为中国 (2016-2020) 提供高分辨率的每日二氧化碳数据集
Zhengwu Yuan1,2, Yang Liu1,2, Aixia Yang3
1Chongqing Engineering Research Center of Spatial Big Data Intelligent Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
Scientific data
|January 15, 2026
概括
本研究提出了一种用于重建中国各地高分辨率二氧化碳 (CO2) 数据的新方法,改进了碳循环研究. 增强的数据集为气候政策支持提供了更可靠的时空CO2动态.
科学领域:
- 大气科学 大气科学
- 地球系统科学 地球系统科学
- 环境监测 环境监测
背景情况:
- 高分辨率列平均干空气二氧化碳分子分数 (XCO2) 数据对于理解碳循环和支持气候政策至关重要.
- 卫星二氧化碳检索因云层和气溶而面临限制,导致数据碎片化和不连续.
研究的目的:
- 开发一种可靠的方法来重建中国各地的时空XCO2动态.
- 提高二氧化碳测量的准确性和连续性,用于碳研究和气候政策.
主要方法:
- 使用通过贝叶斯优化 (XGBoost-BO) 优化的XGBoost模型.
- 嵌入了SHAP (夏普利添加式扩展) 以提高模型的解释性.
- 使用多源辅助参数绘制大气XCO2度的地图.
主要成果:
- 与OCO-2卫星观测结果取得了很高的一致性 (R2=0.98,RMSE=0.58 ppm,MAPE=0.07%).
- 根据TCCON在中国的地面测量进行验证 (合肥:R2=0.92,河:R2=0.70).
- 证明了具有物理意义的时空空间XCO2动态重建.
结论:
- 使用SHAP的XGBoost-BO模型提供了一种可靠的方法来重建高分辨率的XCO2数据.
- 重建后的数据集增强了对中国的碳来源和碳汇的描述.
- 这种方法通过提高数据连续性和准确性来支持气候政策目标,例如碳峰值和中性.
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