# RainUNet for Super-Resolution Rain Movie Prediction under Spatio-temporal Shifts

arXiv: https://arxiv.org/abs/2212.04005 · HTML: https://mlsanity.com/paper/2212.04005/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 18 references that are in the map.
- First posted on arXiv 7 Dec 2022.

## Builds on (most cited first, 11 of 11)

- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Averaging Weights Leads to Wider Optima and Better Generalization](https://mlsanity.com/paper/1803.05407/) (2018)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations](https://mlsanity.com/paper/1707.03237/) (2017)
- [Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks](https://mlsanity.com/paper/1711.10305/) (2017)
- [Visual Attention Network](https://mlsanity.com/paper/2202.09741/) (2022)
- [SmaAt-UNet: Precipitation Nowcasting using a Small Attention-UNet Architecture](https://mlsanity.com/paper/2007.04417/) (2020)
- [Learning Spatio-Temporal Representation with Local and Global Diffusion](https://mlsanity.com/paper/1906.05571/) (2019)
- [MS-nowcasting: Operational Precipitation Nowcasting with Convolutional LSTMs at Microsoft Weather](https://mlsanity.com/paper/2111.09954/) (2021)
- [Optimization Planning for 3D ConvNets](https://mlsanity.com/paper/2201.04021/) (2022)

## Built on by (most cited first, 3 of 3)

- [PAUNet: Precipitation Attention-based U-Net for rain prediction from satellite radiance data](https://mlsanity.com/paper/2311.18306/) (2023)
- [Efficient Baseline for Quantitative Precipitation Forecasting in Weather4cast 2023](https://mlsanity.com/paper/2311.18806/) (2023)
- [A Space-Time Transformer for Precipitation Nowcasting](https://mlsanity.com/paper/2511.11090/) (2025)

## Abstract (arXiv)

This paper presents a solution to the Weather4cast 2022 Challenge Stage 2. The goal of the challenge is to forecast future high-resolution rainfall events obtained from ground radar using low-resolution multiband satellite images. We suggest a solution that performs data preprocessing appropriate to the challenge and then predicts rainfall movies using a novel RainUNet. RainUNet is a hierarchical U-shaped network with temporal-wise separable block (TS block) using a decoupled large kernel 3D convolution to improve the prediction performance. Various evaluation metrics show that our solution is effective compared to the baseline method. The source codes are available at https://github.com/jinyxp/Weather4cast-2022