浏览全部资源
扫码关注微信
Published： 07 May 2022 ，
扫 描 看 全 文
李丁，秦凯，薛勇，饶兰兰，张亦舒，何秦.2022.基于S5P/TROPOMI的中国东部气溶胶单次散射反照率反演初探.遥感学报，26（5）： 897-912
Li D，Qin K，Xue Y，Rao L L，Zhang Y S and He Q. 2022. Preliminary retrieval of aerosol single scattering albedo in eastern China based on S5P/TROPOMI. National Remote Sensing Bulletin， 26（5）：897-912
气溶胶单次散射反照率SSA（Single Scattering Albedo）的卫星定量遥感对气候评估和大气污染治理均具有重要意义。搭载于S5P（Sentinel-5 Precursor）上的对流层监测仪（TROPOMI）具有目前同类卫星传感器中最优的空间分辨率。本文基于S5P/TROPOMI数据开展了中国东部地区的SSA反演研究。首先利用中国东部地区AERONET（Aerosol Robotic Network）站点数据对OPAC（Optical Properties of Aerosols and Clouds）气溶胶模型进行约束改进，构建了更为合适的气溶胶类型，并使用地基激光雷达（Lidar）预设相应气溶胶类型的垂直结构。然后使用辐射传输模型SCIATRAN构建查找表LUT（Look-Up Table），将TROPOMI UVAI（Ultraviolet Absorbing Index）和MODIS AOD（Aerosol Optical Depth）数据联合输入反演气溶胶SSA数据。反演结果与地基站点数据对比，相关系数
R
2
为0.61，均方根误差为0.05；和OMI SSA产品相比，总体趋势一致且具有空间连续性更好。基于TROPOMI的高分辨率SSA算法和数据将有助于中小尺度下气溶胶时空分布、光学特性等研究。
Quantitative retrieval of aerosol Single Scattering Albedo (SSA) from satellite remote sensing is important for climate assessment and air pollution control. This study developed a preliminary SSA retrieval algorithm based on S5p/TROPOMI and Aqua/MODIS in eastern China.
The optical absorption of aerosol is highly sensitive in the near-ultraviolet bands
which is significantly correlated with the aerosol model
vertical profile
and corresponding aerosol loading. Considering the actual situation of aerosols in eastern China
the Optical Properties of Aerosols and Clouds aerosol model is constrained using AERONET data in eastern China to produce a more suitable aerosol type
and the corresponding vertical structure of different aerosol types is predefined using ground-based Lidar. Then
the radiative transfer model SCIATRAN is used for sensitivity analysis to further adjust the aerosol model and establish a series of look-up tables (LUTs) for different aerosol subtypes. The SSA can retrieve individual LUT at pixels with the collection of TROPOMI Ultraviolet Absorbing Index (UVAI) and MODIS AOD together. In the process
the Ångström Index values from MODIS and UVAI are used in combination for preliminary classification of aerosol type to improve the accuracy and efficiency.
Compared with the ground-based observations
the coefficient of determination (
R
2
) is 0.61
and the root mean square error is 0.05. Compared with OMI instantaneous inversion and monthly average SSA images
the distributions of TROPOMI SSA show a consistent overall trend and have better spatial continuity and larger inter-pixel variation. Further site-by-site analysis shows that the SSA and AOD are highly correlated with the type of aerosols where the site is located. The SSA in Shanghai with more sea salt aerosols is stable above 0.95
while the Beijing area is affected by multiple factors and the SSA varies greatly with time series from 0.85 to 0.98.
In conclusion
the preliminary SSA retrieval algorithm based on TROPOMI in this study has good verification accuracy. Using MODIS aerosol products as input can effectively improve the accuracy of SSA inversion. The algorithm still has some uncertainties and needs to be further improved from the aspects of aerosol type and aerosol vertical profile. Nevertheless
the algorithm is helpful for the classification of aerosol types
aerosol microphysical
and optical properties of aerosol in small- and medium-scale regions.
