ANGEOAnnales GeophysicaeANGEOAnn. Geophys.1432-0576Copernicus PublicationsGöttingen, Germany10.5194/angeo-35-1151-2017 Mesospheric OH layer altitude at midlatitudes: variability over the Sierra Nevada Observatory in Granada, Spain (37∘ N, 3∘ W)García-ComasMayamaya@iaa.eshttps://orcid.org/0000-0003-2323-4486López-GonzálezMaría JoséGonzález-GalindoFranciscode la RosaJosé LuisLópez-PuertasManuelhttps://orcid.org/0000-0003-2941-7734ShepherdMarianna G.https://orcid.org/0000-0003-2731-8513ShepherdGordon G.Instituto de Astrofísica de Andalucía-CSIC, Glorieta de la Astronomía s/n, 18008 Granada, SpainCentre for Research in Earth and Space Science, York University, 4700 Keele St., Toronto, Ontario M3J 1P3, CanadaMaya García-Comas (maya@iaa.es)25October20173551151116431July201719September201720September2017This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://angeo.copernicus.org/articles/35/1151/2017/angeo-35-1151-2017.htmlThe full text article is available as a PDF file from https://angeo.copernicus.org/articles/35/1151/2017/angeo-35-1151-2017.pdf
The mesospheric OH layer varies on several timescales, primarily
driven by variations in atomic oxygen, temperature, density and transport
(advection). Vibrationally excited OH airglow intensity, rotational
temperature and altitude are closely interrelated and thus accompany each
other through these changes. A correct interpretation of the OH layer
variability from airglow measurements requires the study of the three
variables simultaneously. Ground-based instruments measure excited OH
intensities and temperatures with high temporal resolution, but they do not
generally observe altitude directly. Information on the layer height is
crucial in order to identify the sources of its variability and the causes
of discrepancies in measurements and models. We have used SABER
space-based 2002–2015 data to infer an empirical function for predicting
the altitude of the layer at midlatitudes from ground-based measurements of
OH intensity and rotational temperature. In the course of the analysis, we
found that the SABER altitude (weighted by the OH volume emission rate) at midlatitudes
decreases at a rate of 40 m decade-1, accompanying an increase of
0.7 % decade-1 in OH intensity and a decrease of
0.6 K decade-1 in OH equivalent temperature. SABER OH altitude barely
changes with the solar cycle, whereas OH intensity and temperature vary by
7.8 % per 100 s.f.u. and 3.9 K per 100 s.f.u., respectively. For
application of the empirical function to Sierra Nevada Observatory SATI data,
we have calculated OH intensity and temperature SATI-to-SABER transfer
functions, which point to relative instrumental drifts of
-1.3 % yr-1 and 0.8 K yr-1, respectively, and a
temperature bias of 5.6 K. The SATI predicted altitude using the empirical
function shows significant short-term variability caused by overlapping
waves,
which often produce changes of more than 3–4 km in a few hours, going along
with 100 % and 40 K changes in intensity and temperature, respectively.
SATI OH layer wave effects are smallest in summer and largest around New
Year's Day. Moreover, those waves vary significantly from day to day. Our
estimations suggest that peak-to-peak OH nocturnal variability, mainly due to
wave variability, changes within 60 days at least 0.8 km for altitude in
autumn, 45 % for intensity in early winter and 6 K for temperature in
midwinter. Plausible upper limit ranges of those variabilities are
0.3–0.9 km, 40–55 % and 4–7 K, with the exact values depending on
the season.
Atmospheric composition and structure (airglow and aurora)Introduction
Since first identified the strong OH airglow emission
originating from vibrational rotation transitions of OH vibrationally excited
molecules (OH* or simply OH hereafter), its measurements have been
extensively used to study the mesopause region temperature and OH emission
layer from the ground. These studies have focused on varied topics: analysis
of the impact of atmospheric waves on regional and global scales, the detection of
geo-hazards, the effect of sudden stratospheric warmings (SSWs), seasonal and
interannual variations, external forcing response, long-term trends,
cross-validation for satellite measurements, the detection of satellite
drifts and the
determination of OH radiative properties e.g.,to mention just a
few..
Ground-based OH airglow intensity and temperatures are often assumed to be
representative of an emission layer centered at a fixed altitude, generally
around 87 km ± 2 km e.g.,. Nevertheless, the OH
layer altitude varies on multiple timescales. Its variation is partially
driven by changes in transport (advection), primarily by those in
atomic oxygen (the main source of nighttime ozone and thus of excited OH),
temperature and density (through their effect on chemical reactions)
. On a short timescale, measured daily variations are due to
internal gravity waves and tides. reported vertical
displacements of the order of ±3 km at the Equator. Day-to-day changes
are mainly due to the varying effect of planetary waves and tides. In
addition, sudden stratospheric warmings alter the OH altitude, producing up
to 10 km vertical shifts during the descent phase . On
a medium timescale, the seasonal variation in the emission altitudes exhibits
semiannual, annual and quasi-biennial oscillations with up to 1.0, 1.5 and
0.5 km amplitudes, respectively
.
and reported a year-to-year
monthly mean OH altitude variation of 2–3 km and attributed it to the
effect of the El Niño–Southern Oscillation (ENSO). Some authors
e.g have found no obvious long-term trend or solar
cycle signatures in the OH emission altitude, in contrast to other studies
that found a slow altitude decrease with time .
