一种卷积神经网络方法用于从多轴差光学吸收光谱测量中检索热层臭氧垂直分布
Zijie Wang1, Xin Tian1, Pinhua Xie2
1Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China.
The Science of the total environment
|July 27, 2024
概括
本研究引入了一个卷积神经网络 (CNN) 模型,使用多轴差光吸收光谱 (MAX-DOAS) 数据准确检索热层臭氧 (O3) 垂直分布,通过降低误差率改进了以前的方法.
科学领域:
- 大气化学和物理大气化学和物理
- 遥感 遥感 遥感 遥感
- 机器学习在环境科学中的应用
背景情况:
- 热层臭氧 (O3) 的垂直分布对大气过程至关重要.
- 多轴差光吸收光谱 (MAX-DOAS) 测量面临着平流层O3干扰的挑战.
- 准确的O3档案对于空气质量监测和气候研究至关重要.
研究的目的:
- 开发和验证一个卷积神经网络 (CNN) 模型,从MAX-DOAS测量中检索热层O3垂直分布.
- 在MAX-DOAS数据中解决和克服平流层O3吸收的干扰.
- 为了提高热层O3概况检索的准确性和可靠性.
主要方法:
- 采用混合特征选择模型 (PCA-F_Regression-SVR) 来识别O3逆转的敏感因素,包括气象和微量气体概况.
- 使用预处理的MAX-DOAS光谱和选择的敏感因素作为输入,用重新分析的O3配置文件作为输出,构建了一个CNN模型.
- 该模型的性能使用平均绝对百分比误差 (MAPE) 进行评估,并与独立数据集进行比较.
主要成果:
- 美国有线电视新闻网 (CNN) 模型成功地复制了热层O3配置文件,平均绝对百分比误差 (MAPE) 从26%降至约19%.
- 获取的O3配置显示出高斯式分布,峰值在950hPa (550m) 左右.
- 该模型显示,由于温度的影响,在夏季倾向于高估表面O3,而在光滑的再分析数据中对极端值不敏感.
结论:
- 开发的CNN模型有效地利用MAX-DOAS光谱进行准确的热层O3垂直分布检索.
- 该方法成功地减轻了平流层O3干扰,为O3分析提供了更可靠的方法.
- 这种技术为大气研究,空气质量管理和气候建模提供了宝贵的数据.
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