ANGEOAnnales GeophysicaeANGEOAnn. Geophys.1432-0576Copernicus PublicationsGöttingen, Germany10.5194/angeo-35-181-2017Large-scale gravity wave perturbations in the mesopause region above Northern
Hemisphere midlatitudes during autumnal equinox: a joint study by the USU Na
lidar and Whole Atmosphere Community Climate ModelCaiXuguanghttps://orcid.org/0000-0003-4632-1697YuanTaotitus.yuan@usu.eduLiuHan-LiPhysics department of Utah State University, Logan, Utah, UT 84115, USACenter of Atmosphere and Space Science (CASS) Utah State University,
Logan, Utah, UT 84322, USAHigh Altitude Observatory, National Center for Atmospheric Research,
Boulder, Colorado, CO 80307, USATao Yuan (titus.yuan@usu.edu)2February20173521811888November201630December20166January2017This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://angeo.copernicus.org/articles/35/181/2017/angeo-35-181-2017.htmlThe full text article is available as a PDF file from https://angeo.copernicus.org/articles/35/181/2017/angeo-35-181-2017.pdf
To investigate gravity wave (GW) perturbations in the
midlatitude mesopause region during boreal equinox, 433 h of continuous
Na lidar full diurnal cycle temperature measurements in September between
2011 and 2015 are utilized to derive the monthly profiles of GW-induced
temperature variance, T′2, and the potential energy density (PED).
Operating at Utah State University (42∘ N, 112∘ W),
these lidar measurements reveal severe GW dissipation near 90 km, where both
parameters drop to their minima (∼ 20 K2 and
∼ 50 m2 s-2, respectively). The study also shows that
GWs with periods of 3–5 h dominate the midlatitude mesopause region during
the summer–winter transition. To derive the precise temperature
perturbations a new tide removal algorithm suitable for all ground-based
observations is developed to de-trend the lidar temperature measurements and
to isolate GW-induced perturbations. It removes the tidal perturbations
completely and provides the most accurate GW perturbations for the ground-based observations. This algorithm is validated by comparing the true GW
perturbations in the latest mesoscale-resolving Whole Atmosphere Community
Climate Model (WACCM) with those derived from the WACCM local outputs by
applying this newly developed tidal removal algorithm.
Ionosphere (wave propagation) – meteorology and atmospheric dynamics (middle atmosphere dynamics; waves and tides)Introduction
Gravity wave (GW) forcing and its associated spatial and temporal spectrum
within the mesosphere/lower thermosphere (MLT) are the key parameters for
understanding the energy and momentum transfers between the lower and upper
atmosphere GWs (Fritts and Alexander, 2003). GWs also play critical roles
in various coupling processes between neutral atmosphere and the ionosphere
(Vadas and Liu, 2013; Liu and Vadas, 2013). Mainly generated in the
troposphere by orography, convection, and jet-front systems, the GWs
propagate upward and horizontally throughout the atmosphere. Due to
conservation of wave energy density, the wave amplitudes increase as they
propagate into higher altitudes to compensate for the decreasing air
density, until they become unstable and break mostly in MLT region.
Based on Ern et al. (2004), GW potential energy density (PED) is directly
related to the GW momentum flux (MF) and the associated body forcing on the
mean flow. Indeed, Yuan et al. (2016) successfully derived the momentum flux
of a GW packet through this approach by using the coordinated observations
from the Na lidar and a co-located mesospheric temperature mapper (Pautet et
al., 2014). The calculation of PED requires precise measurements of both
slow varying background temperature modulated by large-scale tidal waves and
the GW-induced temperature perturbations through carefully de-trending of
the data. So far, experimental investigations on GW perturbations and
dynamics have mostly focused on nocturnal small-scale individual events with short
periods (Bossert et al., 2015; Fritts et al., 2014; Cai et al., 2014; Yuan
et al., 2016). Except for a few studies in the polar region, long-period
large-scale GWs, especially inertia GWs, which play critical roles in MLT
dynamics, are not well studied, leaving contributions from most of these
waves unchecked. The ground-based experimental studies on climatological GW
perturbations (Gardner and Liu, 2007; Rauthe et al., 2006, 2008) have been
limited to nocturnal observations as well with various de-trending
algorithms developed, trying to remove tidal biases from the measurements.
