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Measurement of Aerosols Optical Thickness of the Atmosphere using the GLOBE Handheld Sun Photometer
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Stacking Machine Learning Models Empowered High Time-Height-Resolved Ozone Profiling from the Ground to the
Sanbao Zhang1, Shanshan Wang1,2, Jian Zhu1
1Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China.
Environmental Science & Technology
|April 17, 2024
Summary
This study introduces a new method using multiaxis differential optical absorption spectroscopy (MAX-DOAS) and machine learning (ML) to measure ozone (O3) profiles. This cost-effective technique provides high-resolution ozone data from the ground to the stratopause.
Area of Science:
- Atmospheric chemistry and physics
- Remote sensing technologies
- Machine learning applications in environmental science
Background:
- Ozone (O3) profiles are vital for understanding atmospheric dynamics, but traditional monitoring methods have limitations.
- Conventional O3 monitoring suffers from low spatiotemporal resolution, high costs, and complex procedures.
Purpose of the Study:
- To develop a novel, cost-effective method for retrieving high-resolution ozone (O3) profiles.
- To enable detailed analysis of ozone sources, sinks, and transport dynamics.
Main Methods:
- Combined multiaxis differential optical absorption spectroscopy (MAX-DOAS) with machine learning (ML) models.
- Trained ML models using radiative transfer modeling, MAX-DOAS observations, and reanalysis data.
- Employed a stacking approach to enhance ML model accuracy for O3 retrieval.
Main Results:
- Achieved high temporal resolution (minute-level) and vertical resolution (hundred-meter scale) for O3 profiles.
- Validated MAX-DOAS O3 profiles against in situ, lidar, and satellite data, showing high consistency.
- Estimated total error for the O3 retrieval approach to be within 25%.
Conclusions:
- This study presents the first ground-based passive remote sensing of high time-height-resolved O3 distribution from 0-60 km.
- The developed MAX-DOAS and ML approach offers a cost-effective and versatile tool for atmospheric monitoring.
- Opens new avenues for understanding ozone dynamics and enables potential for stereoscopic trace gas observations.
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