Stacking Machine Learning Models Empowered High Time-Height-Resolved Ozone Profiling from the Ground to the Stratopause Based on MAX-DOAS Observation

Abstract

Ozone (O3) profiles are crucial for comprehending the intricate interplay among O3 sources, sinks, and transport. However, conventional O3 monitoring approaches often suffer from limitations such as low spatiotemporal resolution, high cost, and cumbersome procedures. Here, we propose a novel approach that combines multiaxis differential optical absorption spectroscopy (MAX-DOAS) and machine learning (ML) technology. This approach allows the retrieval of O3 profiles with exceptionally high temporal resolution at the minute level and vertical resolution reaching the hundred-meter scale. The ML models are trained using parameters obtained from radiative transfer modeling, MAX-DOAS observations, and a reanalysis data set. To enhance the accuracy of retrieving the aqueous phosphorus from O3, we employ a stacking approach in constructing ML models. The retrieved MAX-DOAS O3 profiles are compared to data from an in situ instrument, lidar, and satellite observation, demonstrating a high level of consistency. The total error of this approach is estimated to be within 25%. On balance, this study is the first ground-based passive remote sensing of high time-height-resolved O3 distribution from ground to the stratopause (0–60 km). It opens up new avenues for enhancing our understanding of the dynamics of O3 in atmospheric environments. Moreover, the cost-effective and portable MAX-DOAS combined with this versatile profiling approach enables the potential for stereoscopic observations of various trace gases across multiple platforms.