paper
2021
Explainability, uncertainty & physics
Hybrid physics-ML & parameterization Position or perspective paper
Other Journal
2021-05-18
012
May 2021
Suzanne Kawamleh
Philosophy of Science
Philosophical argument that neural-network cloud parameterizations lacking physical process representation undermine the reliability of climate model predictions.
Explainability, uncertainty & physics
Can Machines Learn How Clouds Work? The Epistemic Implications of Machine Learning Methods in Climate Science
In one sentence. Philosophical argument that neural-network cloud parameterizations lacking physical process representation undermine the reliability of climate model predictions.
Labels
- Topic
- Explainability, uncertainty & physics, Hybrid physics-ML & parameterization
- Type
- Position or perspective paper
- Method
- Other
- Domain
- Global
- Status
- Research
- Identifiers
- DOI:10.1086/714877 OpenAlex:W3163834479
- Added
- 2026-10-22
Labels and summary are generated by Claude and reviewed weekly. Wrong? Edit this entry.