A convolutional neural networks method for tropospheric ozone vertical distribution retrieval from Multi-AXis...

The vertical distribution of tropospheric ozone (O₃) is crucial for understanding atmospheric physicochemical processes. A Convolutional Neural Networks (CNN) method for the retrieval of tropospheric O₃ vertical distribution from ground-based Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements to tackle the issue of stratospheric O₃ absorption interference faced by MAX-DOAS in obtaining tropospheric O₃ profiles. Firstly, a hybrid model, named PCA-F_Regression-SVR, is developed to screen features sensitive to O₃ inversion based on the MAX-DOAS spectra and EAC4 reanalysis O₃ profiles, which incorporates Principal Component Analysis (PCA), F_Regression function, and Support Vector Regression (SVR) algorithm. Thus, these screened features for ancillary inversion include the profiles of temperature, specific humidity, fraction of cloud coverage, eastward and northward wind, the profiles of SO₂, NO₂, and HCHO, as well as season and time features to serve as sensitive factors. Secondly, the preprocessed MAX-DOAS spectra dataset and the sensitive factor dataset are utilized as input, while the O₃ profiles of the EAC4 reanalysis dataset incorporating the surface O₃ concentrations are employed as output for constructing the CNN model. The Mean Absolute Percentage Error (MAPE) decreases from 26 % to approximately 19 %. Finally, the CNN model is applied for inversion and comparison of tropospheric O₃ profiles using independent input data. The CNN model effectively reproduces the O₃ profiles of the EAC4 dataset, showing a Gaussian-like spatial distribution with peaks primarily around 950 hPa (550 m). Since the reanalysis data used for model training has been smoothed, the CNN model is insensitive to extreme values. This behavior can be attributed to the MAPE loss function, which evaluates Absolute Percentage Errors (APEs) of O₃ concentration at all altitudes, resulting in varying retrieval accuracy across different altitudes while maintaining overall MAPE control. Temporally, the CNN model tends to overestimate surface O₃ in summer by around 20 μg/m³, primarily due to the influence of the temperature feature in the sensitivity factor dataset. In conclusion, leveraging MAX-DOAS spectra enables the retrieval of tropospheric O₃ vertical distribution through the established CNN model.