遥感TROPOMIMODIS单次散射反照率吸收性气溶胶紫外气溶胶吸收指数
remote sensingTROPOMIMODISSSAabsorbing aerosolUVAI
Bilal M, Nichol J E and Nazeer M. 2016. Validation of Aqua-MODIS C051 and C006 operational aerosol products using AERONET measurements over Pakistan. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(5): 2074-2080 [DOI: 10.1109/JSTARS.2015.2481460http://dx.doi.org/10.1109/JSTARS.2015.2481460]
Chen B. 2012. Detection Lighe-Absorbing Aerosols and Their Properties from Satellite and AERONET Observations Over East Asia. Lanzhou: Lanzhou University
陈斌. 2012. 利用卫星和AERONET观测资料对东亚地区吸收性气溶胶识别及其光学特征分析. 兰州: 兰州大学
Chen L F, Li S S, Tao J H and Wang Z T. 2011. Research and Application of Aerosol Remote Sensing Quantitative Retrieval. Beijing: Science Press
陈良富, 李莘莘, 陶金花, 王中挺. 2011. 气溶胶遥感定量反演研究与应用. 北京: 科学出版社
Dave J V. 1978. Effect of aerosols on the estimation of total ozone in an atmospheric column from the measurements of its ultraviolet radiance. Journal of the Atmospheric Sciences, 35(5): 899-911 [DOI: 10.1175/1520-0469(1978)035<0899:EOAOTE>2.0.CO;2http://dx.doi.org/10.1175/1520-0469(1978)035<0899:EOAOTE>2.0.CO;2]
Dubovik O, Holben B, Eck T F, Smirnov A, Kaufman Y J, King M D, Tanré D and Slutsker I. 2002. Variability of absorption and optical properties of key aerosol types observed in worldwide locations. Journal of the Atmospheric Sciences, 59(3): 590-608 [DOI: 10.1175/1520-0469(2002)059<0590:VOAAOP>2.0.CO;2http://dx.doi.org/10.1175/1520-0469(2002)059<0590:VOAAOP>2.0.CO;2]
Eswaran K, Satheesh S K and Srinivasan J. 2019. Multi-satellite retrieval of single scattering albedo using the OMI-MODIS algorithm. Atmospheric Chemistry and Physics, 19(5): 3307-3324 [DOI: 10.5194/acp-19-3307-2019http://dx.doi.org/10.5194/acp-19-3307-2019]
Fan W Z, Qin K, Xu J, Yuan L M, Li D, Jin Z and Zhang K F. 2019. Aerosol vertical distribution and sources estimation at a site of the Yangtze River Delta region of China. Atmospheric Research, 217: 128-136 [DOI: 10.1016/j.atmosres.2018.11.002http://dx.doi.org/10.1016/j.atmosres.2018.11.002]
Guo J P, Liu H, Wang F, Huang J F, Xia F, Lou M Y, Wu Y R, Jiang J H, Xie T, Zhaxi Y and Yung Y L. 2016. Three-dimensional structure of aerosol in China: a perspective from multi-satellite observations. Atmospheric Research, 178-179: 580-589 [DOI: 10.1016/j.atmosres.2016.05.010http://dx.doi.org/10.1016/j.atmosres.2016.05.010]
Hammer M S, Martin R V, van Donkelaar A, Buchard V, Torres O, Ridley D A and Spurr R J D. 2015. Interpreting the Ultraviolet Aerosol Index observed with the OMI satellite instrument to understand absorption by organic aerosols: implications for atmospheric oxidation and direct radiative effects. Atmospheric Chemistry and Physics, 15(19): 27405-27447 [DOI: 10.5194/acpd-15-27405-2015http://dx.doi.org/10.5194/acpd-15-27405-2015]
