Quantitative bias estimates for tropospheric NO2 columns retrieved from SCIAMACHY, OMI, and GOME-2 using a common standard for East Asia
Quantitative bias estimates for tropospheric NO2 columns retrieved from SCIAMACHY, OMI, and GOME-2 using a common standard for East Asia
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DOI：
10.5194/amt-5-2403-2012
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发表时间：
2012-01-01
影响因子：
3.8
通讯作者：
Wang, Z. F.
中科院分区：
文献类型：
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作者：
Irie, H.;Boersma, K. F.;Wang, Z. F.
为了比较来自三种不同卫星传感器（SCIAMACHY、OMI和GOME-2）的对流层二氧化氮（NO2）垂直柱密度（VCD）数据，我们使用一个共同的标准来定量评估各自数据集的偏差。作为标准，采用2006-2011年日本和中国多个站点的地面多轴差分光学吸收光谱（MAX-DOAS）单组配置观测数据进行回归分析。各种空间符合准则的检验表明，回归曲线的斜率会受到NO2在考虑区域的空间分布的影响。在日本东京附近，随着MAX-DOAS与卫星观测点之间的距离，坡度呈系统变化，但这种系统依赖性并不明显，在中国站点的比较中，相关系数普遍较高。在这些结果的基础上，我们主要关注中国的比较，并估计SCIAMACHY、OMI和GOME-2数据（TM4NO2A和DOMINO版本2产品）与MAX-DOAS观测值的偏差分别为-5 +/- 14%、-10 +/- 14%和+ 1 +/- 14%，这些偏差都很小且不显著。我们建议，这些小偏差现在允许将这些卫星数据结合起来进行分析，以进行空气质量研究，这比以前更加系统和定量。
For the intercomparison of tropospheric nitrogen dioxide (NO2) vertical column density (VCD) data from three different satellite sensors (SCIAMACHY, OMI, and GOME-2), we use a common standard to quantitatively evaluate the biases for the respective data sets. As the standard, a regression analysis using a single set of collocated ground-based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) observations at several sites in Japan and China from 2006-2011 is adopted. Examinations of various spatial coincidence criteria indicates that the slope of the regression line can be influenced by the spatial distribution of NO2 over the area considered. While the slope varies systematically with the distance between the MAX-DOAS and satellite observation points around Tokyo in Japan, such a systematic dependence is not clearly seen and correlation coefficients are generally higher in comparisons at sites in China. On the basis of these results, we focus mainly on comparisons over China and estimate the biases in SCIAMACHY, OMI, and GOME-2 data (TM4NO2A and DOMINO version 2 products) against the MAX-DOAS observations to be -5 +/- 14 %, -10 +/- 14 %, and + 1 +/- 14 %, respectively, which are all small and insignificant. We suggest that these small biases now allow for analyses combining these satellite data for air quality studies, which are more systematic and quantitative than previously possible.