paper
2022
Observations & remote sensing
New model or method
Classical ML (trees, GP, linear, kernels) Journal
2022-01-24
012
Jan 2022
Yuxin Zheng, Cuixian Lu, Zhilu Wu et al.
Geophysical Research Letters
Stacked machine learning maps GNSS zenith delays to precipitable water vapor without meteorological inputs, achieving under 2.5 mm accuracy and 22-28% gains over GPT3.
Machine Learning‐Based Model for Real‐Time GNSS Precipitable Water Vapor Sensing
In one sentence. Stacked machine learning maps GNSS zenith delays to precipitable water vapor without meteorological inputs, achieving under 2.5 mm accuracy and 22-28% gains over GPT3.
Labels
- Topic
- Observations & remote sensing
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Domain
- Regional
- Data
- ERA5 / reanalysis, Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1029/2021gl096408 OpenAlex:W4207039706
- Added
- 2026-10-22
Labels and summary are generated by Claude and reviewed weekly. Wrong? Edit this entry.