Furthermore, the magnitude of all these variations depends on the vibrational
level .
A ground-based OH airglow instrument does not provide direct information on
the altitudes at which the OH emission emanates. The measured intensities and
temperatures are vertically weighted means. Knowledge of the altitude of the
emitting layer is necessary for a comprehensive interpretation of variations
of the OH layer. As mentioned above, this is because the three variables are
interconnected. The view from the ground complements global geospace
measurements on a regional scale, particularly for short-lived events and
short period and wavelength oscillations. Moreover, omitting information on
altitude biases comparisons with models and satellite measurements
because changes in altitude produce or are produced by intensity and
temperature variations . Nevertheless, the issues caused by
the assumption of a fixed altitude have very often been neglected in past
studies.
There are few reports on methods to estimate the OH layer altitude from
ground-based measurements. One possibility is to use observations with more
than one instrument and determine whether to triangulate OH observations at separated
locations or to correlate them with
simultaneous ground-based wind measurements . Another possibility
is to employ the OH intensities measured from the ground to infer the OH
emission altitude, relying on the fact that the latter depends quasi-linearly
on the former .
In this context, proposed a method to predict the altitude
of the OH layer from ground-based instrument measurements of intensities by
using an empirical function derived from space-based instrument
measurements. Three main steps are then needed: (1) to set and fit an
expression from the satellite instrument that reproduces the altitude as a
function of the independent variable (the OH intensity in their case), (2) to
derive the independent variable transfer function from measurements of the
ground- to the space-based instrument and (3) to apply both functions to
estimate the altitude from the ground-based instrument measurements alone.
The empirical function used by included a term proportional
to the OH intensity plus five more terms accounting for annual and
semiannual oscillations, diurnal and semidiurnal variations and the solar
cycle. further added a term proportional to the OH
intensity squared and slightly reduced the residual.
In this work, we aim to provide a mathematical expression to estimate OH
altitude from airglow intensity and temperature ground-based measurements and
to report the short-term nocturnal variability in the three variables at
northern midlatitudes. We use measurements of a SATI spectrometer, which
observes OH intensity and temperature over the Sierra Nevada Observatory in
Granada, Spain (37∘ N, 3∘ W). In order to derive the
altitude of the OH layer, we adopt a similar methodology to
except that we use a new empirical function, including not only OH airglow
intensity but also temperature as independent variables. We only needed two
additional terms: a semiannual oscillation and a linear local time term. We
selected SABER data (onboard the TIMED satellite and that simultaneously
measures OH volume emission rate and temperature profiles) for estimating the
coefficients of the empirical function connecting OH altitude with intensity
and temperature. Moreover, we study the seasonal variability, the solar
impact and the trends in airglow altitudes, intensities and temperatures
observed by SABER.
The structure of this article is as follows. We briefly describe the SATI and
SABER measurements in Sects. and ,
respectively. The rationale for the selection of the empirical function and
the results from the fit to SABER data are given in
Sect. . In that section, we also discuss SABER OH layer
seasonal and decadal variations. The evaluation of the SATI-to-SABER OH
intensity and temperature transfer functions, including estimations of
instrument relative drifts, and the subsequent application to SATI
measurements to determine OH altitude are presented in
Sect. . We also report two case studies and discuss
day-to-day changes in the nocturnal variability for the complete SATI
dataset. We conclude this report with a summary in Sect. .
SATI-OSN
Spectral Airglow Temperature Imagers (SATIs) are Fabry–Pérot spectrometers
in which the etalon is an ultra-narrow band (2Å) interference filter
. In addition to O2 emissions in the atmospheric band,
SATIs measure OH emissions from the Δv=4 transition of the v=6
vibrational level. The OH layer temperature, i.e., the temperature weighted
with the OH relative intensity at the OH layer altitudes, is retrieved from
the rotational structure of single measurements considering the relative
emission of three pairs of Q-branch lines (K1, K2 and K3 transitions) under
the assumption of rotational local thermodynamic equilibrium (LTE, which
holds for low rotational levels) and assuming the Einstein
coefficients. Background emission is simultaneously determined and
subtracted, and the total OH(6–2) band emission, SATI OH intensity
hereafter, is derived after simulation assuming the rotational LTE of a scaled
spectrum at the derived temperature. The relative contribution from the three
pairs of Q-branch lines to the total band emission is roughly 25 %
.
