paper
2022
Climate analysis & attribution
Explainability, uncertainty & physics New model or method
CNN / U-NetColorado State University (incl. CIRA)Stanford University Other
2022-12-16
026
Dec 2022
Jared T. Trok, Frances V. Davenport, Elizabeth A. Barnes et al.
preprint
Colorado State University (incl. CIRA), Stanford University
CNNs predict daily maximum temperature from soil moisture and geopotential, and partial dependence reveals nonlinear soil moisture-temperature coupling across mid-latitude regions.
Climate analysis & attribution
Using Machine Learning with Partial Dependence Analysis to Investigate Land-Atmosphere Coupling
In one sentence. CNNs predict daily maximum temperature from soil moisture and geopotential, and partial dependence reveals nonlinear soil moisture-temperature coupling across mid-latitude regions.
Labels
- Topic
- Climate analysis & attribution, Explainability, uncertainty & physics
- Type
- New model or method
- Method
- CNN / U-Net
- Domain
- Regional
- Data
- ERA5 / reanalysis
- Status
- Research
- Identifiers
- DOI:10.22541/essoar.167117448.86075329/v1 OpenAlex:W4311726315
- Added
- 2026-10-22
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