基于SCIAMACHY资料的中层一氧化氮模式
Mesospheric nitric oxide model from SCIAMACHY data
摘要：
我们提出了一个由SCIAMACHY（Scanning Imaging Absorption SpectroMeter for Atmospheric Chartoghaphy）临边扫描数据导出的中间层（$\approx$ 60- 90 km）一氧化氮NO的经验模式。这项工作补充和扩展了NOEM（一氧化氮经验模型; Marsh等人，2004）和SANOMA（SMR获得的一氧化氮模式大气; Kiviranta等人，2018年），在较低的热层经验模型。回归分析建立在Hendrickx等人（2017）的研究遗产和Sinnhuber等人（2016）的叠加时代分析的基础上，这些分析估计了颗粒沉淀的NO产生。我们的模型将来自SCIAMACHY的每日（纵向）平均NO数密度（Bender等人，2017 a，b）作为地磁纬度对太阳莱曼α和地磁AE（极光带电集流）指数的函数。我们使用一个非线性回归模型，将一个有限的和季节性变化的寿命为地磁感应NO。我们估计的参数，通过找到最大后验概率和计算参数的不确定性，使用马尔可夫链蒙特卡罗抽样。除了提供中间层NO含量的估计值外，回归系数还表明某些过程占主导地位的区域。
英文摘要：
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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