Hammer M S, Martin R V, van Donkelaar A, Buchard V, Torres O, Ridley D A and Spurr R J D. 2016. Interpreting the ultraviolet aerosol index observed with the OMI satellite instrument to understand absorption by organic aerosols: implications for atmospheric oxidation and direct radiative effects. Atmospheric Chemistry and Physics, 16(4): 2507-2523 [DOI: 10.5194/acp-16-2507-2016http://dx.doi.org/10.5194/acp-16-2507-2016]
He H, Wang X M, Wang Y S, Wang Z F, Liu J G and Chen Y F. 2013. Formation mechanism and control strategies of haze in China. Bulletin of Chinese Academy of Sciences, 28(3): 344-352
贺泓, 王新明, 王跃思, 王自发, 刘建国, 陈运法. 2013. 大气灰霾追因与控制. 中国科学院院刊, 28(3): 344-352 [DOI: 10.3969/j.issn.1000-3045.2013.03.008http://dx.doi.org/10.3969/j.issn.1000-3045.2013.03.008]
He K B. 2018. Regional cooperation mechanism of air pollution prevention and control in China. Institutional Reform and Management in China, (1): 39-41
贺克斌. 2018. 我国大气污染防治区域协作机制. 中国机构改革与管理, (1): 39-41 [DOI: 10.3969/j.issn.2095-1507.2018.01.014http://dx.doi.org/10.3969/j.issn.2095-1507.2018.01.014]
Herman J R, Bhartia P K, Torres O, Hsu C, Seftor C and Celarier E. 1997. Global distribution of UV-absorbing aerosols from Nimbus 7/TOMS data. Journal of Geophysical Research: Atmospheres, 102(D14): 16911-16922 [DOI: 10.1029/96jd03680http://dx.doi.org/10.1029/96jd03680]
Hess M, Koepke P and Schult I. 1998. Optical properties of aerosols and clouds: the software package OPAC. Bulletin of the American Meteorological Society, 79(5): 831-844 [DOI: 10.1175/1520-0477(1998)079<0831:OPOAAC>2.0.CO;2http://dx.doi.org/10.1175/1520-0477(1998)079<0831:OPOAAC>2.0.CO;2]
Holben B, Slutsker I, Giles D, Eck T, Smirnov A, Sinyuk A, Schafer J, Sorokin K, Rodriguez J, Kraft J and Scully A. 2016. AERONET Version 3 Release: Providing Significant Improvements for Multi-Decadal Global Aerosol Database and Near Real-Time Validation. NASA
IPCC. 2014. Climate Change 2013: the Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press
Jeong M J and Hsu N C. 2008. Retrievals of aerosol single-scattering albedo and effective aerosol layer height for biomass-burning smoke: synergy derived from “A-Train” sensors. Geophysical Research Letters, 35(24): L24801 [DOI: 10.1029/2008GL036279http://dx.doi.org/10.1029/2008GL036279]
Jeong U, Kim J, Ahn C, Torres O, Liu X, Bhartia P K, Spurr R J D, Haffner D, Chance K and Holben B N. 2016. An optimal-estimation-based aerosol retrieval algorithm using OMI near-UV observations. Atmospheric Chemistry and Physics, 16(1): 177-193 [DOI: 10.5194/acp-16-177-2016http://dx.doi.org/10.5194/acp-16-177-2016]
Jethva H, Torres O and Ahn C. 2014. Global assessment of OMI aerosol single-scattering albedo using ground-based AERONET inversion. Journal of Geophysical Research: Atmospheres, 119(14): 9020-9040 [DOI: 10.1002/2014JD021672http://dx.doi.org/10.1002/2014JD021672]
