# Broad-UNet: Multi-scale feature learning for nowcasting tasks

arXiv: https://arxiv.org/abs/2102.06442 · HTML: https://mlsanity.com/paper/2102.06442/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 49 references that are in the map.
- First posted on arXiv 12 Feb 2021.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Unsupervised Machine Translation Using Monolingual Corpora Only](https://mlsanity.com/paper/1711.00043/) (2017)
- [Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model](https://mlsanity.com/paper/1706.03458/) (2017)
- [SmaAt-UNet: Precipitation Nowcasting using a Small Attention-UNet Architecture](https://mlsanity.com/paper/2007.04417/) (2020)
- [Machine Learning for Precipitation Nowcasting from Radar Images](https://mlsanity.com/paper/1912.12132/) (2019)
- [Deep Learning for Time-Series Analysis](https://mlsanity.com/paper/1701.01887/) (2017)
- [Audio Word2Vec: Unsupervised Learning of Audio Segment Representations using Sequence-to-sequence Autoencoder](https://mlsanity.com/paper/1603.00982/) (2016)
- [Precipitation Nowcasting with Satellite Imagery](https://mlsanity.com/paper/1905.09932/) (2019)
- [Deep coastal sea elements forecasting using U-Net based models](https://mlsanity.com/paper/2011.03303/) (2020)
- [Cloud Cover Nowcasting with Deep Learning](https://mlsanity.com/paper/2009.11577/) (2020)

## Built on by (most cited first, 25 of 25)

- [Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey](https://mlsanity.com/paper/2312.03014/) (2023)
- [Precipitation nowcasting with generative diffusion models](https://mlsanity.com/paper/2308.06733/) (2023)
- [AA-TransUNet: Attention Augmented TransUNet For Nowcasting Tasks](https://mlsanity.com/paper/2202.04996/) (2022)
- [Symbolic regression for scientific discovery: an application to wind speed forecasting](https://mlsanity.com/paper/2102.10570/) (2021)
- [Deep learning for precipitation nowcasting: A survey from the perspective of time series forecasting](https://mlsanity.com/paper/2406.04867/) (2024)
- [Multistream Graph Attention Networks for Wind Speed Forecasting](https://mlsanity.com/paper/2108.07063/) (2021)
- [GA-SmaAt-GNet: Generative Adversarial Small Attention GNet for Extreme Precipitation Nowcasting](https://mlsanity.com/paper/2401.09881/) (2024)
- [TENT: Tensorized Encoder Transformer for Temperature Forecasting](https://mlsanity.com/paper/2106.14742/) (2021)
- [Graph Dual-stream Convolutional Attention Fusion for Precipitation Nowcasting](https://mlsanity.com/paper/2401.07958/) (2024)
- [SAR-UNet: Small Attention Residual UNet for Explainable Nowcasting Tasks](https://mlsanity.com/paper/2303.06663/) (2023)
- [MS-RNN: A Flexible Multi-Scale Framework for Spatiotemporal Predictive Learning](https://mlsanity.com/paper/2206.03010/) (2022)
- [MS-LSTM: Exploring Spatiotemporal Multiscale Representations in Video Prediction Domain](https://mlsanity.com/paper/2304.07724/) (2023)
- [WF-UNet: Weather Fusion UNet for Precipitation Nowcasting](https://mlsanity.com/paper/2302.04102/) (2023)
- [Evidential Deep Learning for Probabilistic Modelling of Extreme Storm Events](https://mlsanity.com/paper/2412.14048/) (2024)
- [SSA-UNet: Advanced Precipitation Nowcasting via Channel Shuffling](https://mlsanity.com/paper/2504.18309/) (2025)
- [RainPro-8: An Efficient Deep Learning Model to Estimate Rainfall Probabilities Over 8 Hours](https://mlsanity.com/paper/2505.10271/) (2025)
- [Probabilistic Precipitation Nowcasting with Rectified Flow Transformers](https://mlsanity.com/paper/2605.31204/) (2026)
- [RainAI -- Precipitation Nowcasting from Satellite Data](https://mlsanity.com/paper/2311.18398/) (2023)
- [Deep Vision in Analysis and Recognition of Radar Data: Achievements, Advancements and Challenges](https://mlsanity.com/paper/2302.09990/) (2023)
- [Self-supervised Spatial-Temporal Learner for Precipitation Nowcasting](https://mlsanity.com/paper/2412.15917/) (2024)
- [MAD-SmaAt-GNet: A Multimodal Advection-Guided Neural Network for Precipitation Nowcasting](https://mlsanity.com/paper/2603.04461/) (2026)
- [SmaAT-QMix-UNet: A Parameter-Efficient Vector-Quantized UNet for Precipitation Nowcasting](https://mlsanity.com/paper/2603.21879/) (2026)
- [A Diffusion-Contrastive Graph Neural Network with Virtual Nodes for Wind Nowcasting in Unobserved Regions](https://mlsanity.com/paper/2604.10328/) (2026)
- [Beyond MSE: Improving Precipitation Nowcasting with Multi-Quantile Regression](https://mlsanity.com/paper/2605.30122/) (2026)
- [Temporal Context Conditioning for Seasonality-Aware Precipitation Nowcasting of High-Intensity Rainfall](https://mlsanity.com/paper/2606.09959/) (2026)

## Abstract (arXiv)

Weather nowcasting consists of predicting meteorological components in the short term at high spatial resolutions. Due to its influence in many human activities, accurate nowcasting has recently gained plenty of attention. In this paper, we treat the nowcasting problem as an image-to-image translation problem using satellite imagery. We introduce Broad-UNet, a novel architecture based on the core UNet model, to efficiently address this problem. In particular, the proposed Broad-UNet is equipped with asymmetric parallel convolutions as well as Atrous Spatial Pyramid Pooling (ASPP) module. In this way, The the Broad-UNet model learns more complex patterns by combining multi-scale features while using fewer parameters than the core UNet model. The proposed model is applied on two different nowcasting tasks, i.e. precipitation maps and cloud cover nowcasting. The obtained numerical results show that the introduced Broad-UNet model performs more accurate predictions compared to the other examined architectures.