Low Cloud Detection in Multilayer Scenes Using Satellite Imagery with Machine Learning Methods
Problem
Detecting low clouds hidden beneath higher cloud layers in satellite imagery, which is hard for passive sensors and matters for aviation nowcasting and weather forecasting.
Why it matters
Low cloud detection directly affects aviation safety and general weather forecasting accuracy.
What the AI did
Random forest and neural network classifiers were trained on GOES ABI radiances, brightness temperatures, surface info and humidity to flag low clouds in multilayer scenes and improve the Cloud Cover Layers product.
Results
not stated in source
Impact
Peer-reviewed methods developed to improve an operational cloud product, but no measured performance is given in the evidence.
Record
| Venue | Journal of Atmospheric and Oceanic Technology |
|---|---|
| Published | 2022 |
| Work type | article |
| Authors | John M. Haynes, Yoo‐Jeong Noh, Steven D. Miller, Katherine Haynes, Imme Ebert‐Uphoff, Andrew K. Heidinger |
| Citations | 24 (OpenAlex, at import time) |
| References | 45 |
| Model or method | Random forest and neural network |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1175/jtech-d-21-0084.1 |
| Open access | closed |
| Keywords | low clouds, multilayer clouds, machine learning, satellite imagery, random forest, neural network |
Importance rating
real but narrower advance
Abstract states low-cloud detection is important for aviation nowcasting and general weather forecasting; work improves an operational cloud product (PoD 0.685 to 0.815), a real but specialized meteorological advance.
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