Dust Machine Learning Probability

Posted on: 2/25/2022

This lesson introduces operational users to a machine-learning based Dust Probability product developed by the NASA SPoRT program for the application of detecting and monitoring blowing dust plumes at night. Advances in earth observing satellites has improved monitoring and detection of dust both day and night through derived imagery such as the Dust RGB. However, limitations of the RGB at night result in less contrast between dust and land surface features, as seen by the user. A Machine Learning (ML) model has been developed and applied to GOES-16 ABI to overcome this limitation and improve nighttime dust detection.


Author (s): Kevin Fuell (UAH), Emily Berndt (NASA), Rob Junod (UAH), Sebastian Harkema (UAH)
Language: English
Location: U.S. Southwest and Central
Date of event(s): March 16-17, 2021; March 15-16, 2018
Categories: Model Derived, Safety, Aerosols and Air Quality

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