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. In this study, we explore a new approach based on machine learning(ML) for deriving aerosol extinction coefficient profiles, single-scatteringalbedo and asymmetry parameter at 360 nm from a single multi-axis differential optical absorption spectroscopy (MAX-DOAS) sky scan.Our method relies on a multi-output sequence-to-sequence model combiningconvolutional neural networks (CNNs) for feature extraction and longshort-term memory networks (LSTMs) for profile prediction. The model wastrained and evaluated using data simulated by Vector Linearized Discrete Ordinate Radiative Transfer (VLIDORT) v2.7, which contains1 459 200 unique mappings. From the simulations, 75 % were randomly selected for training and the remaining 25 % for validation. The overall error ofestimated aerosol properties (1) for total aerosol optical depth (AOD) is - 1.4 ± 10.1  %,(2) for the single-scattering albedo is 0.1±3.6  %, and (3) for the asymmetryfactor is - 0.1 ± 2.1  %. The resulting model is capable ofretrieving aerosol extinction coefficient profiles with degrading accuracyas a function of height. The uncertainty due to the randomness in MLtraining is also discussed.

atmospheric measurement techniques

A feasibility study to use machine learning as an inversion algorithm for aerosol profile and property retrieval from multi-axis differential absorption spectroscopy measurements