  The Weather4Cast competition (hosted by NeurIPS 2022) required competitors to
predict super-resolution rain movies in various regions of Europe when
low-resolution satellite contexts covering wider regions are given. In this
paper, we show that a general baseline 3D U-Net can be significantly improved
with region-conditioned layers as well as orthogonality regularizations on
1x1x1 convolutional layers. Additionally, we facilitate the generalization with
a bag of training strategies: mixup data augmentation, self-distillation, and
feature-wise linear modulation (FiLM). Presented modifications outperform the
baseline algorithms (3D U-Net) by up to 19.54% with less than 1% additional
parameters, which won the 4th place in the core test leaderboard.
