Near-real time retrieval of tropospheric NO2 from OMI

被引:361
|
作者
Boersma, K. F.
Eskes, H. J.
Veefkind, J. P.
Brinksma, E. J.
Van der A, R. J.
Sneep, M.
van den Oord, G. H. J.
Levelt, P. F.
Stammes, P.
Gleason, J. F.
Bucsela, E. J.
机构
[1] KNMI, De Bilt, Netherlands
[2] NASA, Goddard Space Flight Ctr, Greenbelt, MD 20771 USA
关键词
D O I
10.5194/acp-7-2103-2007
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
We present a new algorithm for the near-real time retrieval - within 3 h of the actual satellite measurement - of tropospheric NO2 columns from the Ozone Monitoring Instrument (OMI). The retrieval is based on the combined retrieval-assimilation-modelling approach developed at KNMI for off-line tropospheric NO2 from the GOME and SCIAMACHY satellite instruments. We have adapted the off-line system such that the required a priori information profile shapes and stratospheric background NO2 - is now immediately available upon arrival ( within 80 min of observation) of the OMI NO2 slant columns and cloud data at KNMI. Slant columns for NO2 are retrieved using differential optical absorption spectroscopy (DOAS) in the 405 465 nm range. Cloud fraction and cloud pressure are provided by a new cloud retrieval algorithm that uses the absorption of the O-2-O-2 collision complex near 477 nm. Online availability of stratospheric slant columns and NO2 profiles is achieved by running the TM4 chemistry transport model (CTM) forward in time based on forecast ECMWF meteo and assimilated NO2 information from all previously observed orbits. OMI NO2 slant columns, after correction for spurious across-track variability, show a random error for individual pixels of approximately 0.7 x 10(15) molec cm(-2). Cloud parameters from OMI's O-2-O-2 algorithm have similar frequency distributions as retrieved from SCIAMACHY's Fast Retrieval Scheme for Cloud Observables ( FRESCO) for August 2006. On average, OMI cloud fractions are higher by 0.011, and OMI cloud pressures exceed FRESCO cloud pressures by 60 hPa. A sequence of OMI observations over Europe in October 2005 shows OMI's capability to track changeable NOx air pollution from day to day in cloud-free situations.
引用
收藏
页码:2103 / 2118
页数:16
相关论文
共 50 条
  • [21] Near-real time foF2 predictions using neural networks
    Oyeyemi, Elijah O.
    McKinnell, L. A.
    Poole, A. W. V.
    JOURNAL OF ATMOSPHERIC AND SOLAR-TERRESTRIAL PHYSICS, 2006, 68 (16) : 1807 - 1818
  • [22] A high spatial resolution retrieval of NO2 column densities from OMI: method and evaluation
    Russell, A. R.
    Perring, A. E.
    Valin, L. C.
    Bucsela, E. J.
    Browne, E. C.
    Min, K-E
    Wooldridge, P. J.
    Cohen, R. C.
    ATMOSPHERIC CHEMISTRY AND PHYSICS, 2011, 11 (16) : 8543 - 8554
  • [23] A comparison of the impact of TROPOMI and OMI tropospheric NO2 on global chemical data assimilation
    Sekiya, Takashi
    Miyazaki, Kazuyuki
    Eskes, Henk
    Sudo, Kengo
    Takigawa, Masayuki
    Kanaya, Yugo
    ATMOSPHERIC MEASUREMENT TECHNIQUES, 2022, 15 (06) : 1703 - 1728
  • [24] Inversion Models for the Retrieval of Total and Tropospheric NO2 Columns
    Liu, Song
    ATMOSPHERE, 2019, 10 (10)
  • [25] NEAR-REAL TIME ESTIMATES OF LEAF AREA INDEX FROM AVHRR TIME SERIES DATA
    Kandasamy, S.
    Verger, A.
    Baret, F.
    Weiss, M.
    Buis, S.
    2012 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2012, : 6475 - 6478
  • [26] Intercomparison of SCIAMACHY and OMI tropospheric NO2 columns:: Observing the diurnal evolution of chemistry and emissions from space
    Boersma, K. Folkert
    Jacob, Daniel J.
    Eskes, Henk J.
    Pinder, Robert W.
    Wang, Jun
    van der A, Ronald J.
    JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2008, 113 (D16)
  • [27] Detection of Strong NOX Emissions from Fine-scale Reconstruction of the OMI Tropospheric NO2 Product
    Lee, Jae-Hyeong
    Lee, Sang-Hyun
    Kim, Hyun Cheol
    REMOTE SENSING, 2019, 11 (16)
  • [28] Changes in OMI tropospheric NO2 columns over Europe from 2004 to 2009 and the influence of meteorological variability
    Zhou, Yipin
    Brunner, Dominik
    Hueglin, Christoph
    Henne, Stephan
    Staehelin, Johannes
    ATMOSPHERIC ENVIRONMENT, 2012, 46 : 482 - 495
  • [29] Applicability of NGGM near-real time simulations in flood detection
    Purkhauser, A. F.
    Koch, J. A.
    Pail, R.
    JOURNAL OF GEODETIC SCIENCE, 2019, 9 (01) : 111 - 126
  • [30] A near-real time automatic MODIS data processing system
    Shutler, JD
    Smyth, TJ
    Land, PE
    Groom, SB
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2005, 26 (05) : 1049 - 1055