麦克普拉 - 一个新的气体概况反转算法用于MAX-DOAS,适用于50米的垂直分辨率
Jiangyi Zheng1, Pinhua Xie2, Xin Tian3
1Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; University of Science and Technology of China, Hefei 230026, China.
The Science of the total environment
|July 28, 2023
概括
一个新的算法,McPrA,通过减少对先验数据的依赖来改善MAX-DOAS测量中的微量气体配置分析. 这种方法提高了评估空气污染物及其环境健康风险的准确性.
科学领域:
- 大气化学和物理大气化学和物理
- 环境科学和环境监测
- 远程传感和数据检索
背景情况:
- 空气污染物对环境和健康构成重大风险,需要准确的评估方法.
- MAX-DOAS (多轴差异光吸收光谱) 方法提供垂直气体配置分析,但由于依赖先验配置文件而受到限制.
- 现有的MAX-DOAS检索算法需要改进,以提高微量气体概况评估的准确性.
研究的目的:
- 引入一种新的MAX-DOAS微量气体概况反转算法,McPrA,旨在减少对先验概况信息的依赖.
- 通过使用MAX-DOAS测量来提高微量气体概况检索的精度和垂直分辨率.
- 根据既有方法和现场数据验证McPrA算法的性能.
主要方法:
- 开发了McPrA (蒙特卡罗概况分析) 算法,采用蒙特卡罗方法对微量气体进行最佳估计.
- 使用来自SCIATRAN辐射转移建模的空气质量因子计算气体垂直柱密度.
- 通过将重量函数与先验配置文件相结合来检索痕迹气体的垂直分布,并结合一个规范化过程以改进匹配,并允许灵活修改网格以获得高垂直分辨率 (高达50米).
主要成果:
- 麦克普拉算法在检索气体概况方面表现出高精度,与现场数据相比,第一层NO2的相关系数超过0.89,与现场数据相比.
- 灵敏度实验确定了最佳检索参数,产生了大于3.0.0的自由度.
- 与WRF-Chem和合成数据进行的比较验证实验证实了McPrA算法在准确的气体概况检索中的有效性.
结论:
- 麦克普拉算法在MAX-DOAS微量气体分析中提供了显著的进步,减少了对先验数据的依赖,并提高了准确性.
- 该算法的网格修改灵活性和高垂直分辨率能力使其成为空气质量监测的宝贵工具.
- 麦克普拉为评估空气污染物分布提供了可靠和有效的方法,有助于更好地了解环境和健康风险.
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