Mesospheric nitric oxide model from SCIAMACHY data. (arXiv:2102.08455v1 [physics.ao-ph])
<a href="http://arxiv.org/find/physics/1/au:+Bender_S/0/1/0/all/0/1">Stefan Bender</a>, <a href="http://arxiv.org/find/physics/1/au:+Sinnhuber_M/0/1/0/all/0/1">Miriam Sinnhuber</a>, <a href="http://arxiv.org/find/physics/1/au:+Espy_P/0/1/0/all/0/1">Patrick J. Espy</a>, <a href="http://arxiv.org/find/physics/1/au:+Burrows_J/0/1/0/all/0/1">John P. Burrows</a>
We present an empirical model for nitric oxide NO in the mesosphere
($approx$60–90 km) derived from SCIAMACHY (SCanning Imaging Absorption
spectroMeter for Atmospheric CHartoghraphY) limb scan data. This work
complements and extends the NOEM (Nitric Oxide Empirical Model; Marsh et al.,
2004) and SANOMA (SMR Acquired Nitric Oxide Model Atmosphere; Kiviranta et al.,
2018) empirical models in the lower thermosphere. The regression ansatz builds
on the heritage of studies by Hendrickx et al. (2017) and the superposed epoch
analysis by Sinnhuber et al. (2016) which estimate NO production from particle
precipitation.
Our model relates the daily (longitudinally) averaged NO number densities
from SCIAMACHY (Bender et al., 2017a, b) as a function of geomagnetic latitude
to the solar Lyman-alpha and the geomagnetic AE (auroral electrojet) indices.
We use a non-linear regression model, incorporating a finite and seasonally
varying lifetime for the geomagnetically induced NO. We estimate the parameters
by finding the maximum posterior probability and calculate the parameter
uncertainties using Markov chain Monte Carlo sampling. In addition to providing
an estimate of the NO content in the mesosphere, the regression coefficients
indicate regions where certain processes dominate.
We present an empirical model for nitric oxide NO in the mesosphere
($approx$60–90 km) derived from SCIAMACHY (SCanning Imaging Absorption
spectroMeter for Atmospheric CHartoghraphY) 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 superposed 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., 2017a, b) as a function of geomagnetic latitude
to the solar Lyman-alpha and the geomagnetic AE (auroral electrojet) 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.
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