paper
2025
Data assimilation
New model or method
Classical ML (trees, GP, linear, kernels)6 h–3 daysChina Meteorological Administration (CMA)Fudan University (FuXi)Institute of Atmospheric Physics, Chinese Academy of Sciences Journal
2025-05-09
047
May 2025
Yong-Hui Li, Wan-Suo Duan, Wei Han et al.
Journal of Geophysical Research: Atmospheres
China Meteorological Administration (CMA), Fudan University (FuXi), Institute of Atmospheric Physics, Chinese Academy of Sciences
A random forest generates pseudo soundings for Tibetan Plateau initial conditions, reducing hourly precipitation simulation bias by 31.8%.
Improving Tropical Cyclone Track Forecast Skill Through Assimilating Target Observation Achieved by AI‐Based Conditional Nonlinear Optimal Perturbation
In one sentence. A random forest generates pseudo soundings for Tibetan Plateau initial conditions, reducing hourly precipitation simulation bias by 31.8%.
Labels
- Topic
- Data assimilation
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days
- Domain
- Regional
- Data
- ERA5 / reanalysis, Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1029/2024jd043261 OpenAlex:W4410219098
- Added
- 2026-10-22
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