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

Ozone (O) profiles are crucial for comprehending the intricate interplay among O sources, sinks, and transport. However, conventional O 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 O 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 O, we employ a stacking approach in constructing ML models. The retrieved MAX-DOAS O 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 O distribution from ground to the stratopause (0-60 km). It opens up new avenues for enhancing our understanding of the dynamics of O 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.