# Integrating Weather Station Data and Radar for Precipitation Nowcasting: SmaAt-fUsion and SmaAt-Krige-GNet

arXiv: https://arxiv.org/abs/2502.16116 · HTML: https://mlsanity.com/paper/2502.16116/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 58 references that are in the map.
- First posted on arXiv 22 Feb 2025.

## Builds on (most cited first, 14 of 14)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Skillful Precipitation Nowcasting using Deep Generative Models of Radar](https://mlsanity.com/paper/2104.00954/) (2021)
- [Deep Learning for Day Forecasts from Sparse Observations](https://mlsanity.com/paper/2306.06079/) (2023)
- [SmaAt-UNet: Precipitation Nowcasting using a Small Attention-UNet Architecture](https://mlsanity.com/paper/2007.04417/) (2020)
- [Spatio-temporal graph neural networks for multi-site PV power forecasting](https://mlsanity.com/paper/2107.13875/) (2021)
- [Deep Graph Convolutional Networks for Wind Speed Prediction](https://mlsanity.com/paper/2101.10041/) (2021)
- [Wind speed prediction using multidimensional convolutional neural networks](https://mlsanity.com/paper/2007.12567/) (2020)
- [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)
- [Thunderstorm nowcasting with deep learning: a multi-hazard data fusion model](https://mlsanity.com/paper/2211.01001/) (2022)
- [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)

## Abstract (arXiv)

Short-term precipitation nowcasting is essential for flood management, transportation, energy system operations, and emergency response. However, many existing models fail to fully exploit the extensive atmospheric information available, relying primarily on precipitation data alone. This study examines whether integrating multi variable weather-station measurements with radar can enhance nowcasting skill and introduces two complementary architectures that integrate multi variable station data with radar images. The SmaAt-fUsion model extends the SmaAt-UNet framework by incorporating weather station data through a convolutional layer, integrating it into the bottleneck of the network; The SmaAt-Krige-GNet model combines precipitation maps with weather station data processed using Kriging, a geo-statistical interpolation method, to generate variable-specific maps. These maps are then utilized in a dual-encoder architecture based on SmaAt-GNet, allowing multi-level data integration. Experimental evaluations were conducted using four years (2016--2019) of weather station and precipitation radar data from the Netherlands. Results demonstrate that SmaAt-Krige-GNet outperforms the standard SmaAt-UNet, which relies solely on precipitation radar data, in low precipitation scenarios, while SmaAt-fUsion surpasses SmaAt-UNet in both low and high precipitation scenarios. This highlights the potential of incorporating discrete weather station data to enhance the performance of deep learning-based weather nowcasting models.