paper
2019
Data assimilation
Explainability, uncertainty & physics New model or method
Classical ML (trees, GP, linear, kernels)Colorado State University (incl. CIRA) Journal
2019-12-27
022
Dec 2019
Michael Goodliff, Steven J. Fletcher, Anton J. Kliewer et al.
Journal of Geophysical Research Atmospheres
Colorado State University (incl. CIRA)
Support vector machines and neural networks detect non-Gaussian error distributions in the Lorenz-63 model, informing relaxation of Gaussian assumptions in data assimilation.
Detection of Non‐Gaussian Behavior Using Machine Learning Techniques: A Case Study on the Lorenz 63 Model
In one sentence. Support vector machines and neural networks detect non-Gaussian error distributions in the Lorenz-63 model, informing relaxation of Gaussian assumptions in data assimilation.
Labels
- Topic
- Data assimilation, Explainability, uncertainty & physics
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Domain
- Global
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.1029/2019jd031551 OpenAlex:W2997445836
- Added
- 2026-10-22
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