从2015年到2020年,基于OCO-2和CAMS数据,每天在中国各地生成高分辨率和全覆盖的XCO2
Tongwen Li1, Jingan Wu1, Tianxing Wang1
1School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai, China; Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, Ministry of Natural Resources, Guangzhou, China.
The Science of the total environment
|June 18, 2023
概括
中国的高分辨率二氧化碳 (CO2) 数据是使用深度神经网络生成的,将卫星和再分析数据融合在一起. 这一数据集通过提供从2015年到2020年的每日,全面覆盖的二氧化碳测量来帮助实现碳中和目标.
科学领域:
- 地球和环境科学 地球和环境科学
- 大气科学 大气科学
- 气候科学 气候科学
背景情况:
- 中国的目标是到2060年实现碳中和,需要精确的二氧化碳 (CO2) 监测.
- 卫星遥感为二氧化碳数据提供了广泛的覆盖范围,但由于云层和传感器限制,面临着空间差距.
- 现有的二氧化碳数据集往往缺乏详细碳循环分析所需的分辨率和完整性.
研究的目的:
- 为了生成每日,高分辨率 (0.1°),全覆盖的 CO2 (XCO2) 干空分子分数的平均值,在中国的 2015-2020 年的数据.
- 开发一种可靠的方法来融合卫星观测和重新分析数据,以克服数据缺口.
- 为了解中国的二氧化碳分布,季节变化和趋势提供一个有价值的数据集.
主要方法:
- 使用深度神经网络 (DNN) 框架将来自轨道碳观测站-2 (OCO-2) 卫星和哥白尼号大气监测服务 (CAMS) 的数据重新分析.
- 该DNN模型学习了卫星XCO2,CAMS XCO2和相关环境因素之间的关系.
- 每日全面覆盖的XCO2数据是使用训练DNN模型,CAMS数据和环境变量生成的.
主要成果:
- 在基于样本的交叉验证中,生成的XCO2数据集显示出高准确性,R2为0.963和RMSE为0.99ppm.
- 独立的实地验证证实了数据集的可靠性,显示R2为0.866和RMSE为1.71ppm.
- 分析显示,从2015年到2020年,中国的二氧化碳增长率为2.71ppm/年.
结论:
- 该研究成功地为中国生产了长期,高分辨率和无差距的XCO2数据集.
- 这一数据集显著提高了对中国内碳循环和大气二氧化碳动态的理解.
- 产生的数据是公开的,对于支持中国的碳中和目标至关重要.
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