大气SO2柱总量遥感反演算法比较分析及验证

闫欢欢; 李晓静; 张兴赢; 王维和; 陈良富; 张美根; 徐晋

doi:10.7498/aps.65.084204

摘要

卫星遥感技术已成为城市污染气体SO2监测和全球火山活动监测及预警的重要手段. 目前新的PCA (principal component analysis)算法有效减小了反演数据噪声, 并替代之前业务算法BRD (band residual difference)用于边界层SO2柱总量产品的反演. 然而, 目前对PCA算法反演产品精度的评价和验证研究较少, 缺少与BRD算法产品进行长时间序列的比较以评估算法适用性, 尤其在中国大气污染重点城市区域. 本文利用地基多轴差分吸收光谱仪(MAX-DOAS)观测及多尺度空气质量模式系统(RAMS-CMAQ)大气化学模式模拟等数据, 评估PCA和BRD 反演算法的精度及误差. 另外, 选取洁净海洋地区、中国大气污染重点城市区域和高浓度火山喷发三种情况, 比较分析PCA 与BRD SO2 总量的时空格局变化差异及对不同SO2总量下的适用性, 并对两种算法反演不确定性进行分析讨论. 结果表明, 在中国京津冀、珠江三角洲和长江三角洲区域, PCA SO2总量反演值低于BRD, BRD反演结果更接近于地基的MAX-DOAS观测值, 冬季BRD和PCA SO2总量值低于RAMS-CMAQ 模拟结果, 夏季7月和8月BRD SO2总量值高于RAMS-CMAQ 模拟结果. 在SO2总量接近于0 值的洁净海洋地区, PCA 算法产品噪声水平低于BRD算法, 但PCA 反演结果整体偏差大于BRD算法. 在高浓度火山喷发情况下, 当SO2总量大于25 DU时BRD SO2总量反演值低于PCA, 且随着SO2 总量增大, 两种算法反演值差异亦增大. 该研究对于OMI (Ozone Monitering Instrument) SO2产品的应用具有重要的参考价值, 通过分析不同反演算法的差异及对其不确定性追因, 对于算法改进研究也具有重要的科学意义.

关键词:

Abstract

Remote sensing technology provides an unprecedented tool for the continuous and real-time monitoring of atmospheric SO2 from volcanic eruption and anthropogenic emission. The Global Ozone Monitoring Experiment (GOME), SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY), and Ozone Monitoring Instrument (OMI) have high SO2 monitoring capability. The OMI, which was launched on the EOS/Aura platform in July 2004, has the same hyperspectral measurements as the GOME and SCIAMACHY, but offers the improved spatial resolution at nadir (1324 km2) and daily global coverage for short-lifetime SO2. For OMI operational SO2 planetary boundary layer (PBL) retrieval, the previous band residual difference (BRD) algorithm has been replaced by principal component analysis (PCA) algorithm, which effectively reduces the systematic biases in SO2 column retrievals. However, there are few studies on the evaluations and validations of PCA SO2 retrievals over China, and the long-term comparisons with BRD SO2 retrievals also need to be conducted. In this study, the accuracies of PCA and BRD SO2 retrievals are validated by using ground-based multi axis differential optical absorption spectroscopy (MAX-DOAS) located in Beijing, and regional atmospheric modeling system, community multi-scale air quality (RAMS-CMAQ) modeling system model which can simulate the vertical distribution of atmospheric SO2. Moreover, BRD and PCA SO2 retrievals from oceanic area, eastern China and Reunion volcanic eruption are compared to find the long-term trend and spatiotemporal differences between SO2 columns. Finally, the uncertainty of SO2 retrieval, caused by measurement errors, band selection and input parameter errors in radiative transfer model, are analysed to understand the limitations of BRD and PCA algorithms. Results show that both PCA and BRD SO2 retrievals over Beijing are lower than ground-based MAX-DOAS measurements of SO2. PCA and BRD SO2 retrievals over eastern China are lower than the simulated SO2 columns from RAMS-CMAQ in winter 2008, but in July and August BRD SO2 columns are higher than RAMS-CMAQ simulations. The values of SO2 columns from BRD over China are more consistent with those from ground-based MAX-DOAS and RAMS-CMAQ model than from PCA. Although PCA algorithm effectively reduces the noise in SO2 column retrieval, SO2 columns from PCA over China are lower than those from BRD. For oceanic area where SO2 amount is nearly zero, the standard deviation of PCA results is lower than that of BRD, but the absolute value of averaged PCA SO2 column is larger than that of BRD. In the case of Reunion volcanic eruption with SO2 columns larger than 25 DU, the BRD SO2 columns are lower than PCA retrievals. Meanwhile, with the increase of SO2 column, the difference between BRD and PCA SO2 retrievals increases. Detailed uncertainty analysis shows the influences of measurement errors, band selection and inputs of radiative transfer model on the retrieval results. This study is important for developing the retrieval algorithm, and can also improve the application of OMI SO2 products.