Jia C, Sun L and Chen Y F, Zhang X K, Wang W Y and Wang Y J. 2020. Inversion of aerosol optical depth for Landsat 8 OLI data using deep belief network. Journal of Remote Sensing, 24(10): 1180-1192
贾臣, 孙林, 陈允芳, 张熙空, 王伟燕, 王永吉. 2020. 深度置信网络算法反演Landsat 8 OLI气溶胶光学厚度. 遥感学报, 24(10): 1180-1192 [DOI: 10.11834/jrs.20200048http://dx.doi.org/10.11834/jrs.20200048]
Kang Y, Wang L L, Xin J Y, Tao M H, Song T, Gong C S, Wang Y S and Li G. 2019. Analysis of the change trend of aerosol single-scattering albedo in the Areas of Northern China Based on AERONET and OMI Data. Climatic and Environmental Research, 24(5): 537-551
康颖, 王莉莉, 辛金元, 陶明辉, 宋涛, 巩崇水, 王跃思, 李广. 2019. 基于AERONET和OMI数据分析中国北方典型地区气溶胶单次散射反照率的变化趋势. 气候与环境研究, 24(5): 537-551 [DOI: 10.3878/j.issn.1006-9585.2018.18006http://dx.doi.org/10.3878/j.issn.1006-9585.2018.18006]
Lee J, Hsu N C, Bettenhausen C, Sayer A M, Seftor C J and Jeong M J. 2015. Retrieving the height of smoke and dust aerosols by synergistic use of VIIRS, OMPS, and CALIOP observations. Journal of Geophysical Research: Atmospheres, 120(16): 8372-8388 [DOI: 10.1002/2015JD023567http://dx.doi.org/10.1002/2015JD023567]
Levelt P F, Joiner J, Tamminen J, Veefkind J P, Bhartia P K, Stein Zweers D C, Duncan B N, Streets D G, Eskes H, van der A R, McLinden C, Fioletov V, Carn S, de Laat J, DeLand M, Marchenko S, McPeters R, Ziemke J, Fu D J, Liu X, Pickering K, Apituley A, González Abad G, Arola A, Boersma F, Chan Miller C, Chance K, de Graaf M, Hakkarainen J, Hassinen S, Ialongo I, Kleipool Q, Krotkov N, Li C, Lamsal L, Newman P, Nowlan C, Suleiman R, Tilstra L G, Torres O, Wang H Q and Wargan K. 2018. The Ozone Monitoring Instrument: overview of 14 years in space. Atmospheric Chemistry and Physics, 18(8): 5699-5745 [DOI: 10.5194/acp-18-5699-2018http://dx.doi.org/10.5194/acp-18-5699-2018]
Levy R C, Mattoo S, Munchak L A, Remer L A, Sayer A M, Patadia F and Hsu N C. 2013. The Collection 6 MODIS aerosol products over land and ocean. Atmospheric Measurement Techniques, 6(11): 2989-3034 [DOI: 10.5194/amt-6-2989-2013http://dx.doi.org/10.5194/amt-6-2989-2013]
Li D. 2020. Remote Sensing Retrieval of AOD/SSA Based on AHI/TROPOMI——A Case Study of Eastern China. Xuzhou: China University of Mining and Technology
李丁. 2020. 基于AHI/TROPOMI的气溶胶AOD/SSA遥感反演——以中国东部地区为例. 徐州: 中国矿业大学 [DOI: 10.27623/d.cnki.gzkyu.2020.000659http://dx.doi.org/10.27623/d.cnki.gzkyu.2020.000659]
Li D, Qin K, Wu L X, Xu J, Letu H, Zou B, He Q and Li Y F. 2019a. Evaluation of JAXA himawari-8-AHI level-3 aerosol products over eastern China. Atmosphere, 10(4): 215 [DOI: 10.3390/atmos10040215http://dx.doi.org/10.3390/atmos10040215]
Li F S, Ju T Z, Jia W P, Chang F, Cheng H and Xie S T. 2018. Temporal and spatial distribution of absorbing aerosols index in Lanzhou based on remote sensing data. Acta Scientiae Circumstantiae, 38(12): 4582-4591