At the same time, several bandpass filter techniques are also be attempted
and utilized in similar GW investigations. However, it is quite difficult to
set the frequency range to cover the whole GW temporal spectrum, because
some inertia GWs can have quite low frequencies that are close to those of
tidal waves (diurnal tide excluded). For example, the GW inertial period is
18.44 h at Utah State University (USU) based on the inertial frequency ω=2Ωsinϕ, where Ω is the Earth rotation rate and
ϕ is latitude. In addition to these challenges on the reliable
measurements of GW perturbations, large tidal day-to-day variability makes
it extremely difficult to calculate the tidal wave modulations precisely,
not to mention distilling GW perturbations, even for the spaceborne
observations with global-scale coverage.
In this paper, we utilize the full diurnal cycle temperature observations
(a total of 433 h) in the month of September between 2011 and 2015, taken
by the USU Na lidar, to calculate the monthly temperature variance and PED
during boreal autumnal equinox in the midlatitude mesopause region. Here we
develop a new algorithm to de-trend and remove the tidal bias in the lidar
temperature measurements. To evaluate this new algorithm, we introduce the
latest Whole Atmosphere Community Climate Model (WACCM) mesoscale simulation
results (Liu et al., 2014), which generate global distribution and
variations in GW. This new and unique study covers the GW spectrum up to
inertia period, providing the most comprehensive large-scale GW results from ground-based observations. As the only investigation of this kind based on
full diurnal cycle observations, these results could be utilized to evaluate
the current effort of GWs simulations from all whole-atmosphere general
circulation models, such as WACCM (Garcia et al., 2007), that can simulate
GW impacts and evolution from the surface up to the lower thermosphere.
Instrumentation and model involved
The USU Na lidar is a narrowband resonance fluorescence Doppler lidar system
operating at the Na D2 line with a 120 MHz full width at half maximum (FWHM) laser
pulse bandwidth. It has the capability of measuring high-resolution
temperature and horizontal wind profiles within the mesopause region (80–105 km)
in full diurnal cycles (Krueger et al., 2015). Collaborating with the
other surrounding instruments, such as the Advanced Mesospheric Temperature
Mapper (Pautet et al., 2014) and Meteor Wind Radar, the lidar can provide
critical measurements of tidal and planetary wave variations (She et
al., 2004; Yuan et al., 2008, 2010). The high-resolution lidar measurements
also provide detailed description on GW activities and their impacts in the
MLT (Yuan et al., 2014, 2016; Cai et al., 2014; Fritts et al., 2014).
WACCM is a comprehensive numerical model covering the altitude range between
the surface and thermosphere. It uses the National Center for Atmosphere
Research (NCAR) Community Earth System Model (CESM) as a common numerical
framework (Garcia et al., 2007). Liu et al. (2014 and the references
within) developed a high-resolution WACCM run based on the framework of
WACCM version 5 and simulated GW activity in mesoscale globally from the
troposphere to the lower thermosphere.
The lidar data are first processed with 1 h temporal resolution and
smoothed vertically through a moving 2 km FWHM
Hanning window. The temperature measurements with uncertainty equal or
larger than 15 K are treated as bad data. There are a total of 433 h of
continuous full diurnal cycle lidar data at USU involved in this study.
These September data include 116 h in 2011; 73 h in 2012, with one gap
of less than a day; 64 h in 2013; 89 h in 2014; and 91 h in 2015.
The WACCM temperature simulations included in this study are from 1 to 10 September at 41∘ N from 0 to
360∘ longitude, with zonal resolution of 0.3125∘ and
temporal resolution of 1 h. As with the lidar data processing, the WACCM
hourly temperature outputs are also smoothed with a 2 km FWHM Hanning window
in the vertical direction.
