Journal cover Journal topic
Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 5.509 IF 5.509
  • IF 5-year value: 5.689 IF 5-year 5.689
  • CiteScore value: 5.44 CiteScore 5.44
  • SNIP value: 1.519 SNIP 1.519
  • SJR value: 3.032 SJR 3.032
  • IPP value: 5.37 IPP 5.37
  • h5-index value: 86 h5-index 86
  • Scimago H index value: 161 Scimago H index 161
Discussion papers
https://doi.org/10.5194/acp-2018-872
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2018-872
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 22 Oct 2018

Research article | 22 Oct 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).

Mesospheric nitric oxide model from SCIAMACHY data

Stefan Bender1, Miriam Sinnhuber2, Patrick J. Espy1, and John P. Burrows3 Stefan Bender et al.
  • 1Norwegian University of Science and Technology, Trondheim, Norway
  • 2Karlsruhe Institute of Technology, Karlsruhe, Germany
  • 3University of Bremen, Bremen, Germany

Abstract. We present an empirical model for nitric oxide (NO) in the mesosphere (≈60–90km) derived from SCIAMACHY limb scan data. This work complements and extends the NOEM (Nitric Oxide Empirical Model, Marsh et al. (2004)) and SANOMA (SMR Acquired Nitric Oxide Model Atmosphere, Kiviranta et al. (2018)) empirical models in the lower thermosphere. The regression ansatz builds on the heritage of studies by Hendrickx et al. (2017) and the super-posed epoch analysis by Sinnhuber et al. (2016) which estimate NO production from particle precipitation.

Our model relates the daily (longitudinally) averaged NO number densities from SCIAMACHY (Bender et al., 2017b, a) as a function of geomagnetic latitude to the solar Lyman-α and the geomagnetic AE indices. We use a non-linear regression model incorporating a finite and seasonally varying lifetime for the geomagnetically induced NO. We estimate the parameters by finding the maximum posterior probability and calculate the parameter uncertainties using Markov-Chain Monte-Carlo sampling. In addition to providing an estimate of the NO content in the mesosphere, the regression coefficients indicate regions where certain processes dominate.

Stefan Bender et al.
Interactive discussion
Status: open (until 17 Dec 2018)
Status: open (until 17 Dec 2018)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement
Stefan Bender et al.
Stefan Bender et al.
Viewed  
Total article views: 248 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
201 42 5 248 5 3
  • HTML: 201
  • PDF: 42
  • XML: 5
  • Total: 248
  • BibTeX: 5
  • EndNote: 3
Views and downloads (calculated since 22 Oct 2018)
Cumulative views and downloads (calculated since 22 Oct 2018)
Viewed (geographical distribution)  
Total article views: 248 (including HTML, PDF, and XML) Thereof 246 with geography defined and 2 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Cited  
Saved  
No saved metrics found.
Discussed  
No discussed metrics found.
Latest update: 19 Nov 2018
Publications Copernicus
Download
Short summary
We present an empirical model for nitric oxide (NO) in the mesosphere (60–90 km) derived from SCIAMACHY limb scan data. Our model relates the daily (longitudinally) averaged NO number densities from SCIAMACHY as a function of geomagnetic latitude to the solar Lyman-alpha and the geomagnetic AE indices. We use a non-linear regression model incorporating a finite and seasonally varying lifetime for the geomagnetically induced NO.
We present an empirical model for nitric oxide (NO) in the mesosphere (60–90 km) derived from...
Citation
Share