# Analog forecasting of extreme-causing weather patterns using deep learning

arXiv: https://arxiv.org/abs/1907.11617 · HTML: https://mlsanity.com/paper/1907.11617/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2019.

## Built on by (most cited first, 30 of 33)

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- [Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Review](https://mlsanity.com/paper/2207.07580/) (2022)
- [Inductive biases in deep learning models for weather prediction](https://mlsanity.com/paper/2304.04664/) (2023)
- [Extreme heatwave sampling and prediction with analog Markov chain and comparisons with deep learning](https://mlsanity.com/paper/2307.09060/) (2023)
- [Using Deep Learning to Identify Initial Error Sensitivity for Interpretable ENSO Forecasts](https://mlsanity.com/paper/2404.15419/) (2024)
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- [Uncertainty propagation in feed-forward neural network models](https://mlsanity.com/paper/2503.21059/) (2025)
- [AI-boosted rare event sampling to characterize extreme weather](https://mlsanity.com/paper/2510.27066/) (2025)
- [From Black Box to Insight: Explainable AI for Extreme Event Preparedness](https://mlsanity.com/paper/2511.13712/) (2025)
- [Harnessing Artificial Intelligence for Sustainable Agricultural Development in Africa: Opportunities, Challenges, and Impact](https://mlsanity.com/paper/2401.06171/) (2024)
- [Generative forecasting with joint probability models](https://mlsanity.com/paper/2512.24446/) (2025)
- [Dynamics-Informed Deep Learning for Predicting Extreme Events](https://mlsanity.com/paper/2603.10777/) (2026)
- [Extreme precipitation forecasting using attention augmented convolutions](https://mlsanity.com/paper/2201.13408/) (2022)
- [Accurate Long-term Air Temperature Prediction with a Fusion of Artificial Intelligence and Data Reduction Techniques](https://mlsanity.com/paper/2209.15424/) (2022)
- [Advancing Heatwave Forecasting via Distribution Informed-Graph Neural Networks (DI-GNNs): Integrating Extreme Value Theory with GNNs](https://mlsanity.com/paper/2411.13496/) (2024)

## Abstract (arXiv)

Numerical weather prediction (NWP) models require ever-growing computing time/resources, but still, have difficulties with predicting weather extremes. Here we introduce a data-driven framework that is based on analog forecasting (prediction using past similar patterns) and employs a novel deep learning pattern-recognition technique (capsule neural networks, CapsNets) and impact-based auto-labeling strategy. CapsNets are trained on mid-tropospheric large-scale circulation patterns (Z500) labeled 0-4 depending on the existence and geographical region of surface temperature extremes over North America several days ahead. The trained networks predict the occurrence/region of cold or heat waves, only using Z500, with accuracies (recalls) of 69%-45% (77%-48%) or 62%-41% (73%-47%) 1-5 days ahead. CapsNets outperform simpler techniques such as convolutional neural networks and logistic regression. Using both temperature and Z500, accuracies (recalls) with CapsNets increase to ~ 80% (88%), showing the promises of multi-modal data-driven frameworks for accurate/fast extreme weather predictions, which can augment NWP efforts in providing early warnings.