paper
2021
Observations & remote sensing
Nowcasting New model or method
Classical ML (trees, GP, linear, kernels)RNN / LSTM / ConvLSTMColorado State University (incl. CIRA)NOAA (incl. GFDL, PSL, GSL, NCEP/EMC) Journal
2021-12-15
047
Dec 2021
John M. Haynes, Yoo‐Jeong Noh, S. D. Miller et al.
Journal of Atmospheric and Oceanic Technology
Colorado State University (incl. CIRA), NOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
Random forest and neural network models on GOES-ABI imagery improve low-cloud detection in multilayer scenes, raising probability of detection from 0.685 to 0.815 versus the CCL product.
Low Cloud Detection in Multilayer Scenes Using Satellite Imagery with Machine Learning Methods
In one sentence. Random forest and neural network models on GOES-ABI imagery improve low-cloud detection in multilayer scenes, raising probability of detection from 0.685 to 0.815 versus the CCL product.
Labels
- Topic
- Observations & remote sensing, Nowcasting
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels), RNN / LSTM / ConvLSTM
- Domain
- Regional
- Data
- Satellite, Other
- Status
- Research
- Identifiers
- DOI:10.1175/jtech-d-21-0084.1 OpenAlex:W4200052465
- Added
- 2026-10-22
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