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MAD-SmaAt-GNet: A Multimodal Advection-Guided Neural Network for Precipitation Nowcasting

Samuel van Wonderen, Siamak Mehrkanoon

TL;DR

This paper introduces the Multimodal Advection-Guided Small Attention GNet (MAD-SmaAt-GNet), which extends the core SmaAt-UNet by incorporating an additional encoder to learn from multiple weather variables and integrating a physics-based advection component to ensure physically consistent predictions.

Abstract

Precipitation nowcasting (short-term forecasting) is still often performed using numerical solvers for physical equations, which are computationally expensive and make limited use of the large volumes of available weather data. Deep learning models have shown strong potential for precipitation nowcasting, offering both accuracy and computational efficiency. Among these models, convolutional neural networks (CNNs) are particularly effective for image-to-image prediction tasks. The SmaAt-UNet is a lightweight CNN based architecture that has demonstrated strong performance for precipitation nowcasting. This paper introduces the Multimodal Advection-Guided Small Attention GNet (MAD-SmaAt-GNet), which extends the core SmaAt-UNet by (i) incorporating an additional encoder to learn from multiple weather variables and (ii) integrating a physics-based advection component to ensure physically consistent predictions. We show that each extension individually improves rainfall forecasts and that their combination yields further gains. MAD-SmaAt-GNet reduces the mean squared error (MSE) by 8.9% compared with the baseline SmaAt-UNet for four-step precipitation forecasting up to four hours ahead. Additionally, experiments indicate that multimodal inputs are particularly beneficial for short lead times, while the advection-based component enhances performance across both short and long forecasting horizons.

MAD-SmaAt-GNet: A Multimodal Advection-Guided Neural Network for Precipitation Nowcasting

TL;DR

This paper introduces the Multimodal Advection-Guided Small Attention GNet (MAD-SmaAt-GNet), which extends the core SmaAt-UNet by incorporating an additional encoder to learn from multiple weather variables and integrating a physics-based advection component to ensure physically consistent predictions.

Abstract

Precipitation nowcasting (short-term forecasting) is still often performed using numerical solvers for physical equations, which are computationally expensive and make limited use of the large volumes of available weather data. Deep learning models have shown strong potential for precipitation nowcasting, offering both accuracy and computational efficiency. Among these models, convolutional neural networks (CNNs) are particularly effective for image-to-image prediction tasks. The SmaAt-UNet is a lightweight CNN based architecture that has demonstrated strong performance for precipitation nowcasting. This paper introduces the Multimodal Advection-Guided Small Attention GNet (MAD-SmaAt-GNet), which extends the core SmaAt-UNet by (i) incorporating an additional encoder to learn from multiple weather variables and (ii) integrating a physics-based advection component to ensure physically consistent predictions. We show that each extension individually improves rainfall forecasts and that their combination yields further gains. MAD-SmaAt-GNet reduces the mean squared error (MSE) by 8.9% compared with the baseline SmaAt-UNet for four-step precipitation forecasting up to four hours ahead. Additionally, experiments indicate that multimodal inputs are particularly beneficial for short lead times, while the advection-based component enhances performance across both short and long forecasting horizons.
Paper Structure (12 sections, 7 equations, 5 figures, 3 tables)

This paper contains 12 sections, 7 equations, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related works
  3. Methods
  4. Proposed model: MAD-SmaAt-GNet
  5. Benchmark model, ablation study, and parameters
  6. Training
  7. Quality metrics and model evaluation
  8. Experiments
  9. Results and discussion
  10. Conclusions
  11. Acknowledgements
  12. Code and data availability

Figures (5)

  • Figure 1: The architecture of (a) MAD-SmaAt-GNet when given $4$ rain images and corresponding images of other weather variables and a target of $4$ output images (best viewed in colour). Additionally, the architecture of (b) the evolution network with the same rain inputs. Each bar represents a sequence of images or feature maps with corresponding dimensions. The number of channels is given above the bars and the height and width of the images on the left of the bars (unless unchanged).
  • Figure 2: Plots of a rain image before and after the cropping. Note that the coordinates start in the top-left corner and that the images are vertically flipped compared to a standard map of the Netherlands.
  • Figure 3: Plots of the other weather variables used as inputs for the models with two streams. Note that the coordinates start in the top-left corner and that the images appear vertically flipped (this is best seen in the image of relative humidity where the map of the Netherlands is somewhat visible from the image).
  • Figure 4: Plot of the MSEs of the models per time step. The persistence model is not included in the plot due to its much higher MSEs which would skew the plot.
  • Figure 5: The predictions of the models for a certain test sample and the corresponding ground-truth images. The images form a sequence of predictions from $1$ to $4$ hours ahead.