堆叠机器学习模型授权高时间-高度分辨率的臭氧分析,从地面到平流层,基于MAX-DOAS观测
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
概括
这项研究引入了一种使用多轴差光学吸收光谱 (MAX-DOAS) 和机器学习 (ML) 来测量臭氧 (O3) 配置文件的新方法. 这种具有成本效益的技术提供了高分辨率的臭氧数据,从地面到平流层.
科学领域:
- 大气化学和物理大气化学和物理
- 遥感技术 遥感技术
- 机器学习在环境科学中的应用.
背景情况:
- 臭氧 (O3) 档案对于了解大气动态至关重要,但传统的监测方法存在局限性.
- 传统的O3监测存在时空分辨率低,成本高,程序复杂等问题.
研究的目的:
- 开发一种新的,具有成本效益的方法来获取高分辨率的臭氧 (O3) 配置文件.
- 为了能够详细分析臭氧来源,沉积点和运输动态.
主要方法:
- 结合多轴差光学吸收光谱 (MAX-DOAS) 与机器学习 (ML) 模型.
- 训练有素的ML模型使用辐射转移建模,MAX-DOAS观测和重新分析数据.
- 采用堆叠方法来提高O3检索的ML模型准确性.
主要成果:
- 实现O3配置文件的高时间分辨率 (分钟级) 和垂直分辨率 (百米级).
- 通过对现场,激光雷达和卫星数据进行验证的MAX-DOAS O3配置文件,显示出高度一致性.
- 估计O3检索方法的总误差在25%以内.
结论:
- 本研究介绍了第一个基于地面的被动遥感高时间高度分辨率的O3分布,从0-60公里.
- 开发的MAX-DOAS和ML方法为大气监测提供了一个具有成本效益和多功能工具.
- 开辟了了解臭氧动态的新途径,并为立体镜微量气体观测提供了潜力.
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