synthetic-AMFs-ML

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.

在本研究中，我们探讨了基于机器学习（ML）的新方法，用于从单一MAX-DOAS天顶扫描中推导出气溶胶消光系数剖面、360纳米处的单次散射反照率和不对称参数。本方法依赖于一组多输出序列到序列模型，该模型结合了卷积神经网络（CNN）进行特征提取和长短期记忆网络（LSTM）进行剖面预测。该模型使用由VLIDORT v2.7模拟的数据进行训练和评估，该数据包含1459200个独特的映射。其中75%的模拟数据被随机选取用于训练，剩余的25%用于验证。估计的气溶胶属性的整体误差为：（1）总气溶胶光学厚度为-1.4 ± 10.1%；（2）单次散射反照率为0.1 ± 3.6%；（3）不对称因子为-0.1 ± 2.1%。所得到的模型能够以高度为函数的降级精度恢复气溶胶消光系数剖面。同时，还讨论了由于机器学习训练中的随机性所导致的不确定性。