基于OCO-2卫星观测模拟高精度XCO2的空间分布

doi:10.12082/DQXXKX.2018.170121

地球信息科学学报 ›› 2018, Vol. 20 ›› Issue (9): 1316-1326.doi: 10.12082/DQXXKX.2018.170121

基于OCO-2卫星观测模拟高精度XCO₂的空间分布

张丽丽^1,^3,⁴(), 赵明伟², 赵娜^3,⁴, 岳天祥^3,^4,^*()

1. 中国科学院遥感与数字地球研究所,北京 100101
2. 滁州学院,滁州 239000
3. 中国科学院地理科学与资源研究所,北京 100101
4. 中国科学院大学,北京 100049

收稿日期:2017-03-29 修回日期:2018-06-21 出版日期:2018-09-25 发布日期:2018-09-25
通讯作者: 岳天祥 E-mail:zhangll@lreis.ac.cn;yue@lreis.ac.cn
作者简介:
作者简介：张丽丽（1987-）,女,博士,主要从事大气遥感、GIS应用研究。E-mail: zhangll@lreis.ac.cn
基金资助:
国家自然科学基金重大项目课题（41590844）;国家自然科学基金创新团体项目（41421001）

Modeling the Spatial Distribution of XCO₂ with High Accuracy Based on OCO-2's Observations

ZHANG Lili^1,^3,⁴(), ZHAO Mingwei², ZHAO Na^3,⁴, YUE Tianxiang^3,^4,^*()

1. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101
2. Chuzhou University, Chuzhou 239000
3. Institute of Geographic Sciences and Natural Resources Research,Beijing 100101
4.University of Chinese Academy of Sciences, Beijing 100049

Received:2017-03-29 Revised:2018-06-21 Online:2018-09-25 Published:2018-09-25
Contact: YUE Tianxiang E-mail:zhangll@lreis.ac.cn;yue@lreis.ac.cn
Supported by:
National Natural Science Foundation of China, No.41590844;National Natural Science Foundation of China, No.41421001.

摘要/Abstract

摘要：

本文首次基于OCO-2卫星观测数据,采用高精度曲面建模（High Accuracy Surface Modeling, HASM）的方法来模拟大范围高精度的二氧化碳柱浓度（XCO₂）的空间分布。首先,探讨分析HASM方法应用于模拟OCO-2卫星观测XCO₂的空间分布的可行性。从2014年9月至2015年8月OCO-2观测的12个月的XCO₂数据中,分别随机选取其各个月90%的XCO₂数据用于空间插值,剩余10%作为精度验证点。自验证结果表明,12个月的平均绝对值误差为0.34 ppm。由此可见,HASM适用于模拟OCO-2卫星观测XCO₂的空间分布。然后,采用HASM对OCO-2在2014年9月至2015年8月的各个月观测数据进行空间插值,获取空间分辨率为0.5°×0.5°的各个月均值XCO₂的空间分布,同时基于地基观测TCCON( Total Carbon Column Observing Network)站的XCO₂数据对HASM模拟结果进行交叉验证。验证结果表明,HASM模拟的XCO₂与TCCON站对应观测数据相比,其平均绝对值误差为0.81 ppm,相关系数为0.88。因此,HASM在模拟OCO-2卫星观测的XCO₂空间分布上具有很大的优势。

关键词: OCO-2, 高精度曲面建模, XCO₂空间分布, 空间插值, TCCON

Abstract:

In this study, high accuracy surface modeling (HASM) is used to simulate the spatial distribution of XCO₂ with high accuracy in a wild range of regions based on OCO-2 satellite's observations. Firstly, we discussed and analyzed the feasibility of HASM simulation of the spatial distribution of XCO₂ observed by OCO-2 satellite. For the OCO-2's XCO₂ from September 2014 to August 2015, 90% of each month's XCO₂ data were randomly selected as the input of spatial interpolation and the remaining 10% were used to verify the result of spatial interpolation.The results show that the mean absolute error of 12 months is 0.34 ppm. Therefore, HASM is suitable for simulating the spatial distribution of XCO₂ observed by OCO-2 satellite. Then we use HASM to simulate monthly XCO₂ distribution at the spatial resolution of 0.5°×0.5° based on OCO-2's observations from September 2014 to August 2015. Simultaneously, the corresponding TCCON (Total Carbon Column Observing Network) observations are used to verify HASM's simulations. The result shows that the mean absolute error between HASM simulations and TCCON observations is 0.81 ppm and the correlation coefficient between them is 0.88. Therefore, HASM has great advantages in simulating the spatial distribution of XCO₂ observed by OCO-2 satellite.

Key words: OCO-2, high accuracy surface modeling, spatial distribution of XCO₂, spatial interpolation, TCCON

张丽丽, 赵明伟, 赵娜, 岳天祥. 基于OCO-2卫星观测模拟高精度XCO₂的空间分布[J]. 地球信息科学学报, 2018, 20(9): 1316-1326.DOI:10.12082/DQXXKX.2018.170121

ZHANG Lili,ZHAO Mingwei,ZHAO Na,YUE Tianxiang. Modeling the Spatial Distribution of XCO₂ with High Accuracy Based on OCO-2's Observations[J]. Journal of Geo-information Science, 2018, 20(9): 1316-1326.DOI:10.12082/DQXXKX.2018.170121

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参考文献 26

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字段	下限(≥)	上限(≤)
Warn level	N/A	15
Outcome flag	N/A	2
Preprocessors/h2o_ratio	0.700	1.030
Preprocessors/co2_ratio	0.995	1.025
Preprocessors/dp_apb	-15.00	5.00
Retrieval/dp	-5.00	10.0
Retrieval/aod_ice	N/A	0.050
Reteieval/Aod_sulfate	N/A	0.400
Retrieval/Aod_dust	0.001	0.30
Retrieval/co₂_grad_del	-70.0	70.0
Retrieval/albedo_2	0.10	N/A
Blended albedo(2.4albedo_3-1.13albedo_1)	N_A	0.8
Dof_co2	1.8	N/A
Sounding/airmass	N/A	3.6

字段	下限(≥)	上限(≤)
Warn level	N/A	15
Outcome flag	N/A	2
Preprocessors/co2_ratio	0.994	1.020
Preprocessors/dp_apb	N/A	0.00
Retrieval/dp	-3.00	9.0
Retrieval/co2_grad_del	-30.0	5.0
Retrieval/albedo_slope_3*10^5	1.0	10.0
Retrieval/windspeed	2.0	N/A
Sounding/snr_weak_co2	380	N/A
Sounding/airmass	N/A	3.5

时间	个数	最小值/ppm	最大/ppm	均值/ppm	方差/ppm
2014-09	250 414	387.42	401.30	395.77	1.02
2014-10	34 356	391.60	401.01	395.53	0.89
2014-11	107 061	387.42	401.30	396.03	0.78
2014-12	380 597	390.13	403.80	396.45	0.84
2015-01	89 219	390.67	402.23	396.48	0.76
2015-02	147 529	390.13	403.02	396.34	0.96
2015-03	178 943	390.66	405.82	397.07	1.38
2015-04	47 624	392.88	404.49	397.31	1.38
2015-05	16 254	393.65	405.82	398.45	1.50
2015-06	73 722	388.68	405.83	398.59	1.55
2015-07	24 319	388.68	404.52	398.54	1.51
2015-08	28 152	390.42	405.83	398.18	1.41

基于OCO-2卫星观测模拟高精度XCO₂的空间分布

Modeling the Spatial Distribution of XCO₂ with High Accuracy Based on OCO-2's Observations

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