李逢帅, 巨天珍, 贾维平, 常锋, 程虎, 谢顺涛. 2018. 基于OMI数据的兰州地区吸收性气溶胶指数时空分布特征研究. 环境科学学报, 38(12): 4582-4591 [DOI: 10.13671/j.hjkxxb.2018.0348http://dx.doi.org/10.13671/j.hjkxxb.2018.0348]
Li Z Q, Wang Y, Guo J P, Zhao C F, Cribb M C, Dong X Q, Fan J W, Gong D Y, Huang J P, Jiang M J, Jiang Y Q, Lee S S, Li H, Li J M, Liu J J, Qian Y, Rosenfeld D, Shan S Y, Sun Y L, Wang H J, Xin J Y, Yan X, Yang X, Yang X Q, Zhang F and Zheng Y T. 2019b. East Asian study of tropospheric aerosols and their impact on regional clouds, precipitation, and climate (EAST-AIRCPC). Journal of Geophysical Research: Atmospheres, 124(23): 13026-13054 [DOI: 10.1029/2019JD030758http://dx.doi.org/10.1029/2019JD030758]
Li Z Q, Xie Y S, Zhang Y, Li L, Xu H, Li K T and Li D H. 2019. Advance in the remote sensing of atmospheric aerosol composition. Journal of Remote Sensing, 23(3): 359-373
李正强, 谢一凇, 张莹, 李雷, 许华, 李凯涛, 李东辉. 2019. 大气气溶胶成分遥感研究进展. 遥感学报, 23(3): 359-373 [DOI: 10.11834/jrs.20198185http://dx.doi.org/10.11834/jrs.20198185]
Liu B M, Ma Y Y, Guo J P, Gong W, Zhang Y, Mao F Y, Li J, Guo X R and Shi Y F. 2019. Boundary layer heights as derived from ground-based radar wind profiler in Beijing. IEEE Transactions on Geoscience and Remote Sensing, 57(10): 8095-8104 [DOI: 10.1109/TGRS.2019.2918301http://dx.doi.org/10.1109/TGRS.2019.2918301]
Liu J Y, Ren C H, Huang X, Nie W, Wang J P, Sun P, Chi X G and Ding A J. 2020. Increased aerosol extinction efficiency hinders visibility improvement in eastern China. Geophysical Research Letters, 47(20): e2020GL090167 [DOI: 10.1029/2020GL090167http://dx.doi.org/10.1029/2020GL090167]
Mei L L, Zhao C X, de Leeuw G, Burrows J P, Rozanov V, Che H Z, Vountas M, Ladstätter-weißenmayer A and Zhang X Y. 2019a. A critical evaluation of deep blue algorithm derived AVHRR aerosol product over China. Journal of Geophysical Research: Atmospheres, 124(22): 12173-12193 [DOI: 10.1029/2018JD029929http://dx.doi.org/10.1029/2018JD029929]
Mei L L, Zhao C X, de Leeuw G, Che H Z, Che Y H, Rozanov V, Vountas M and Burrows J P. 2019b. Understanding MODIS dark-target collection 5 and 6 aerosol data over China: effect of surface type, aerosol loading and aerosol absorption. Atmospheric Research, 228: 161-175 [DOI: 10.1016/j.atmosres.2019.05.023http://dx.doi.org/10.1016/j.atmosres.2019.05.023]
Molteni F, Buizza R, Palmer T N and Petroliagis T. 1996. The ECMWF ensemble prediction system: methodology and validation. Quarterly Journal of the Royal Meteorological Society, 122(529): 73-119 [DOI: 10.1002/qj.49712252905http://dx.doi.org/10.1002/qj.49712252905]
Qin K, Wang L Y, Xu J, Letu H, Zhang K F, Li D, Zou J H and Fan W Z. 2018. Haze optical properties from long-term ground-based remote sensing over Beijing and Xuzhou, China. Remote Sensing, 10(4): 518 [DOI: 10.3390/rs10040518http://dx.doi.org/10.3390/rs10040518]
