paper
2022
Hybrid physics-ML & parameterization
New model or method
Classical ML (trees, GP, linear, kernels) Journal
2022-10-06
016
Oct 2022
Mozhgan Amiramjadi, Riwal Plougonven, Ali Reza Mohebalhojeh et al.
Journal of the Atmospheric Sciences
Random forests reconstruct nonorographic gravity wave variability at source levels from ERA5 columns with R2 near 0.85, finding local background wind most informative.
Hybrid physics-ML & parameterization
Using Machine Learning to Estimate Nonorographic Gravity Wave Characteristics at Source Levels
In one sentence. Random forests reconstruct nonorographic gravity wave variability at source levels from ERA5 columns with R2 near 0.85, finding local background wind most informative.
Labels
- Topic
- Hybrid physics-ML & parameterization
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Domain
- Regional
- Data
- ERA5 / reanalysis
- Status
- Research
- Identifiers
- DOI:10.1175/jas-d-22-0021.1 OpenAlex:W4303182363
- Added
- 2026-10-22
Labels and summary are generated by Claude and reviewed weekly. Wrong? Edit this entry.