Keywords:

作者及机构信息

1.
中国气象局国家卫星气象中心, 北京 100081;

2.
中国科学院遥感与数字地球研究所, 遥感科学国家重点实验室, 北京 100101;

3.
中国科学院大气物理研究所, 大气边界层物理和大气化学国家重点实验室, 北京 100029;

4.
中国科学院安徽光学精密机械研究所, 环境光学与技术重点实验室, 合肥 230031

通信作者: 李晓静, lixiaoj@cma.gov.cn

基金项目: 国家卫星气象中心青年人才基金项目、高分辨率对地观测系统重大专项气象应用示范项目(批准号: E310/1112)、公益气象行业专项(批准号: GYHY201106045)、欧盟FP7框架国际合作项目(批准号: 606719)和国家自然科学基金(批准号: 41501413)资助的课题.

Authors and contacts

1.
National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China;

2.
State Key Laboratory of the Science of Remote Sensing, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;

3.
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China;

4.
Key Laboratory of Environmental Optical and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei 230031, China

Corresponding author: Li Xiao-Jing, lixiaoj@cma.gov.cn

Funds: Project supported by the Young Science Fund from National Satellite Meteorological Center, High-resolution Earth Observation System, China (Grant No. E310/1112), the Special Scientific Research Fund of Meteorological Public Welfare Profession of China (Grant No. GYHY201106045), Partnership with China on Space Data (PANDA) (Grant No. 606719), National Natural Science Foundation of China (Grant No. 41501413).

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施引文献

[1]	Myhre G, Stordal F, Berglen T F, Sundet J K, Isaksen I S A 2004 J. Atmos. Sci. 61 485
[2]	Benkovitz C M, Schwartz S E, Jensen M P, Miller M A 2006 Atmos. Chem. Phys. 6 4723
[3]	Jernelov A 1983 Ambio 12 362
[4]	Larssen T, Lydersen E, Tang D G, He Y, Gao J X, Liu H Y, Duan L, Seip H M, Vogt R D, Mulder J, Shao M, Wang Y H, Shang H, Zhang X S, Solberg S, Aas W, Okland T, Eilertsen O, Angell V, Liu Q R, Zhao D W, Xiang R J, Xiao J S, Luo J H 2006 Environ. Sci. Technol. 40 418
[5]	Zhao D, Sun B 1986 Ambio 15 2
[6]	Haines A 2003 Int. J. Epidemiol. 32 321
[7]	Fishman J, Bowman K W, Burrows J P, Richter A, Chance K V, Edwards D P, Martin R V, Morris G A, Pierce R B, Ziemke J R, Al-Saadi J A, Creilson J K, Schaack T K, Thompson A M 2008 B. Am. Meteorol. Soc. 89 805
[8]	Martin R V 2008 Atmos. Environ. 42 7823
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[26]	Carn S A, Krueger A J, Arellano S, Krotkov N A, Yang K 2008 J. Volcanol. Geoth. Res. 176 141
[27]	Fioletov V, McLinden C, Krotkov N, Yang K, Loyola D, Valks P, Theys N, van Roozendael M, Nowlan C, Chance K 2013 J. Geophys. Res. Atmos. 118 11
[28]	Ialongo I, Hakkarainen J, Kivi R, Anttila P, Krotkov N, Yang K, Li C, Tukiainen S, Hassinen S, Tamminen J 2015 Atmos. Meas. Tech. 8 2279
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[30]	He H, Li C, Loughner C P, Li Z Q, Krotkov N A, Yang K, Wang L, Zheng Y F, Bao X D, Zhao G Q, Dickerson R R 2012 J. Geophys. Res. Atmos. 117
[31]	Cai Z, Liu Y, Wang P, Meng X, Xia J 2008 Asia-Pacific Remote Sensing Noumea, New Caledonia, November 18, 2008 p71520O
[32]	Wang T, Wang P C, Yu H, et al. 2013 Acta Phys. Sin. 62 054206 (in Chinese) [王婷, 王普才, 余环等 2013 62 054206]
[33]	Li C, Joiner J, Krotkov N A, Bhartia P K 2013 Geophys. Res. Lett. 40 6314
[34]	Fioletov V, McLinden C, Krotkov N, Li C 2015 Geophys. Res. Lett. 42 1969
[35]	Li C, Zhang Q, Krotkov N A, Streets D G, He K, Tsay S C, Gleason J F 2010 Geophys. Res. Lett. 37 292
[36]	Bogumil K, Orphal J, Homann T, Voigt S, Spietz P, Fleischmann O C, Vogel A, Hartmann M, Kromminga H, Bovensmann H, Frerick J, Burrows J P 2003 J. Photoch. Photobio. A 157 167
[37]	Dave J V 1964 Astrophys. J. 140 1292
[38]	Xu J, Xie P H, Si F Q, Li A, Dou K, Liu Y, Qin M, Wang M H, Zhang Y, Shi P 2009 J. Atmos. Environ. Opt. 4 347 (in Chinese) [徐晋, 谢品华, 司福棋, 李昂, 窦科, 刘宇, 秦敏, 王曼华, 章勇, 石鹏 2009 大气与环境光学学报 4 347]
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