Qin K, Wu L X, Wong M S, Letu H, Hu M Y, Lang H M, Sheng S J, Teng J Y, Xiao X and Yuan L M. 2016. Trans-boundary aerosol transport during a winter haze episode in China revealed by ground-based Lidar and CALIPSO satellite. Atmospheric Environment, 141: 20-29 [DOI: 10.1016/j.atmosenv.2016.06.042http://dx.doi.org/10.1016/j.atmosenv.2016.06.042]
Royal Netherlands Meteorological Institute (KNMI). 2018. TROPOMI ATBD of the UV aerosol index[EB/OL]. [2020-04-27]. http://www.TROPOMI.eu/sites/default/files/files/S5P-KNMI-L2-0008-RP-TROPOMI_ATBD_UVAI-1.1.0-20180615_signed.pdfhttp://www.TROPOMI.eu/sites/default/files/files/S5P-KNMI-L2-0008-RP-TROPOMI_ATBD_UVAI-1.1.0-20180615_signed.pdf
Royal Netherlands Meteorological Institute (KNMI). 2019. TROPOMI ATBD of the aerosol layer height[EB/OL]. [2020-04-27]. http://www.tropomi.eu/sites/default/files/files/publicSentinel-5P-TROPOMI-ATBD-Aerosol-Height.pdfhttp://www.tropomi.eu/sites/default/files/files/publicSentinel-5P-TROPOMI-ATBD-Aerosol-Height.pdf
Rozanov A, Rozanov V, Buchwitz M, Kokhanovsky A and Burrows J P. 2005. SCIATRAN 2.0-A new radiative transfer model for geophysical applications in the 175-2400 nm spectral region. Advances in Space Research, 36(5): 1015-1019 [DOI: 10.1016/j.asr.2005.03.012http://dx.doi.org/10.1016/j.asr.2005.03.012]
Satheesh S K, Torres O, Remer L A, Babu S S, Vinoj V, Eck T F, Kleidman R G and Holben B N. 2009. Improved assessment of aerosol absorption using OMI-MODIS joint retrieval. Journal of Geophysical Research: Atmospheres, 114(D5): D05209 [DOI: 10.1029/2008JD011024http://dx.doi.org/10.1029/2008JD011024]
She L, Xue Y, Yang X H, Leys J, Guang J, Che Y H, Fan C, Xie Y Q and Li Y. 2019. Joint retrieval of aerosol optical depth and surface reflectance over land using geostationary satellite data. IEEE Transactions on Geoscience and Remote Sensing, 57(3): 1489-1501 [DOI: 10.1109/TGRS.2018.2867000http://dx.doi.org/10.1109/TGRS.2018.2867000]
Smirnov A, Holben B N, Eck T F, Dubovik O and Slutsker I. 2000. Cloud-screening and quality control algorithms for the AERONET database. Remote Sensing of Environment, 73(3): 337-349 [DOI: 10.1016/s0034-4257(00)00109-7http://dx.doi.org/10.1016/s0034-4257(00)00109-7]
Stammes P. 2002. OMI algorithm theoretical basis document volume III: clouds, aerosols, and surface UV irradiance[EB/OL]. [2020-04-27]. https://eospso.gsfc.nasa.gov/sites/default/files/atbd/ATBD-OMI-03.pdfhttps://eospso.gsfc.nasa.gov/sites/default/files/atbd/ATBD-OMI-03.pdf
Stein Zweers D, Sneep M, Tilstra G, Stammes P, de Graaf M and Veefkind P. 2018. First results of the TROPOMI UV Aerosol Index compared to the OMI Aerosol Index//20th EGU General Assembly, EGU2018. Vienna: EGU : 7422
