review of "An empirical model of nitric oxide in the upper mesosphere and lower thermosphere based on 12 years of Odin-SMR measurements"

2018 ◽  
Author(s):  
Anonymous
Keyword(s):  
Nitric Oxide ◽  
Empirical Model ◽  
Lower Thermosphere ◽  
Mesosphere And Lower Thermosphere

scholarly journals An empirical model of nitric oxide in the upper mesosphere and lower thermosphere based on 12 years of Odin SMR measurements

2018 ◽  
Vol 18 (18) ◽  
pp. 13393-13410 ◽  
Author(s):  
Joonas Kiviranta ◽  
Kristell Pérot ◽  
Patrick Eriksson ◽  
Donal Murtagh
Keyword(s):  
Nitric Oxide ◽  
Empirical Model ◽  
Input Data ◽  
Lower Thermosphere ◽  
Number Density ◽  
Magnetic Latitude ◽  
Auroral Activity ◽  
K Index ◽  
Solar Declination ◽  
Mesosphere And Lower Thermosphere

Abstract. Nitric oxide (NO) is produced by solar photolysis and auroral activity in the upper mesosphere and lower thermosphere region and can, via transport processes, eventually impact the ozone layer in the stratosphere. This work uses measurements of NO taken between 2004 and 2016 by the Odin sub-millimeter radiometer (SMR) to build an empirical model that links the prevailing solar and auroral conditions with the measured number density of NO. The measurement data are averaged daily and sorted into altitude and magnetic latitude bins. For each bin, a multivariate linear fit with five inputs, the planetary K index, solar declination, and the F10.7 cm flux, as well as two newly devised indices that take the planetary K index and the solar declination as inputs in order to take NO created on previous days into account, constitutes the link between environmental conditions and measured NO. This results in a new empirical model, SANOMA, which only requires the three indices to estimate NO between 85 and 115 km and between 80∘ S and 80∘ N in magnetic latitude. Furthermore, this work compares the NO calculated with SANOMA and an older model, NOEM, with measurements of the original SMR dataset, as well as measurements from four other instruments: ACE, MIPAS, SCIAMACHY, and SOFIE. The results suggest that SANOMA can capture roughly 31 %–70 % of the variance of the measured datasets near the magnetic poles, and between 16 % and 73 % near the magnetic equator. The corresponding values for NOEM are 12 %–38 % and 7 %–40 %, indicating that SANOMA captures more of the variance of the measured datasets than NOEM. The simulated NO for these regions was on average 20 % larger for SANOMA, and 78 % larger for NOEM, than the measured NO. Two main reasons for SANOMA outperforming NOEM are identified. Firstly, the input data (Odin SMR NO) for SANOMA span over 12 years, while the input data for NOEM from the Student Nitric Oxide Experiment (SNOE) only cover 1998–2000. Additionally, some of the improvement can be accredited to the introduction of the two new indices, since they include information of auroral activity on prior days that can significantly enhance the number density of NO in the MLT during winter in the absence of sunlight. As a next step, SANOMA could be used as input in chemical models, as a priori information for the retrieval of NO from measurements, or as a tool to compare Odin SMR NO with other instruments. SANOMA and accompanying scripts are available on http://odin.rss.chalmers.se (last access: 15 September 2018).

scholarly journals An empirical model of nitric oxide in the upper mesosphere and lower thermosphere based on 12 years of Odin-SMR measurements

2018 ◽  
Author(s):  
Joonas Kiviranta ◽  
Kristell Pérot ◽  
Patrick Eriksson ◽  
Donal Murtagh
Keyword(s):  
Nitric Oxide ◽  
Empirical Model ◽  
Input Data ◽  
Lower Thermosphere ◽  
Number Density ◽  
Magnetic Latitude ◽  
Auroral Activity ◽  
K Index ◽  
Solar Declination ◽  
Mesosphere And Lower Thermosphere

