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
Nitric oxide (NO) is a reactive free radical and, together with Nitrogen
dioxide (
Previous studies have established that EPP from auroral activity dominates
the variation of NO near the magnetic poles, while solar soft X-rays
contribute more near the magnetic equator
Therefore the amount of NO is affected by seasonal variation of sunlight.
Under sunlight conditions in the MLT region, NO has a chemical lifetime of
less than 1 day, whereas during the polar night in winter, it may persist
for several weeks
This study focuses on the effect of solar and auroral activity on the amount of NO in the MLT. Over the past several decades, at least six satellites have measured NO in the MLT region. These include the past instruments, SNOE (Student Nitric Oxide Experiment), SCIAMACHY (Scanning Imaging Absorption spectroMeter for Atmospheric CHartographY), and MIPAS (Michelson Interferometer for Passive Atmospheric Sounding), as well as the currently active Odin SMR (sub-millimeter radiometer), SOFIE (Solar Occultation for Ice Experiment), and ACE (Atmospheric Chemistry Experiment) instruments. The limitation of satellite measurements is that they only cover certain locations and periods of time. Yet, many applications, such as chemical models of the upper atmosphere, require information on the amount of NO at any given time or location. To help bridge this gap, a model that connects known environmental conditions, such as auroral activity, with measured NO can help to provide an estimate of NO at any time and place. Such a model can also help validate and constrain poorly resolved or underdetermined parameters of first principle models.
However, no study has validated NOEM or proposed a contending model since
its release. This study aims to fill these two gaps by building a new
empirical model based on NO measurements in the MLT by Odin SMR for the
period 2004–2016. We hypothesize that an empirical model derived from Odin
SMR should be more accurate than NOEM because the Odin SMR measurements
include a larger range of solar conditions over a period of over 12 years.
Furthermore, SNOE measured only daytime NO whereas SMR provides NO
measurements during both daytime and nighttime. This might introduce some discrepancy
between NOEM and the resulting empirical model, since the concentration of NO
is characterized by a strong diurnal variation, depending on latitude and
altitude
This study primarily aims to derive a new empirical model based on the 12 years of Odin SMR measurements to calculate NO in the MLT. This new model will be named the SMR Acquired Nitric Oxide Model Atmosphere (SANOMA). Additionally, this study aims to evaluate the performance of both SANOMA and NOEM by comparing simulated NO with measurements from the independent NO-measuring instruments SOFIE, SCIAMACHY, ACE, and MIPAS.
Section
This section outlines the Odin SMR dataset that forms the basis for SANOMA.
Section
The Odin SMR instrument scans the limb of the atmosphere and has been
observing NO thermal emission lines in a band centered around 551.7 GHz
since October 2003
Using the measured emission spectra, an inversion algorithm derives the
volume mixing ratio (VMR) of NO as a function of altitude for the location of
the measurement with an altitude resolution of
Only measurements in which the measurement response, a measure of the
relative contributions of the measurement and the a priori dataset, exceeds 0.75 are
considered for our analysis. Although no study has validated the version 3.0
Level 2 data,
SANOMA will express NO in number density to facilitate its comparison to
NOEM. The Odin SMR measured VMR at each altitude is converted to number
density (molecules cm
For each measurement day, the NO number density is sorted into bins according
to altitude and magnetic latitude. Prior to sorting, each individual
measurement is interpolated in altitude with grid points at the centers of
the altitude bins. The bins for altitude run from 85 to
Mean NO density in molecules per cubic centimeter for 1 August 2006 calculated from V3.0 Level 2 Odin SMR data.
Daily mean F10.7 cm flux over time in solar flux units
(10
Since auroral and solar activity create NO in the MLT, proxies that describe these two phenomena constitute key parts of SANOMA. In the search for appropriate proxies, this chapter introduces some of the most common ones.
Measurements of the irregular variations of the horizontal component of the
Earth's magnetic field constitute an auroral activity index, the Kp index
To describe solar activity, the 10.7 cm solar radio flux is among the most
widely used indices. It constitutes a proxy for the incoming solar soft
X-rays and is based on the solar radio emission in a 100 MHz wide band
centered around 2800 MHz
This section describes the method used to derive SANOMA from the original
Odin SMR measurements. Section
Figure
Mean NO density of NO measured by Odin SMR from 2004 to 2016.
Daily mean NO density of Odin SMR NO at
To investigate the link between auroral activity and the NO number density,
Fig.
Figure
NO number density measured with SOFIE and Kp index over time for
Figure
Parameter
The parameter
SMR-measured and SANOMA-simulated time series of NO at 102 km and
The coefficients of the
SANOMA uses the three indices from NOEM and the two indices defined in
Eqs. (
The complete SANOMA model comprises of a total of 165 individual multivariate
linear fits, one for each altitude–latitude bin. The coefficients of these
fits indicate which of the input indices influence NO at the various
locations. Figure
The coefficients corresponding to the
This section compares NO simulated with SANOMA with the original SMR-measured
NO to confirm that the model has been successfully built. SANOMA has a
resolution of 6.66 km in altitude and 5
To explore the added value of the two new indices and the SANOMA equation,
Fig.