One of the currently operating SATIs around the world is located at
37∘ N and 3∘ W in the Observatorio de Sierra
Nevada (OSN), in Granada (Spain). It belongs to the Instituto de
Astrofísica de Andalucía (CSIC) and
is a part of the Network for the Detection of Mesopause Change (NDMC). An OH spectrum is measured every 4 min
during nighttime under clear-sky and no-moon conditions. SATI-OSN has been
routinely operating since 1998. Its observations have not been continuous due
to instrumental problems. We use measurements from the beginning of
2002, when SABER started measuring, until the end of 2015, available by request at the NDMC site (http://wdc.dlr.de/ndmc). There are three
main gaps in this period: January 2005–October 2005,
January 2008–September 2009 and January 2010–January 2012. Measurements
were intermittent from the end of 2012 until 2015 (see Fig. 1 in
). Since temperatures are retrieved considering
relative line intensities, offsets between separated (in time) measurement
periods are not significant.
SABER
The Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) is
a broadband radiometer onboard the TIMED satellite developed by NASA. It has
provided profiles of atmospheric infrared and near-infrared emission in 10
channels since 2002 in a nearly global manner .
Latitudes from 52∘ S to 52∘ N are observed continuously.
Higher latitudes (up to 82∘) are covered alternately at each
hemisphere after the satellite yaws every 2 months. SABER measures each day
at two almost fixed local solar times (LSTs) at each latitude, but its slow
precession allows for a complete LST coverage in 120 days.
Among other atmospheric variables, SABER provides measurements of temperature
from 15 µm CO2 emissions and OH
volume emission rates (VERs) from Abel inversions of the limb radiance
measured at 1.64 and 2.06 µm, sensitive to Δv=2 transitions
of v=4,5 and v=8,9 vibrational levels, respectively .
We use version 2.0 data, publicly available at http://saber.gats-inc.com. OH VERs used here are unfiltered, so the band
contribution outside the channel bandpass is taken into account
. Errors in temperature around the OH layer at low latitudes to midlatitudes are estimated to be 3.5 K (systematic) and 3.3 K (random)
. Systematic errors in OH VER are less than 5 %
. Pointing altitude is inferred from the satellite
position with an error smaller than 200 m.
Since SABER data are to be expressed as a function of ground-based instrument
OH measurements in this work, we use vertically integrated quantities
throughout this paper. We define SABER OH intensity as the vertically
integrated SABER OH VER voh: Ioh=∫vohdz‾. SABER OH altitudes used here are vertically weighted with
SABER OH VER, zoh=∫vohzdz/∫vohdz‾. The SABER OH equivalent temperatures are
obtained in the same fashion as the rotational temperatures are retrieved
from the ground-based instrument . The ratio
rJ′ of the intensity IJ′ and the Einstein coefficient AJ′ for
each rotational line J′ is estimated with
rJ′=∫vohe-EJ′/kTkdz‾,
where EJ′ is the energy of the rotational transition, k is the
Boltzmann constant and Tk the SABER temperature retrieved from its
measurements at 15 µm. Then, the negative inverse of the slope of
the linear fit between lnrJ′ and EJ′/k is the equivalent
rotational temperature. These differ from OH VER vertically weighted
temperatures at SATI latitudes by less than 1 K on average but avoid
potentially larger differences when temperature vertical gradients are steep
. We note that equivalent temperatures calculated this way are
independent of the Einstein coefficients used.
SATI measurements are not sensitive to the same OH vibrational bands as
SABER. Models and measurements show that the altitude of the band peak
emission depends on its upper vibrational level, mainly due to deactivation
by atomic oxygen . The work of showed that
the peak altitude difference increases by 0.6 km per increasing vibrational
level, assuming linear dependence. reported shifts of
0.4 km for adjacent levels. Therefore, there should be offsets of roughly
+1 and -1.5 km between the peak altitude in SATI and in SABER 1.6 and
2.0 µm channels, respectively. The peak altitude of the vibrational
levels depends on atomic oxygen abundance, which suffers temporal variations,
and so should the altitude offsets between SATI and SABER 1.6 and
2.0 µm measurements. In order to minimize the impact of the offset
variations on the estimation of SATI peak altitudes (sensitive to v=6), we
used the mean of SABER 1.6 and 2.0 µm intensities (sensitive to
v=4,5 and v=8,9, respectively) and the corresponding OH temperature and
altitudes means.
SABER OH intensity (a), equivalent temperature
(b), VER-weighted altitude (c) and residual altitude
(d) after fitting the empirical function (Eq. ). Black:
SABER measurements; red: deseasonalized values (12-month moving averages
subtracted); blue: trend (12-month moving averages and solar linear component
subtracted).
SABER OH intensity (a, d), equivalent temperature
(b, e) and layer altitude (c, f) variations with the day of
the year (a, b, c) and the solar local time (d, e, f).
Colors correspond to local solar time (a)–(c) and to
months of the year (d)–(f) (red: JF, green: MA, dark
blue: MJ, light blue: JA, pink: SO, orange: ND).
Determination of the OH altitude empirical function from SABER
expressed the altitude of the OH layer as a linear function
of the OH intensity that was corrected with annual, semiannual, diurnal and
semidiurnal sinusoidal oscillations and a linear solar term.
added OH intensity squared in the expression. Thus, they
needed 10 and 11 coefficients, respectively, those related to the intensity
plus two for each oscillation (eight in total) and one for the solar term.
