also benefiting from an auxiliary unsupervised learning objective that enforces physics-based constraints on the estimated motion fields. By leveraging these implicit physical priors, the network eliminates the need for explicit ground-truth motion annotations during training.