Mean of the differences (in cm
Adjusted
So far we have presented how well SANOMA explains SMR measurements. However,
the SMR measurements themselves include measurement error and hence a model
will be unable to perfectly reproduce the measurements. We can attempt to
separate the discrepancy between SANOMA and SMR into two parts: the
measurement error from Odin SMR and the modeling error from SANOMA. Having an
understanding of the error of SANOMA can help to assess the reliability of
its simulations. Assuming that all errors are normally distributed, we
estimate the variance of the modeling error with
So far, we have presented SANOMA, its underlying principle, and a comparison
of its simulations with NO measured by Odin SMR. This section evaluates the
performance of SANOMA and NOEM by comparing simulated NO number density with
measured NO from the four independent instruments, SOFIE, SCIAMACHY, ACE-FTS,
and MIPAS. SANOMA can be seen as a tool to compare the SMR dataset with the
other data, and therefore these comparisons can provide valuable information
regarding the accuracy of SMR-NO measurements. For an overview,
Table
Overview of all the compared NO datasets.
SANOMA has a resolution of 6.66 km in altitude and 5
This section compares the SANOMA- and NOEM-simulated NO number density with
Level 2 V1.3 SOFIE measurements
Time series of NO number density measured with SOFIE over the entire
measurement period as well as simulated NO with SANOMA and NOEM, 102 km
altitude,
Time series of NO number density measured with SCIAMACHY and
simulated with SANOMA as well as NOEM, 102 km altitude,
To examine the accuracy of SANOMA and NOEM as a function of magnetic latitude
and altitude, Fig.
To assess the amount of variation that each model captures of the original
SOFIE data, Fig.
This section compares the SANOMA- and NOEM-simulated NO number density with
v1.8.1 SCIAMACHY
Figure
Adjusted
As can be seen in Fig.
This section compares the SANOMA and NOEM simulated NO number densities with
V3.5 ACE measurements
Time series of NO number density measured with ACE and simulated
with SANOMA as well as NOEM, 102 km altitude,
The SANOMA
Finally, this section compares the simulated NO number densities with v5r 622
MIPAS
This section aims to summarize and elaborate on the results of the previous
sections. To achieve an overview of the results, Table
Table
Generally, the models capture more variance in the Northern than in the Southern Hemisphere. Perhaps the larger offset between the geomagnetic and geographic pole, or the more stable dynamics in the Southern Hemisphere affect the amount of NO in ways that are beyond the reach of these simple models.
The SMRNOEM values in Table
Time series of NO number density measured with MIPAS and simulated
with SANOMA as well as NOEM, 102 km altitude,
Since SMR measures both day- and nighttime NO, a positive difference compared
to the daytime measuring instrument SCIAMACHY was expected. SANOMA is also
characterized by a slight positive bias in comparison to the solar
occultation instruments SOFIE and ACE-FTS, which could be due to differences
in the diurnal sampling. The magnitude of the relative differences between
SANOMA and each satellite is similar, although slightly higher than the
differences between the SMR dataset and the other instruments described by
This study presented a new empirical model called SANOMA to simulate NO in
the MLT. This model is based on V3.0 Odin SMR NO, to which we fit
multivariate linear functions using the Kp index, solar declination, the
logarithm of the F10.7 cm flux, as well as two compound indices based on the
Kp index and solar declination. These two compound indices attempt to account
for the lifetime of NO in the absence of sunlight. SANOMA can capture an
average of 63.9 % of the variance of the Odin SMR NO between 88 and
116 km and between
An alternative to the multivariate linear fit in this study would have been
EOF analysis such as in
Our original hypothesis that a model similar to NOEM, but derived using Odin SMR data, would result in a more accurate model was proven to be true. Comparing the results of NOEM and SANOMA with measured NO showed that, especially during times of low solar activity, NOEM overestimates NO by roughly 100 %. This could be attributed to the fact that NOEM was built on only 2 years of SNOE NO data from 1998 to 2000, a period of high solar activity. Hence, when the model is applied to low-activity periods, such as 2009–2010, the extrapolation from high-activity to low-activity conditions is inaccurate, resulting in large errors of NOEM NO compared to the measurements.
In terms of explaining the variation of NO, unlike NOEM, SANOMA manages to
recreate more of the highest concentrations of NO. SANOMA still fails to
explain some of the highest spikes of NO and suffers from a relatively coarse
(6.5 km) altitude resolution as well as a narrow altitude range
(85–115 km). The results from Fig.
Creating SANOMA with all SMR measurements will have likely introduced a positive bias compared to day-measuring instruments, such as SCIAMACHY, since nighttime NO is expected to be higher than daytime NO. An alternative to the current model would be to provide two versions of SANOMA: one for day, and one for night.
Although no rigorous validation of Odin SMR NO in the MLT regions exists,
The observation dataset used to develop SANOMA is
available to any potential user on
JK wrote the main body of text, carried out the work behind SANOMA, analyzed satellite and index data, and created all plots. KP initiated the study and took part in scientific consulting, editing of the text, and input on correctness of facts, among other contributions. Both DM and PE took part in scientific consulting and editing of the text.
The authors declare that they have no conflict of interest.
Odin is a Swedish-lead satellite project funded jointly by Sweden (SNSB), Canada (CSA), Finland (TEKES), France (CNES), and the Third-Party Missions programme of the European Space Agency (ESA). The following people have kindly provided their support by the method indicated in the brackets: Koen Hendrickx (providing SOFIE data), Daniel Marsh (providing NOEM and general feedback), Kaley Walker (providing ACE data), Stefan Bender (providing SCIAMACHY data), and Jean Lilensten (providing general feedback on the model). We would also like to thank the two reviewers for their helpful comments. Edited by: William Ward Reviewed by: Bernd Funke and one anonymous referee