# SmaAt-UNet: Precipitation Nowcasting using a Small Attention-UNet Architecture

arXiv: https://arxiv.org/abs/2007.04417 · HTML: https://mlsanity.com/paper/2007.04417/

## Key facts

- Cited by 71 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2020.

## Built on by (most cited first, 30 of 71)

- [Skillful Precipitation Nowcasting using Deep Generative Models of Radar](https://mlsanity.com/paper/2104.00954/) (2021)
- [Broad-UNet: Multi-scale feature learning for nowcasting tasks](https://mlsanity.com/paper/2102.06442/) (2021)
- [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)
- [Skillful Twelve Hour Precipitation Forecasts using Large Context Neural Networks](https://mlsanity.com/paper/2111.07470/) (2021)
- [Symbolic regression for scientific discovery: an application to wind speed forecasting](https://mlsanity.com/paper/2102.10570/) (2021)
- [Fourier Amplitude and Correlation Loss: Beyond Using L2 Loss for Skillful Precipitation Nowcasting](https://mlsanity.com/paper/2410.23159/) (2024)
- [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)
- [Deterministic Guidance Diffusion Model for Probabilistic Weather Forecasting](https://mlsanity.com/paper/2312.02819/) (2023)
- [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)
- [Extreme Precipitation Nowcasting using Transformer-based Generative Models](https://mlsanity.com/paper/2403.03929/) (2024)
- [Precipitation Nowcasting Using Diffusion Transformer with Causal Attention](https://mlsanity.com/paper/2410.13314/) (2024)
- [WaveC2R: Wavelet-Driven Coarse-to-Refined Hierarchical Learning for Radar Retrieval](https://mlsanity.com/paper/2511.17558/) (2025)
- [Application of belief functions to medical image segmentation: A review](https://mlsanity.com/paper/2205.01733/) (2022)
- [PCT-CycleGAN: Paired Complementary Temporal Cycle-Consistent Adversarial Networks for Radar-Based Precipitation Nowcasting](https://mlsanity.com/paper/2211.15046/) (2022)
- [FedUKD: Federated UNet Model with Knowledge Distillation for Land Use Classification from Satellite and Street Views](https://mlsanity.com/paper/2212.02196/) (2022)
- [Two-stage Rainfall-Forecasting Diffusion Model](https://mlsanity.com/paper/2402.12779/) (2024)
- [RainUNet for Super-Resolution Rain Movie Prediction under Spatio-temporal Shifts](https://mlsanity.com/paper/2212.04005/) (2022)
- [Inductive biases in deep learning models for weather prediction](https://mlsanity.com/paper/2304.04664/) (2023)
- [Extreme Precipitation Nowcasting using Multi-Task Latent Diffusion Models](https://mlsanity.com/paper/2410.14103/) (2024)
- [RAP-Net: Region Attention Predictive Network for Precipitation Nowcasting](https://mlsanity.com/paper/2110.01035/) (2021)
- [WF-UNet: Weather Fusion UNet for Precipitation Nowcasting](https://mlsanity.com/paper/2302.04102/) (2023)
- [RNDiff: Rainfall nowcasting with Condition Diffusion Model](https://mlsanity.com/paper/2402.13737/) (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)

## Abstract (arXiv)

Weather forecasting is dominated by numerical weather prediction that tries to model accurately the physical properties of the atmosphere. A downside of numerical weather prediction is that it is lacking the ability for short-term forecasts using the latest available information. By using a data-driven neural network approach we show that it is possible to produce an accurate precipitation nowcast. To this end, we propose SmaAt-UNet, an efficient convolutional neural networks-based on the well known UNet architecture equipped with attention modules and depthwise-separable convolutions. We evaluate our approaches on a real-life datasets using precipitation maps from the region of the Netherlands and binary images of cloud coverage of France. The experimental results show that in terms of prediction performance, the proposed model is comparable to other examined models while only using a quarter of the trainable parameters.