Abstract. Nitric Oxide (NO) is produced by solar photolysis and auroral activity in the upper mesosphere and lower thermosphere region and can, via transport processes, eventually impact the ozone layer in the stratosphere. This work uses measurements of NO taken between 2004 and 2016 by the Odin Sub Millimetre Radiometer (SMR) to build an empirical model which links the prevailing solar and auroral conditions with the measured number density of NO. The measurement data are averaged daily and sorted into altitude and magnetic latitude bins. For each bin, a multivariate linear fit with five inputs, the planetary K-index, solar declination, and the F10.7cm flux, and two newly devised indices which take the planetary K-index and the solar declination as inputs in order to take NO created on previous days into account, constitutes the link between environmental conditions and measured NO. This results in a new empirical model, SANOMA, which only requires the three indices to estimate NO between 85 km–115 km and 80° S–80° N in magnetic latitude. Furthermore, this work compares the NO calculated with SANOMA and an older model, NOEM, with measurements of the original SMR-dataset, as well as measurements from four other instruments: ACE, MIPAS, SCIAMACHY, and SOFIE. The results suggest that SANOMA can capture roughly 31–70 % of the variance of the measured datasets near the magnetic poles, and between 16–73 % near the magnetic equator. The corresponding values for NOEM are 12–38 % and 7–40 %, indicating that SANOMA captures more of the variance of the measured datasets than NOEM. The simulated NO for these regions was on average 20 % larger for SANOMA, and 78 % larger for NOEM, than the measured NO. Two main reasons for SANOMA outperforming NOEM are identified. Firstly, the input data (Odin SMR NO) for SANOMA spans over 12 years, while the input data for NOEM from the Student Nitric Oxide Experiment (SNOE) only covers 1998–2000. Additionally, some of the improvement can be accredited to the introduction of the two new indices, since they include information of auroral activity on prior days which can significantly enhance the number density of NO in the MLT during winter in the absence of sunlight. As a next step, SANOMA could be used as input in chemical models, as apriori information for the retrieval of NO from measurements, or as a tool to compare Odin SMR NO with other instruments. SANOMA and accompanying scripts are available on http://odin.rss.chalmers.se.

scholarly journals Mesospheric nitric oxide model from SCIAMACHY data

2019 ◽  
Vol 19 (4) ◽  
pp. 2135-2147 ◽  
Author(s):  
Stefan Bender ◽  
Miriam Sinnhuber ◽  
Patrick J. Espy ◽  
John P. Burrows
Keyword(s):  
Nitric Oxide ◽  
Empirical Model ◽  
Lower Thermosphere ◽  
Geomagnetic Latitude ◽  
Model Atmosphere ◽  
Monte Carlo Sampling ◽  
No Production ◽  
Parameter Uncertainties ◽  
Superposed Epoch Analysis ◽  
Scanning Imaging

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.

scholarly journals Comparison of nitric oxide measurements in the mesosphere and lower thermosphere from ACE-FTS, MIPAS, SCIAMACHY, and SMR

2014 ◽  
Vol 7 (12) ◽  
pp. 12735-12794 ◽  
Author(s):  
S. Bender ◽  
M. Sinnhuber ◽  
T. von Clarmann ◽  
G. Stiller ◽  
B. Funke ◽  
...  
Keyword(s):  
Nitric Oxide ◽  
Time Series ◽  
Regression Analysis ◽  
Linear Regression Analysis ◽  
Lower Thermosphere ◽  
Data Sets ◽  
Annual Variations ◽  
Annual Variability ◽  
Geographical Regions ◽  
Mesosphere And Lower Thermosphere

Abstract. We compare the nitric oxide measurements in the mesosphere and lower thermosphere (60 to 150 km) from four instruments: ACE-FTS, MIPAS, SCIAMACHY, and SMR. We use the daily zonal mean data in that altitude range for the years 2004–2010 (ACE-FTS), 2005–2012 (MIPAS), 2008–2012 (SCIAMACHY), and 2003–2012 (SMR). We first compare the data qualitatively with respect to the morphology, focussing on the major features, and then compare the time series directly and quantitatively. In three geographical regions, we compare the vertical density profiles on coincident measurement days. Since none of the instruments delivers continuous daily measurements in this altitude region, we carried out a multi-linear regression analysis. This regression analysis considers annual and semi-annual variability in form of harmonic terms and inter-annual variability by responding linearly to the solar Lyman-α radiation index and the geomagnetic Kp index. This analysis helps to find similarities and differences in the individual data sets with respect to the inter-annual variations caused by geomagnetic and solar variability. We find that the data sets are consistent and that they only disagree on minor aspects. SMR and ACE-FTS deliver the longest time series in the mesosphere and they both agree remarkably well. The shorter time series from MIPAS and SCIAMACHY also agree with them where they overlap. The data agree within ten to twenty percent when the number densities are large, but they can differ by 50 to 100% in some cases.

scholarly journals Comparison of nitric oxide measurements in the mesosphere and lower thermosphere from ACE-FTS, MIPAS, SCIAMACHY, and SMR