Our aim here is to select an empirical function that better reproduces the
layer altitude as a function of the measured variables with a reduced number
of coefficients. In addition to OH intensity, temperature embeds additional
information on the atmospheric dynamics and chemistry affecting the OH layer
altitude, suggesting the inclusion of simultaneously measured temperatures as
a predictor.
Figure shows time series of SABER nighttime OH
intensity, temperature and altitude. The data are first binned in 1 h,
±7∘ longitude and ±5∘ latitude around the SATI location.
This leaves data in one or two local time bins per day. The layer altitude
and intensity exhibit a semiannual oscillation (minima in solstice and
equinoxes), which is somewhat more marked on the intensity (see
also Fig. ). The altitude has an additional annual
variation, with minima in the winter solstice. Temperature presents a marked
annual oscillation in antiphase with that of the altitude.
The semiannual variation in OH intensities has been measured in the past. It
is not fully understood but it is believed that it is affected by Kelvin and
gravity waves and from a semiannual variation in the
diurnal tide amplitude .
mention that the OH airglow emission rate is not correlated with temperature
in the same manner at all altitudes. They discuss that, at the Equator, these
variables are positively correlated below 94 km (where O vertical
transport dominates) and negatively correlated above (where the temperature
dependence of photochemical reactions and atmospheric density dominate).
The different seasonal variations in SABER OH intensity and equivalent
temperature shown in Figs.
and might be reflecting different responses due to
a seasonally varying layer altitude.
Mean, trend and solar component slopes and correlations of
deseasonalized SABER OH-VER-weighted altitude, vertically integrated OH VER
(intensity) and OH-VER-weighted temperature. Means are expressed in meters,
erg cm-2 s-1 and K, respectively. Trend slopes are expressed in
meters per decade, % per decade and K per decade. Solar slopes are
expressed in meters per 100 s.f.u., % per 100 s.f.u. and K per
100 s.f.u.
In order to determine the relationships between trends and responses to solar
variation, we have calculated the 12-month moving averages for OH intensity,
equivalent temperature and VER-weighted altitude (red lines in
Fig. ). Since these are 12-month means (on average
700 measurements), the errors in the data are then reduced to ±0.2 %,
±0.1 K and ±7 m, respectively. We then fitted trend and solar flux
(F10.7; GSFC Space Physics Data Facility; omniweb.gsfc.nasa.gov) linear
components. The fit coefficients are shown in Table . Both
OH temperature and intensity exhibit correlations with the solar flux. The
effect (3.9 K per 100 s.f.u. and 7.8 % per 100 s.f.u.)
is smaller than the seasonal and daily variations. The OH equivalent
temperature solar response is in agreement with that derived by
and (4.89 K per 100 s.f.u. and
4.2 K per 100 s.f.u., respectively) but larger than that of
(1.2 K per 100 s.f.u. for SLOAN and 2.7 K per 100 s.f.u.
for MLS). In contrast, the OH layer altitude is not significantly affected by
the solar cycle. also found a clear dependence of the OH peak
intensities on the solar cycle but not an obvious one for the layer
altitude. The correlation coefficients for trends are small, showing that the
dependence is not necessarily linear. Nevertheless, the slopes show a long-term tendency of decreasing altitude and temperature, -40 m decade-1
and -0.6 K decade-1, respectively. The altitude decrease is slightly
faster than that derived by (-20 m decade-1). The
decadal OH equivalent temperature decrease derived here is
0.1 K decade-1 stronger than that of kinetic temperature at 88 km
previously reported for SABER at midlatitudes. The OH
intensity increases 0.7 % decade-1, opposite to the negative trend
derived by but in agreement with the expected increase in
intensity with decreasing altitude.
Figure shows the dependence among SABER OH intensity,
equivalent temperature and altitude. The OH altitude generally changes
quasi-linearly with OH intensity, but there seems to be an offset in the
winter measurements with respect to the rest of the year. Indeed, the
altitude displays a semiannual oscillation (see Fig. b)
that is asymmetric, whereas that of the intensity is not (see Fig. a).
We note that this asymmetry is in antiphase
with the temperature annual variation. This suggests that temperature
provides additional information for the altitude prediction. Furthermore, as
mentioned above, the correlation between airglow emission and temperature is
altitude dependent , meaning that the relative information
content of these variables depends on the altitude of the OH layer. Indeed,
the seasonal change in the intensity–temperature relationship
(Fig. b, d) is most likely related to the seasonal change
in the relative importance of the chemistry over dynamics. As
discuss, higher OH altitudes are dominated by chemistry, larger temperatures
implying lower intensities, whereas lower altitudes are dominated by
dynamics, with larger temperatures implying larger intensities. From March to
June when the altitude of the layer is high, chemistry is relatively more
important and the intensity–temperature dependence has a small (even
negative) slope. Also, SABER intensity, temperature and altitude variations
with LST reveal the effect of semidiurnal and diurnal tides (lower row in
Fig. ) that change in amplitude over the year, although
not in the same way for the three variables (compare, for example, the
relatively larger intensity variations with LST in March and April with the
smaller variations in temperature and altitude during the same period). This
points to the consideration of oscillation amplitudes varying with time.
