Mesospheric nitric oxide model from SCIAMACHY data

TL;DR

This paper develops an empirical model for mesopheric nitric oxide using SCIAMACHY limb scan data, relating NO densities to geomagnetic and solar activity, with a focus on understanding NO production and regional dominance.

Contribution

It introduces a non-linear regression model that estimates mesopheric NO densities from satellite data, incorporating seasonal NO lifetime variations and uncertainty quantification.

Findings

Model relates NO densities to geomagnetic latitude, solar Lyman-alpha, and AE indices.

Estimates NO content and identifies regions dominated by specific processes.

Provides parameter uncertainties via Markov chain Monte Carlo sampling.

Abstract

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.