2015 ◽  
Vol 8 (10) ◽  
pp. 4171-4195 ◽  
Author(s):  
S. Bender ◽  
M. Sinnhuber ◽  
T. von Clarmann ◽  
G. Stiller ◽  
B. Funke ◽  
...  
Keyword(s):  
Nitric Oxide ◽  
Time Series ◽  
Regression Analysis ◽  
Linear Regression Analysis ◽  
Lower Thermosphere ◽  
Michelson Interferometer ◽  
Data Sets ◽  
Annual Variability ◽  
Geographical Regions ◽  
Mesosphere And Lower Thermosphere

Abstract. We compare the nitric oxide measurements in the mesosphere and lower thermosphere (60 to 150 km) from four instruments: the Atmospheric Chemistry Experiment–Fourier Transform Spectrometer (ACE-FTS), the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY), and the Sub-Millimetre Radiometer (SMR). We use the daily zonal mean data in that altitude range for the years 2004–2010 (ACE-FTS), 2005–2012 (MIPAS), 2008–2012 (SCIAMACHY), and 2003–2012 (SMR). We first compare the data qualitatively with respect to the morphology, focussing on the major features, and then compare the time series directly and quantitatively. In three geographical regions, we compare the vertical density profiles on coincident measurement days. Since none of the instruments delivers continuous daily measurements in this altitude region, we carried out a multi-linear regression analysis. This regression analysis considers annual and semi-annual variability in the form of harmonic terms and inter-annual variability by responding linearly to the solar Lyman-α radiation index and the geomagnetic Kp index. This analysis helps to find similarities and differences in the individual data sets with respect to the inter-annual variations caused by geomagnetic and solar variability. We find that the data sets are consistent and that they only disagree on minor aspects. SMR and ACE-FTS deliver the longest time series in the mesosphere, and they agree with each other remarkably well. The shorter time series from MIPAS and SCIAMACHY also agree with them where they overlap. The data agree within 30 % when the number densities are large, but they can differ by 50 to 100 % in some cases.

scholarly journals Mesospheric nitric oxide model from SCIAMACHY data

2018 ◽  
Author(s):  
Stefan Bender ◽  
Miriam Sinnhuber ◽  
Patrick J. Espy ◽  
John P. Burrows
Keyword(s):  
Nitric Oxide ◽  
Empirical Model ◽  
Lower Thermosphere ◽  
Geomagnetic Latitude ◽  
Model Atmosphere ◽  
Monte Carlo Sampling ◽  
No Production ◽  
Parameter Uncertainties ◽  
Scan Data ◽  
Epoch Analysis

Abstract. We present an empirical model for nitric oxide (NO) in the mesosphere (≈ 60–90 km) 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.

scholarly journals Retrieval of nitric oxide in the mesosphere from SCIAMACHY nominal limb spectra

2017 ◽  
Vol 10 (1) ◽  
pp. 209-220 ◽  
Author(s):  
Stefan Bender ◽  
Miriam Sinnhuber ◽  
Martin Langowski ◽  
John P. Burrows
Keyword(s):  
Nitric Oxide ◽  
Climate Models ◽  
A Priori ◽  
Lower Thermosphere ◽  
Ultra Violet ◽  
Retrieval Algorithm ◽  
Altitude Range ◽  
Mesosphere And Lower Thermosphere ◽  
Scanning Imaging ◽  
Nominal Mode

Abstract. We present a retrieval algorithm for nitric oxide (NO) number densities from measurements from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY, on Envisat) nominal limb mode (0–91 km). The NO number densities are derived from atmospheric emissions in the gamma bands in the range 230–300 nm, measured by the SCIAMACHY ultra-violet (UV) channel 1. The retrieval is adapted from the mesosphere and lower thermosphere mode (MLT, 50–150 km) NO retrieval (Bender et al., 2013), including the same 3-D ray tracing, 2-D retrieval grid, and regularisations with respect to altitude and latitude.Since the nominal mode limb scans extend only to about 91 km, we use NO densities in the lower thermosphere (above 92 km), derived from empirical models, as a priori input. The priors are the Nitric Oxide Empirical Model (NOEM; Marsh et al., 2004) and a regression model derived from the MLT NO data comparison (Bender et al., 2015). Our algorithm yields plausible NO number densities from 60 to 85 km from the SCIAMACHY nominal limb mode scans. Using a priori input substantially reduces the incorrect attribution of NO from the lower thermosphere, where no direct limb measurements are available. The vertical resolution lies between 5 and 10 km in the altitude range 65–80 km.Analysing all SCIAMACHY nominal limb scans provides almost 10 years (from August 2002 to April 2012) of daily NO measurements in this altitude range. This provides a unique data record of NO in the upper atmosphere and is invaluable for constraining NO in the mesosphere, in particular for testing and validating chemistry climate models during this period.

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