The facts described here support the inclusion of the measured temperature as
a predictor. The same conclusion follows from a theoretical perspective.
There are two chemical sources of vibrationally excited OH in the mesopause:
H + O3→ OH(v=1-9) + O2 and
O + HO2→ OH(v=1-6) + O2. The first one dominates. The excited OH losses are
governed by collisions with molecular and atomic oxygen and spontaneous
emission . Removal in collisions with molecular
nitrogen and in chemical reactions with atomic oxygen also occur but do not
significantly affect the OH(v) population
. Assuming photochemical equilibrium
(both for OH and O3), approximated the OH
number density for the v level, [OH(v)]. Using the ideal gas law in his
Eq. (20) and solving for the pressure, we obtain:
p≈A[OH(v)]1/2T2.2(BVMRO-1+C)-1/2,
where VMRO is the volume mixing ratio (VMR) of atomic oxygen, T
is temperature, p is pressure and A, B and C are constants. Assuming
hydrostatic equilibrium, z=-∫kbTmgd(lnp) and
using p as defined by Eq. (), we suggest the use of an
empirical function for the peak altitude depending on
z=f(Tln([OH(v)]),TlnT,fO),
where fO is a function of the atomic oxygen VMR, but simultaneous ground-based
O measurements are not generally available. Thus, we do not use the last
term. Nevertheless, the dynamical processes that trigger daily and seasonal
variation in the nighttime atomic oxygen also affect the temperature and
should somehow be embedded in the temperature. Another caveat of this
approach is that we have assumed an isothermal atmosphere in the vicinity of
the OH layer.
Relationship between SABER OH layer altitude and intensity
(a), Toh×lnIoh(b) and equivalent
temperature (c) and between equivalent temperature and intensity
(d). Colors are the months of the year (red: JF, green: MA, dark
blue: MJ, light blue: JA, pink: SO, orange: ND).
SABER OH layer altitude
residuals (plus mean altitude) after subtracting only the fitted Toh×lnIoh and Toh×lnToh terms
(a)
and all the fitted terms (b) of Eq. (). Color code as
in Fig. .
SABER OH-VER-weighted altitude fit coefficients and diagnostics of
Eq. (). Resulting altitude in meters for vertically integrated
OH VER (Ioh) given in erg cm-2 s-1, OH-VER-weighted
temperatures (Toh) in K and LST in hours.
Colocated SABER vertically integrated OH VER vs. SATI OH
intensities (a) and SABER OH-weighted temperatures vs. SATI OH
rotational temperatures (b). Solid line is the linear
fit.
We also note that, instead of [OH(v)], the OH vertically integrated
emission rate or intensity, Ioh, is the available measurement from the
ground. We note that OH VER is proportional to the OH(v) concentration and,
as showed, Ioh is directly proportional to the peak VER.
Furthermore, we also used OH-VER-weighted altitude (zoh) and equivalent
temperature (Toh), or simply OH altitude and temperature, similar
to what the ground-based instruments observe.
SABER OH altitude linear correlation with Toh×lnIoh is
better than with Ioh (-0.85 compared to -0.77; compare top row
panels in Fig. ). Its correlation is further improved
(0.88) with the sun-corrected intensity when the F10.7 linear
component (see Table ) is subtracted. This is because the
OH altitude is not strongly correlated with the solar cycle. We also found
after thorough testing that we improved the fit of SABER nighttime
measurements when adding a sinusoidal semiannual correction weighted with
temperature. This time-dependent amplitude might be representative of a
time-dependent source of the semiannual oscillation (either Kelvin waves, gravity
waves or tides). Considering additional seasonal and daily oscillations did
not significantly improve the goodness of the fit. We only further decreased
the residuals when taking into account a linear local solar time term
(compared to the diurnal and semidiurnal oscillations used by
). The multiple linear correlation coefficient is then 0.93
(see Table ).
Putting all the above together, we adopted the following empirical formula:
zoh=sITTohlnIoh+sTTohlnToh+ssao1Tohsin(2π182.5d)+ssao2Tohcos(2π182.5d)+sLSTLST+c,
where d is the day of the year, sIT, sT,
ssao1, ssao2 and sLST are the slopes of
the regression and c is a constant.
Figure shows the change in the residual OH layer altitude
as the terms in Eq. () are fitted. When fitting only the first
two terms in the equation (top row), seasonal and local time components still
remain and the standard deviation of the residual is still rather significant
(400 m). These components mainly disappear when also fitting the temperature-weighted semiannual oscillation and the linear local time terms (bottom
row). Then, the standard deviation of the residual is reduced to 250 m (see
also Fig. d).
Table shows the coefficients that result from the fit of
SABER OH temperatures (K), sun-corrected OH intensity (in
erg cm-2 s-1) and LST in hours from midnight to SABER OH altitude
(in meters) by using Eq. (). The multiple linear correlation and
the residual standard deviation are similar to what we obtain by using
the function. Nevertheless, we only need five parameters and a
constant term. This relationship applies for midlatitudes. Since low and
high latitudes are affected by dynamics and chemistry to a different extent,
the expression should be revised and further terms might need to be
considered in those cases.
