Abstract. We present an empirical model for nitric oxide (NO) in the mesosphere
(≈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., 2017b, a) as a function of geomagnetic latitude
to the solar Lyman-α 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.