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

arXiv:2603.04461v1 Announce Type: new 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 ind

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Samuel van Wonderen, Siamak Mehrkanoon · · 1 min read · 2 views
#cs.LG #cs.AI

arXiv:2603.04461v1 Announce Type: new 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.

Executive Summary

The article introduces the Multimodal Advection-Guided Small Attention GNet (MAD-SmaAt-GNet), a deep learning model for precipitation nowcasting that extends the SmaAt-UNet architecture. The model incorporates an additional encoder to learn from multiple weather variables and integrates a physics-based advection component for physically consistent predictions. The results show that MAD-SmaAt-GNet reduces the mean squared error by 8.9% compared to the baseline SmaAt-UNet for four-step precipitation forecasting up to four hours ahead.

Key Points

  • ▸ MAD-SmaAt-GNet extends the SmaAt-UNet architecture with an additional encoder and advection component
  • ▸ The model uses multimodal inputs to learn from multiple weather variables
  • ▸ The advection component ensures physically consistent predictions

Merits

Improved Accuracy

The MAD-SmaAt-GNet model demonstrates improved accuracy in precipitation nowcasting, reducing the mean squared error by 8.9% compared to the baseline SmaAt-UNet.

Physically Consistent Predictions

The integration of a physics-based advection component ensures that the model's predictions are physically consistent, which is essential for reliable precipitation nowcasting.

Demerits

Complexity

The addition of an extra encoder and advection component may increase the model's complexity, potentially making it more difficult to train and interpret.

Expert Commentary

The MAD-SmaAt-GNet model represents a significant advancement in precipitation nowcasting, leveraging the strengths of deep learning and physics-based modeling. The use of multimodal inputs and advection component demonstrates a nuanced understanding of the complex relationships between weather variables. However, further research is needed to fully explore the model's potential and address potential limitations, such as increased complexity. The implications of this work extend beyond precipitation nowcasting, with potential applications in climate modeling, weather forecasting, and decision-making in weather-sensitive industries.

Recommendations

  • ✓ Further research should investigate the application of MAD-SmaAt-GNet to other environmental forecasting tasks
  • ✓ The development of more efficient training methods and interpretability techniques can help mitigate the increased complexity of the model

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