We recall that the use of Eq. () requires using sun-corrected OH
intensities. This mainly improves the fit (it reduces the standard deviation
of the residual). However, it is not always possible to correct the measured
ground-based signal from the sun contribution (for example, if a dataset is
not long enough or has gaps). In that case, the solar slope in
Table shall be used.
SATI (black) nighttime OH predicted layer altitude (top), total band
intensity (middle) and rotational temperature (bottom) for four nights in
October 2009 (a) and from 2 August 2013 to 14 August 2013
(b). Fit to a combination of oscillations is shown in orange (see
text). Purple asterisks are SABER colocated measurements (intensities and
temperatures have been transferred to SATI scale).
Results for the linear transfer function from SATI to SABER
measurements. Slope is expressed in (erg cm-2 s-1) A.U.-1
for intensity and is unitless for temperature. Drifts are expressed in %
per year and K per year, respectively. Constant units are in
erg cm-2 s-1 and K, respectively.
SlopeDriftConstantCorrIntensity(1.66±0.06)×10-41.3 ± 0.20.0520.80Temperature1.05 ± 0.03-0.80 ± 0.06-5.540.81Application to SATI-OSN
Equation and the corresponding coefficients derived from SABER
in Table predict OH altitude from OH intensities and
temperatures simultaneously measured from the ground at northern
midlatitudes. We apply these to SATI measurements from the Sierra Nevada Observatory
(Spain) for a subsequent exploration of nocturnal variability
in the three variables simultaneously. Before applying the equation and in
order to avoid potential errors due to disagreements between SATI and SABER,
it is required to determine the transfer functions between SATI OH
intensities, Ig, and temperatures, Tg, and between SABER vertically
integrated OH volume emission rates, Ioh, and OH equivalent
temperatures, Toh. For this purpose, we use SABER and SATI
colocated measurements. We selected SATI and SABER measurements with
SZA > 100∘ and taken within ±1 h, ±5∘
latitude and ±7∘ longitude. The natural variability within
these ranges at midlatitudes is mainly caused by waves. According to the results
shown in , the expected maximum intensity and
temperature differences due to that space–time mismatch are smaller than
15 % and 6 K, respectively. Nevertheless, we find on average around 20
SATI measurements colocated with each SABER measurement, reducing the
average time–space mismatch and the associated differences to 3 % and
1 K, respectively. Relaxing the colocation criteria reduces the correlation
between SATI and SABER measurements, and constraining them does not
significantly change the results but reduces the number of coincidences.
We found 14 113 SABER–SATI pairs from 2002 to 2015. We averaged SATI
colocations around each SABER measurement, leading to 852 coincidences.
Comparisons between measurements are shown in Fig. . We
use a linear SATI-to-SABER transfer function. We also allow for linear drifts
between instruments that may include, for example, those due to aging:
Xoh=m(Xg+dt)+n,
where X is either OH intensity or temperature, t is time, d is the
relative drift, m is the slope, n is a constant and the oh and g
subindices correspond to the satellite and the ground-based instrument
variables, respectively.
Table shows the retrieved values for slopes, drifts and
constants after performing the regressions. There is a 1.3 % per year
relative drift between SATI and SABER intensities, the SATI signal being
relatively smaller with time, probably due to faster aging. The mean SABER
and SATI temperature difference is 5.6 K, which is in agreement with previous results
from . Temperature differences do not strongly
depend on temperature, so the slope is close to unity. This indicates
that the errors in the Einstein coefficients used in SATI retrievals are not
significant, as concluded using a similar approach. The
relative temperature drift is -0.8 K yr-1, with SATI measuring higher
temperatures with time relative to SABER. A reason for this drift might be
nonlinear aging of SATI, resulting in a response depending on wavelength
and consequently affecting the derived rotational temperature. In
principle, this fact suggests using SATI data for trend analysis with
caution, except for measurements taken at early stages when an accurate
calibration was performed. Nevertheless, we do not rule out a SABER
contribution to this drift. We note that found a
0.7 K yr-1 bias trend in SABER version 1.07 temperatures (we use
version 2.0) by comparing them with ground-based OH and the Aura Microwave Limb
Sounder data at Davis, Antarctica, with SABER getting warmer with time. Either
way, SATI measurements are still valid for temperature wave analyses because
these deal with relative changes and, as shown above, temperature differences
do not depend on absolute temperature (the slope in Eq. is
close to 1).
SATI 2002–2015 OH predicted altitude (a), total band
intensity (b) and rotational temperature (c) peak-to-peak
nocturnal variability (2-σ) versus day of the year: daily values
(black dots), and 60-day running means (black line) and their standard
deviation (orange).
We used Eq. () with the coefficients in
Table to transfer SATI-to-SABER intensities and
temperatures and then used Eq. () with the coefficients in
Table to predict the OH layer altitude at high temporal
sampling (2 min) from SATI measurements. We present the wave decomposition
of the three variables for two typical cases, showing the potential and
abilities of the method (Fig. ).
