Learning predictable and informative dynamical drivers of extreme precipitation using variational autoencoders
From Earthly Machine Learning by Amirpasha
March 7, 2026 · 18 min · Season 2 · Episode 4
About this episode
This episode discusses a novel machine learning approach to identify drivers of extreme precipitation using variational autoencoders.
Citation: Spuler, F. R., Kretschmer, M., Balmaseda, M. A., Kovalchuk, Y., & Shepherd, T. G. (2025). Learning predictable and informative dynamical drivers of extreme precipitation using variational autoencoders. Weather and Climate Dynamics , 6, 995–1014. https://doi.org/10.5194/wcd-6-995-2025 Main Takeaways: Innovative Machine Learning Approach: The study introduces the Categorical Mixture Model Variational Autoencoder (CMM-VAE), a novel generative machine learning method designed to identify probabilistic atmospheric circulation regimes by combining targeted dimensionality reduction and probabilistic clustering into a single model. Resolving a Major Forecasting Trade-off: Traditionally, atmospheric regimes are either highly predictable globally but locally uninformative, or highly informative for local impacts but lacking in subseasonal predictability. CMM-VAE resolves this trade-off, successfully identifying patterns that predict local extremes without sacrificing forecast skill at subseasonal lead times. Targeted Application for Moroccan Rainfall: When applied to extreme winter precipitation in Morocco, the CMM-VAE method successfully disentangled a distinct, highly…
People in this episode
Host: Amirpasha
Topics covered
- machine learning
- extreme precipitation
- atmospheric circulation
- forecasting
- climate dynamics
Keywords
- CMM-VAE
- variational autoencoder
- atmospheric regimes
- Moroccan rainfall
- forecast skill
- climate drivers
Mentioned in this episode
Organizations: Weather and Climate Dynamics
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