Physical-Process MLWP Seminar Series

The Physical-Process MLWP Seminar Series is intended as a lightweight forum for scientific exchange on machine-learning weather prediction through the lens of atmospheric processes and physical consistency.

The aim is not to promote one model architecture or software framework. The aim is to ask what current MLWP systems can and cannot represent physically, and how we can learn from successes, failures, and targeted diagnostics.

Guiding Questions

Each seminar should try to answer:

• What physical process is being studied?
• What model, dataset, architecture, training setup, diagnostic, or constraint is being tested?
• What was the hypothesis for improving or evaluating representation of the process?
• What were the findings?
• What might transfer to other MLWP settings?
• What remains unresolved?

Candidate Themes

• Convective initiation and precipitation organisation.
• Cloud and precipitation physics.
• Physical consistency and conservation constraints.
• Geostrophic balance, stability, waves, and dynamical consistency.
• Extremes and rare events beyond pointwise scores.
• Spatial structure and object-based verification.
• Generative and probabilistic models: are sampled structures physically meaningful?
• Physics-based constraints and regime separation, for example dry versus slight-rain conditions.
• Data assimilation and latent-state representations.
• Architecture choices from a physical-process perspective.

Participation

The series is still being formed. Suggestions for speakers, papers, topics, or examples are welcome. See Contact.

Confirmed seminars will be listed here once speakers have agreed.