# A Variational U-Net for Weather Forecasting

arXiv: https://arxiv.org/abs/2111.03476 · HTML: https://mlsanity.com/paper/2111.03476/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 12 references that are in the map.
- First posted on arXiv 5 Nov 2021.

## Builds on (most cited first, 9 of 9)

- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [3D MRI brain tumor segmentation using autoencoder regularization](https://mlsanity.com/paper/1810.11654/) (2018)
- [A Probabilistic U-Net for Segmentation of Ambiguous Images](https://mlsanity.com/paper/1806.05034/) (2018)
- [Efficient Object Localization Using Convolutional Networks](https://mlsanity.com/paper/1411.4280/) (2014)
- [Utilizing UNet for the future traffic map prediction task Traffic4cast challenge 2020](https://mlsanity.com/paper/2012.00125/) (2020)
- [Traffic4cast-Traffic Map Movie Forecasting -- Team MIE-Lab](https://mlsanity.com/paper/1910.13824/) (2019)
- [Traffic4cast 2020 -- Graph Ensemble Net and the Importance of Feature And Loss Function Design for Traffic Prediction](https://mlsanity.com/paper/2012.02115/) (2020)

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

- [Improvements to short-term weather prediction with recurrent-convolutional networks](https://mlsanity.com/paper/2111.06240/) (2021)
- [VITA: Variational Pretraining of Transformers for Climate-Robust Crop Yield Forecasting](https://mlsanity.com/paper/2508.03589/) (2025)
- [A Space-Time Transformer for Precipitation Nowcasting](https://mlsanity.com/paper/2511.11090/) (2025)

## Abstract (arXiv)

Not only can discovering patterns and insights from atmospheric data enable more accurate weather predictions, but it may also provide valuable information to help tackle climate change. Weather4cast is an open competition that aims to evaluate machine learning algorithms' capability to predict future atmospheric states. Here, we describe our third-place solution to Weather4cast. We present a novel Variational U-Net that combines a Variational Autoencoder's ability to consider the probabilistic nature of data with a U-Net's ability to recover fine-grained details. This solution is an evolution from our fourth-place solution to Traffic4cast 2020 with many commonalities, suggesting its applicability to vastly different domains, such as weather and traffic.