The first case (Fig. a) shows a typical example
(October 2009) in which tidal variation in SATI OH intensity, temperature and
predicted altitude are superimposed to a quasi-2-day planetary wave.
Comparisons with SABER colocated measurements transferred to SATI scale
using Eq. () (also plotted) show reasonable agreement with
SATI data for the three variables. After determining the significant
components in SATI data from the Lomb–Scargle periodogram, we fitted the data
to a composite of diurnal, semidiurnal, terdiurnal and 1.7-day
oscillations (shown in orange in the figure). That yielded amplitudes of
0.6, 1.2, 0.25 and 0.6 km (with errors around ±0.1 km) in
altitude associated with 15, 45, 15 and 15 % (with errors around
±2 %) intensity amplitudes and 7, 9, 4 and 5 K temperature
amplitudes, respectively (with errors around ±1 K). Their combinations produced
overall peak-to-peak variations along this period of 4.5 km in altitude, a
factor of 3 in intensity (with respect to the lowest value) and 40 K in
temperature. Waves do not contribute in equal proportion to the three
variables. Whereas the terdiurnal component is similar to the diurnal for
intensity, altitude and temperature present amplitudes half as strong.
Additionally, altitude variations are in antiphase with temperature and
intensity variations. Tidal altitude amplitudes are on the order of
previously measured values e.g.,. We recall that the
displacements of the OH layer cause SATI OH temperature variations not to
coincide with kinetic temperature variations occurring at a fixed altitude.
Indeed, an examination of the colocated SABER temperature gradients shows
that temperature decreases around 3–4 K km-1 at 80–90 km. Thus,
around 15 K of the overall OH temperature change is due to OH altitude.
Slight night-to-night changes in the tidal amplitudes are also detected by
SATI.
In the second case, nighttime SATI OH intensity, temperature and predicted
altitude from 2 August 2013 to 14 August 2013 (Fig. b)
presented a wealth of superposed oscillations. SABER colocated measurements
(also plotted) agree well with SATI data. The high temporal sampling in SATI
data allowed for the extraction of significant (98 % significance in the power
spectrum) semidiurnal, terdiurnal, 1.8-day and 2.3-day wave
contributions to the signal. The fit to the combination of those oscillations
yielded amplitudes of 0.5, 0.3, 0.7 and 0.8 km, respectively, in altitude
associated with intensity amplitudes of 10 % for the first three modes
and 20 % for the latter and temperature amplitudes of 5, 8 and 7 K for
the latter two modes.
We have also studied SATI OH altitude, intensity and temperature nocturnal
variability for all nights from 2002 to 2015. In order to reduce artifacts
related to a varying time sampling over the year (seasonal variation in the
length of the night), we use variabilities only within 6 h of measurements.
We have estimated the peak-to-peak nocturnal variability for each night
(nocturnal variability hereafter) as 2 times the maximum standard deviation
of the 6 h running bins (2-σ). Since we do not strictly use
peak-to-peak differences, these estimations are conservative. We also note
that the semidiurnal tide dominates SATI nighttime variability
.
The black dots in Fig. shows the nocturnal variability
(2-σ) versus the day of the year (DOI) for altitude, intensity and
temperature. These deviations from the nighttime mean values are caused by
waves (tides, planetary waves and, plausibly, large-scale gravity waves). The
scattering of the dots around each DOI is striking. Instruments providing
measurements at low temporal resolution mask this day-to-day variability. We
calculated 60-day running means to mimic a 2-month temporal resolution (see
Fig. ). The OH altitude peak-to-peak nocturnal
variability bimonthly mean exhibits mainly annual variation (0.5 km
smaller in midsummer than at the beginning of January), with a slight
increase in May (following a more significant rise in intensity but not in
temperature). This seasonal variation is related to and in phase with those
corresponding to the intensity and the temperature, which exhibit semiannual
(maximum values in early winter and late spring) and annual (minimum values
in late spring) oscillations, respectively.
The standard deviations of these nocturnal variability bimonthly means for
each calendar month are also shown in Fig. . In these
standard deviations, the contribution of instrumental random errors
(precision) is superposed on that of wave variability. The standard deviation
of the layer altitude nocturnal variability in autumn (±0.9 km standard
deviation) is significantly larger than from late spring to summer
(±0.3 km standard deviation). As an upper limit of our estimation, these
deviations are assigned completely to wave variability. To estimate the lower
limit, we assign the minimum standard deviation to precision (±0.3 km)
and assume a constant precision through the year. Then, considering the root
square sum of deviations, the change from minimum to maximum standard
deviations leads to wave variations of at least ±0.8 km within each
60-day period during autumn. The intensity and the temperature nocturnal
variabilities have standard deviations changing from
±175 A.U. (±55 %) in December to ±110 A.U. (±40 %)
in spring, and from ±7 K in January to ±4 K from late spring to
August. Analogous to the reasoning for altitude, these become the upper
limits for intensity and temperature wave variability within 60 days for
those seasons. The lower limit is then ±45 % variability for
intensity waves in December and ±6 K for temperature waves in January.
