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Preliminary retrieval of aerosol single scattering albedo in eastern China based on S5P/TROPOMI

  • role： First author第一作者
  • Affiliation：

    China University of Mining and Technology, School of Environment and Spatial Informatics, Xuzhou 221116, China

  • Email：lidinf@cumt.edu.cn
  • Introduction：李丁，研究方向为气溶胶卫星遥感。E-mail： lidinf@cumt.edu.cn
LI Ding1,  
  • role： Corresponding author通信作者
  • Affiliation：

    China University of Mining and Technology, School of Environment and Spatial Informatics, Xuzhou 221116, China

  • Email：qinkai@cumt.edu.cn
  • Introduction：秦凯，研究方向为大气环境遥感与应用。E-mail：qinkai@cumt.edu.cn
QIN Kai1*,  
  • Affiliation：

    China University of Mining and Technology, School of Environment and Spatial Informatics, Xuzhou 221116, China

XUE Yong1,  
  • Affiliation：

    German Aerospace Center (DLR), Weßling 82234, Germany

RAO Lanlan2,  
  • Affiliation：

    China University of Mining and Technology, School of Environment and Spatial Informatics, Xuzhou 221116, China

ZHANG Yishu1,  
  • Affiliation：

    China University of Mining and Technology, School of Environment and Spatial Informatics, Xuzhou 221116, China

HE Qing1

Resümee

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 (R2) 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.

Schlüsselwort

remote sensing;TROPOMI;MODIS;SSA;absorbing aerosol;UVAI

References

  1. 1.
    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
  2. 2.
    Chen B. 2012. Detection Lighe-Absorbing Aerosols and Their Properties from Satellite and AERONET Observations Over East Asia. Lanzhou: Lanzhou University
  3. 3.
    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
  4. 4.
    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
  5. 5.
    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
  6. 6.
    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
  7. 7.
    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
  8. 8.
    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
  9. 9.
    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
  10. 10.
    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
  11. 11.
    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
  12. 12.
    He K B. 2018. Regional cooperation mechanism of air pollution prevention and control in China. Institutional Reform and Management in China, (1): 39-41
  13. 13.
    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
  14. 14.
    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
  15. 15.
    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
  16. 16.
    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
  17. 17.
    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
  18. 18.
    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
  19. 19.
    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
  20. 20.
    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
  21. 21.
    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
  22. 22.
    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
  23. 23.
    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
  24. 24.
    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
  25. 25.
    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
  26. 26.
    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
  27. 27.
    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
  28. 28.
    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
  29. 29.
    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
  30. 30.
    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
  31. 31.
    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
  32. 32.
    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
  33. 33.
    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
  34. 34.
    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
  35. 35.
    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
  36. 36.
    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
  37. 37.
    Royal Netherlands Meteorological Institute (KNMI). 2018. TROPOMI ATBD of the UV aerosol index[EB/OL]. [2020-04-27].
  38. 38.
    Royal Netherlands Meteorological Institute (KNMI). 2019. TROPOMI ATBD of the aerosol layer height[EB/OL]. [2020-04-27].
  39. 39.
    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
  40. 40.
    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
  41. 41.
    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
  42. 42.
    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
  43. 43.
    Stammes P. 2002. OMI algorithm theoretical basis document volume III: clouds, aerosols, and surface UV irradiance[EB/OL]. [2020-04-27].
  44. 44.
    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
  45. 45.
    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
  46. 46.
    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
  47. 47.
    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
  48. 48.
    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
  49. 49.
    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
  50. 50.
    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
  51. 51.
    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
  52. 52.
    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
  53. 53.
    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
  54. 54.
    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
  55. 55.
    Wu D. 2011. Formation and evolution of haze weather. Environmental Science and Technology, 34(3): 157-161
  56. 56.
    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
  57. 57.
    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
  58. 58.
    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
  59. 59.
    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
  60. 60.
    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
  61. 61.
    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
  62. 62.
    Zhang X Y. 2007. Aerosol over China and their climate effect. Advances in Earth Science, 22(1): 12-16

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