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Nitric oxide (NO) data set (60--160 km) from SCIAMACHY nominal limb scans

Bender, Stefan; Sinnhuber, Miriam; Burrows, John P.; Langowski, Martin


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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.804371", 
  "title": "Nitric oxide (NO) data set (60--160 km) from SCIAMACHY nominal limb scans", 
  "issued": {
    "date-parts": [
      [
        2017, 
        6, 
        12
      ]
    ]
  }, 
  "abstract": "<p><strong>Overview</strong><br>\nContains the nitric oxide (NO) number densities (in cm<sup>-3</sup>) from 60 km to 160 km retrieved from SCIAMACHY nominal (~0--90 km) limb scans.</p>\n\n<p>SCIAMACHY is a UV-visible-near-infrared spectrometer which flies on ESA's Envisat and was operational from 08/2002 to 04/2012 (see Burrows et al., 1995 and Bovensmann et al., 1999 and references therein). The nominal limb mode was carried out daily (apart from outages and a few days dedicated to other measurement modes) from 08/2002 until the end of the mission. The limb scans were performed from ground to about 90 km tangent altitude, and the retrieval was performed on a 2.5\u00b0 x 2 km latitude--altitude grid from 90\u00b0S--90\u00b0N and from 60 km--160 km. This data set comprises all SCIAMACHY nominal NO measurements sorted by date and year, each day comprised about 15 orbits. See the accompanying README for the dimension and variable descriptions.</p>\n\n<p>The NO retrieval was carried out at the Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany, and is described in Bender et al., 2017. It is adapted from the MLT NO retrieval described in Bender et al., 2013. We used the SCIAMACHY geo-located atmospheric spectra (SCI_NL__1P) version 8.02 provided by ESA via their data browser at<br>\nhttps://earth.esa.int/web/guest/data-access/browse-data-products.<br>\nThe spectra were calibrated with ESA's `SciaL1C` command line tool available for download at<br>\nhttps://earth.esa.int/web/guest/software-tools/content/-/article/scial1c-command-line-tool-4073.</p>\n\n<p>The SCIAMACHY MLT NO data were previously compared to the results from ACE-FTS, MIPAS, and SMR in Bender et al., 2015, showing that all agree within the respective measurement uncertainties. This nominal data set here was not yet validated with other measurements but compares well to the SCIAMACHY MLT NO measurements below 90 km.</p>\n\n<p><strong>Acknowledgements</strong><br>\nThe development of the retrieval was funded by the Helmholtz-society under the grant number VH-NG-624. The SCIAMACHY project, which was initiated by Professor Burrows in 1984, was funded by the German Aerospace Agency (DLR), the Netherlands Space Office NSO, formerly NIVR, and the Belgium ministry responsible for space. ESA funded the Envisat project. Professor Burrows of University of Bremen is the Principal Investigator. He and his research team comprising his colleagues in Bremen and international scientific collaborators led the scientific support and development of SCIAMACHY and the scientific exploitation of its\u00a0 data products.</p>\n\n<p>The SCIAMACHY instrument is developed by an industrial team headed by companies now known as Airbus SD on the German side and by Dutch Space on the Dutch side and included Belgium companies. The instrument and algorithm development is supported by the activities of the SCIAMACHY Science Advisory Group (SSAG), a team of scientists from various\u00a0 international institutions: University of\u00a0 Bremen (D), SRON (NL), SAO (USA), IASB (B), MPI Chemistry Mainz (D), KNMI (NL),\u00a0 University of Heidelberg (D), IMGA (I), CNRS-LPMA (F). Operational data processing is being performed by ESA and DLR-DFD within the ENVISAT ground\u00a0 segment. Support with respect to mission planning and operations is given by\u00a0 the SCIAMACHY Operations Support Team (SOST). The relevant work at the University of Bremen is funded by the University and State of Bremen.</p>", 
  "author": [
    {
      "family": "Bender, Stefan"
    }, 
    {
      "family": "Sinnhuber, Miriam"
    }, 
    {
      "family": "Burrows, John P."
    }, 
    {
      "family": "Langowski, Martin"
    }
  ], 
  "note": "Please also cite Bender et al., 2017 when using this data set.", 
  "type": "dataset", 
  "id": "804371"
}
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