Su X, Wang L C, Zhang M, Qin W M and Bilal M. 2021. A High-Precision Aerosol Retrieval Algorithm (HiPARA) for Advanced Himawari Imager (AHI) data: development and verification. Remote Sensing of Environment, 253: 112221 [DOI: 10.1016/j.rse.2020.112221http://dx.doi.org/10.1016/j.rse.2020.112221]
Sun J Y T, Veefkind P, Nanda S, van Velthoven P and Levelt P. 2019. The role of aerosol layer height in quantifying aerosol absorption from ultraviolet satellite observations. Atmospheric Measurement Techniques, 12(12): 6319-6340 [DOI: 10.5194/amt-12-6319-2019http://dx.doi.org/10.5194/amt-12-6319-2019]
Sun Z B, Cheng X F and Xia X G. 2021. Spatial-temporaldistribution and impact factors of aerosol optical depth over China. China Environmental Science, 41(10): 4466-4475
孙忠保, 程先富, 夏晓圣. 2021. 中国气溶胶光学厚度的时空分布及影响因素分析. 中国环境科学, 41(10): 4466-4475 [DOI: 10.19674/j.cnki.issn1000-6923.20210705.001http://dx.doi.org/10.19674/j.cnki.issn1000-6923.20210705.001]
Torres O, Ahn C and Chen Z. 2013. Improvements to the OMI near-UV aerosol algorithm using A-train CALIOP and AIRS observations. Atmospheric Measurement Techniques, 6(11): 3257-3270 [DOI: 10.5194/amt-6-3257-2013http://dx.doi.org/10.5194/amt-6-3257-2013]
Torres O, Bhartia P K, Herman J R, Ahmad Z and Gleason J. 1998. Derivation of aerosol properties from satellite measurements of backscattered ultraviolet radiation: theoretical basis. Journal of Geophysical Research: Atmospheres, 103(D14): 17099-17110 [DOI: 10.1029/98jd00900http://dx.doi.org/10.1029/98jd00900]
Torres O, Jethva H, Ahn C, Jaross G and Loyola D G. 2020. TROPOMI aerosol products: evaluation and observations of synoptic-scale carbonaceous aerosol plumes during 2018-2020. Atmospheric Measurement Techniques, 13(12): 6789-6806 [DOI: 10.5194/amt-13-6789-2020http://dx.doi.org/10.5194/amt-13-6789-2020]
Torres O, Tanskanen A, Veihelmann B, Ahn C, Braak R, Bhartia P K, Veefkind P and Levelt P. 2007. Aerosols and surface UV products from ozone monitoring instrument observations: an overview. Journal of Geophysical Research: Atmospheres, 112(D24): D24S47 [DOI: 10.1029/2007JD008809http://dx.doi.org/10.1029/2007JD008809]
Veefkind J P, Aben I, McMullan K, Förster H, de Vries J, Otter G, Claas J, Eskes H J, de Haan J F, Kleipool Q, van Weele M, Hasekamp O, Hoogeveen R, Landgraf J, Snel R, Tol P, Ingmann P, Voors R, Kruizinga B, Vink R, Visser H and Levelt P F. 2012. TROPOMI on the ESA Sentinel-5 Precursor: a GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications. Remote Sensing of Environment, 120: 70-83 [DOI: 10.1016/j.rse.2011.09.027http://dx.doi.org/10.1016/j.rse.2011.09.027]
Wang W, Pan Z X, Mao F Y, Gong W and Shen L J. 2017. Evaluation of VIIRS land aerosol model selection with AERONET measurements. International Journal of Environmental Research and Public Health, 14(9): 1016 [DOI: 10.3390/ijerph14091016http://dx.doi.org/10.3390/ijerph14091016]