Summary and conclusions
An understanding of the variations in the OH layer from ground-based
measurements on multiple timescales relies on simultaneous knowledge of
the intensities, temperatures and altitudes of the emitting layer. A ground-based
OH airglow instrument cannot directly observe the OH altitude of the layer
from which the measured emission emanates. Similar to the approaches of
and , we provide in this work an
empirical formula to predict the altitude of the OH layer from airglow
intensities and temperatures at midlatitudes. The expression was determined
by fitting vertically integrated OH volume emission rates and OH equivalent
temperatures measured by SABER (version 2.0) from 2002 to 2015.
The empirical formula derived in this work takes into account not only OH
intensity, as and did, but also
dependence on OH temperature. Additional information on altitude variations
embedded in temperature (for example, processes not altering OH intensity or
doing it in a different manner) is also taken into account by this method. By
including both variables and only two more terms, namely, a
temperature-weighted semiannual oscillation and a local time linear term, we
fitted the SABER OH layer altitude at midlatitudes with a similar accuracy
(250 m) to that by the earlier studies. That is, we obtain a similar
residual standard deviation by using only six parameters (compared to 10 and
11 coefficients that the abovementioned authors needed, respectively). Since lower and
higher latitudes are affected by dynamics and chemistry to a different
extent, the expression should be revised and further terms might need to be
considered under those conditions.
In the course of the analysis of SABER data spanning more than a solar
cycle, we inferred a descent of 40 m decade-1 in OH altitude, an
increase of 0.7 % decade-1 in OH intensity and a decrease of
0.6 K decade-1 in OH temperature. As previously reported, we found a
significant correlation between intensity and temperature with the solar
cycle (7.8 % per 100 s.f.u. and 3.9 K per 100 s.f.u.) but not for
altitude.
We applied the altitude empirical formula derived from SABER to 2002–2015
SATI airglow spectrometer measurements taken over the Sierra
Nevada Observatory (Spain) in order to examine OH intensity, temperature and altitude
nocturnal variabilities simultaneously. SATI OH temperature and intensity
measurements were previously transferred to the SABER reference system. The
transfer functions relating both instrument measurements, which were derived using
colocated measurements from 2002–2015, included a constant-with-time linear
term allowing for biases between instruments and a time variable term
allowing for relative drifts. The relative drift between SATI and SABER
intensities is -1.3 % yr-1, plausibly due to faster SATI aging.
The derived slope for temperature is close to unity, which suggests that the
Einstein coefficients assumed in SATI retrievals are adequate. We found an
average 5.6 K SATI–SABER temperature difference. SATI temperature drift is
positive relative to SABER (0.8 K yr-1). Nonlinear aging of SATI,
depending on wavelength and affecting the derived rotational temperature,
might explain this bias drift. Nevertheless, we cannot rule out a
contribution from SABER. This result evidences the need for accurate
traceability of the intensity responsivity of ground-based spectrometers.
Even so, we note that SATI data are still valid for wave analyses in which only
relative changes are used.
We examined SATI typical cases showing the convenience of using this approach
when inspecting the OH layer nocturnal variability from ground-based
measurements. In both cases, predicted altitudes from SATI temperatures and
intensities agree well with SABER colocated observations. SATI measurements
allowed us to decompose the overlapping wave components with a high temporal
resolution. We measured vertical variations of more than 4 km between two
consecutive nights due to the combined effect of tides and planetary waves
that accompanied 100 % and 40 K changes in intensity and temperature,
respectively. The SABER colocated temperature gradients suggest that, in
this case, the 4 km vertical displacement is responsible for around 15 K
of temperature change.
An additional advantage of using SATI measurements is that the temporal
resolution permits the evaluation of day-to-day wave variability. Our
estimations suggest that peak-to-peak OH nocturnal variability, mainly caused
by wave variability, changes within 60 days at least 0.8 km for altitude in
autumn, 45 % for intensity in early winter and 6 K for temperature in
midwinter. Plausible upper limit ranges of those variabilities are
0.3–0.9 km, 40–55 % and 4–7 K depending on season. This short-term
variability should be accounted for when studying waves and their impact
using circulation models at high temporal resolution. A comprehensive
analysis of (planetary and tidal) wave decomposition on predicted altitudes
for the complete SATI dataset will be the focus of a future study.
SATI data are available by request at the NDMC site
(http://wdc.dlr.de/ndmc). SABER data are publicly available at
http://saber.gats-inc.com.
The authors declare that they have no conflict of
interest.
Acknowledgements
MGC was financially supported by the MINECO under its “Ramon y Cajal”
subprogram. The IAA team was supported by the Spanish MINECO under project
ESP2014-54362-P and EC FEDER funds. We acknowledge the support of the
Observatorio de Sierra Nevada staff for the maintenance of SATI over the
years. The topical editor,
Christoph Jacobi, thanks John French and one anonymous referee for help in
evaluating this paper.
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