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

摘要

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 360nm from a single multi-axis differential optical absorption spectroscopy (MAX-DOAS) sky scan. Our method relies on a multi-output sequence-to-sequence model combining convolutional neural networks (CNNs) for feature extraction and long short-term memory networks (LSTMs) for profile prediction. The model was trained and evaluated using data simulated by Vector Linearized Discrete Ordinate Radiative Transfer (VLIDORT)?v2.7, which contains 1459200 unique mappings. From the simulations, 75% were randomly selected for training and the remaining 25% for validation. The overall error of estimated 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 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.