Wei J, Li Z Q, Peng Y R and Sun L. 2019. MODIS collection 6.1 aerosol optical depth products over land and ocean: validation and comparison. Atmospheric Environment, 201: 428-440 [DOI: 10.1016/j.atmosenv.2018.12.004http://dx.doi.org/10.1016/j.atmosenv.2018.12.004]
Wu D. 2011. Formation and evolution of haze weather. Environmental Science and Technology, 34(3): 157-161
吴兑. 2011. 灰霾天气的形成与演化. 环境科学与技术, 34(3): 157-161 [DOI: 10.3969/j.issn.1003-6504.2011.03.036http://dx.doi.org/10.3969/j.issn.1003-6504.2011.03.036]
Xie Y S, Li Z Q, Li D H, Xu H and Li K T. 2015. Aerosol optical and microphysical properties of four typical sites of SONET in China based on remote sensing measurements. Remote Sensing, 7(8): 9928-9953 [DOI: 10.3390/rs70809928http://dx.doi.org/10.3390/rs70809928]
Xue Y, He X W, de Leeuw G, Mei L L, Che Y H, Rippin W, Guang J and Hu Y C. 2017. Long-time series aerosol optical depth retrieval from AVHRR data over land in North China and Central Europe. Remote Sensing of Environment, 198: 471-489 [DOI: 10.1016/j.rse.2017.06.036http://dx.doi.org/10.1016/j.rse.2017.06.036]
Yan P, Liu G Q, Zhou X J, Wang J L, Tang J, Liu Q, Wang Z F and Zhou H G. 2010. Characteristics of aerosol optical properties during haze and fog episodes at Shangdianzi in northern China. Journal of Applied Meteorological Science, 21(3): 257-265
颜鹏, 刘桂清, 周秀骥, 王京丽, 汤洁, 刘强, 王振发, 周怀刚. 2010. 上甸子秋冬季雾霾期间气溶胶光学特性. 应用气象学报, 21(3): 257-265 [DOI: 10.3969/j.issn.1001-7313.2010.03.001http://dx.doi.org/10.3969/j.issn.1001-7313.2010.03.001]
Yan S M, Wang Y, Zhang Y J, Gao X A, Wang S M, Dong J and Liu Z D. 2020. Aerosol transmission characteristics of spring in Wutai mountain. China Environmental Science, 40(2): 497-505
闫世明, 王雁, 张岳军, 高兴艾, 王淑敏, 董剑, 刘正东. 2020. 五台山春季气溶胶传输特征. 中国环境科学, 40(2): 497-505 [DOI: 10.19674/j.cnki.issn1000-6923.2020.0104http://dx.doi.org/10.19674/j.cnki.issn1000-6923.2020.0104]
Yang D X, Liu Y, Xia J R and Wang P C. 2012. Measurements of aerosol optical properties over North China and its surrounding areas in autumn by satellite and ground based remote sensing. Climatic and Environmental Research, 17(4): 422-432
杨东旭, 刘毅, 夏俊荣, 王普才. 2012. 华北及其周边地区秋季气溶胶光学性质的星载和地基遥感观测. 气候与环境研究, 17(4): 422-432 [DOI: 10.3878/j.issn.1006-9585.2012.10149http://dx.doi.org/10.3878/j.issn.1006-9585.2012.10149]
Zhang L, Sun J Y, Shen X J, Zhang Y M, Che H, Ma Q L, Zhang Y W, Zhang X Y and Ogren J A. 2015. Observations of relative humidity effects on aerosol light scattering in the Yangtze River Delta of China. Atmospheric Chemistry and Physics, 15(14): 8439-8454 [DOI: 10.5194/acp-15-8439-2015http://dx.doi.org/10.5194/acp-15-8439-2015]
Zhang X Y. 2007. Aerosol over China and their climate effect. Advances in Earth Science, 22(1): 12-16
张小曳. 2007. 中国大气气溶胶及其气候效应的研究. 地球科学进展, 22(1): 12-16 [DOI: 10.3321/j.issn:1001-8166.2007.01.002http://dx.doi.org/10.3321/j.issn:1001-8166.2007.01.002]
相关文章
相关作者
相关机构
京公网安备11010802024621