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synthetic-AMFs-ML

dataset
posted on 23.02.2021, 18:20 by Yun Dong, Elena Spinei, Anuj Karpatne
In this study, we explore a new approach based on machine learning (ML) for deriving aerosol extinction coefficient profiles, single scattering albedo and asymmetry parameter at 360 nm from a single MAX-DOAS sky scan. Our method relies on a multi-output sequence-to-sequence model combining Convolutional Neural Networks (CNN) for feature extraction and Long Short-Term Memory networks (LSTM) for profile prediction. The model was trained and evaluated using data simulated by VLIDORT v2.7, which contains 1459200 unique mappings. 75% randomly selected simulations were used for training and the remaining 25% for validation. The overall error of estimated aerosol properties for (1) total AOD is -1.4 ± 10.1 %, (2) for single scattering albedo is 0.1 ± 3.6 %; and (3) asymmetry factor is -0.1 ± 2.1 %. The resulting model is capable of retrieving aerosol extinction coefficient profiles with degrading accuracy as a function of height. The uncertainty due to the randomness in ML training is also discussed.

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University Libraries, Virginia Tech

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Virginia Polytechnic Institute and State University

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