# Extreme precipitation forecasting using attention augmented convolutions

arXiv: https://arxiv.org/abs/2201.13408 · HTML: https://mlsanity.com/paper/2201.13408/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 31 Jan 2022.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [On the Relationship between Self-Attention and Convolutional Layers](https://mlsanity.com/paper/1911.03584/) (2019)
- [QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension](https://mlsanity.com/paper/1804.09541/) (2018)
- [Analog forecasting of extreme-causing weather patterns using deep learning](https://mlsanity.com/paper/1907.11617/) (2019)
- [Convolutional Self-Attention Networks](https://mlsanity.com/paper/1904.03107/) (2019)
- [Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets](https://mlsanity.com/paper/1605.01156/) (2016)
- [Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances](https://mlsanity.com/paper/2006.07972/) (2020)
- [Extreme Precipitation Seasonal Forecast Using a Transformer Neural Network](https://mlsanity.com/paper/2107.06846/) (2021)

## Built on by (most cited first, 1 of 1)

- [From Black Box to Insight: Explainable AI for Extreme Event Preparedness](https://mlsanity.com/paper/2511.13712/) (2025)

## Abstract (arXiv)

Extreme precipitation wreaks havoc throughout the world, causing billions of dollars in damage and uprooting communities, ecosystems, and economies. Accurate extreme precipitation prediction allows more time for preparation and disaster risk management for such extreme events. In this paper, we focus on short-term extreme precipitation forecasting (up to a 12-hour ahead-of-time prediction) from a sequence of sea level pressure and zonal wind anomalies. Although existing machine learning approaches have shown promising results, the associated model and climate uncertainties may reduce their reliability. To address this issue, we propose a self-attention augmented convolution mechanism for extreme precipitation forecasting, systematically combining attention scores with traditional convolutions to enrich feature data and reduce the expected errors of the results. The proposed network architecture is further fused with a highway neural network layer to gain the benefits of unimpeded information flow across several layers. Our experimental results show that the framework outperforms classical convolutional models by 12%. The proposed method increases machine learning as a tool for gaining insights into the physical causes of changing extremes, lowering uncertainty in future forecasts.