MethodologyLidar data
From Ern et al. (2004), the PED can be calculated as follows:
PED=12g2N2T′2T‾2,
where g= 9.5 m s-2 is the gravitational constant; T‾ is the
mean temperature with tidal modulations removed; T′2is the variance
of the temperature perturbation T′, which is the fitting residual that will be
discussed later; and N2=gT‾dT‾dz+gCP is the background Brunt–Vaisala frequency
squared with the lapse rate gCP= 9.5 K km-1 in mesopause
region.
To calculate GW-induced temperature perturbations at each lidar-measured
altitude, the slowly varying background temperature, T0, has to be
calculated and subtracted from the lidar data. This can be achieved through
a two-step algorithm. First we apply a least-squares fitting (LSF) algorithm
(shown in Eq. ) that covers all the tidal periods on the multi-day
continuous lidar measurements to derive the mean temperature and tidal
period perturbations, provided that tidal period inertia GWs do not exist or
are insignificant. Second, by utilizing the derived mean temperature and
tidal period perturbations in step 1 we reconstruct the background
temperature, T0, in the mesopause region as follows:
T0=T‾+∑i=14Aicos2πi24t-ϕi,
where T‾ is the mean temperature, i=1 for the diurnal tide,
i=2 for the semi-diurnal tide, i=3 for the terdiurnal tide, i=4 for
the quardiurnal tide, and ϕi represents the tidal phase for the
ith tidal component. Considering the large day-to-day tidal
variability, a 24 h fitting window sliding across the multi-day lidar
measurements at 1 h step is applied. The abovementioned LSF is conducted
within each of the fitting windows to derive the tidal period perturbations
that are regarded as the representatives of solar thermal tidal waves within
the fitting window. These derived tidal period perturbations, along with the simultaneously deduced mean
temperature, T‾, are utilized to reconstruct the slow varying
background temperature, T0. After de-trending the temperature at each
lidar altitude, the differences between the lidar temperature measurements
and this reconstructed background temperature are treated as GW-induced
temperature perturbations, T′. The temperature variance, T′2, for
PED calculation shown in Eq. () can be calculated within the fitting window
during the same process. The window then moves 1 h forward to repeat the
above process until it reaches the end of the data set. This algorithm is
applied on the full diurnal cycle lidar temperature measurements for each
September to achieve the monthly profiles of temperature variance and PED of
the year. The final mean monthly profiles of these two parameters,
representing the mean GW activities, are achieved by averaging all these
September results.
Figure 1 shows an example of this new method of determining the GW-induced
temperature perturbations based on a Na lidar campaign from UT day of year
(DOY) 252 to 255 (9 to 12 September) in 2015. Figure 1a is the
comparison between the hourly lidar temperature measurements and the
reconstructed slow varying background temperature, T0, at 89, 92 and 95 km based on the LSF described in the previous paragraph. Figure 1b is
the corresponding T′ at these three altitudes, which is the difference between
the lidar-measured temperatures and the reconstructed T0. As shown in
the Fig. 1a, the reconstructed T0 from this new method fits the lidar
temperature measurements extremely well, reproducing the large-scale
day-to-day variability in the MLT temperature. It also proves that the new
algorithm generates realistic background temperature and GW-induced
temperature perturbations.
(a) The lidar temperature measurements (black) and the reconstructed
background temperature (red) at 89, 92, and 95 km from UT day 252 to 256
in 2015. (b) The corresponding temperature perturbation. The temperatures at
92 and 95 km are increased by 50 and 100 K, respectively, and perturbations
at 92 and 95 km are increased by 20 and 40 K, respectively. The magenta
lines represent the zero-point line for the temperature perturbation.
Figure 2 indicates the monthly averaged temperature variance profiles (Fig. 2a)
and PED profiles (Fig. 2b) derived from this LSF algorithm. On the other
hand, to investigate the differences between these full diurnal cycle
results and those based solely upon nocturnal lidar measurements, we
reprocess the USU Na lidar nighttime data between 02:00 and 12:00 UT by
following the algorithm presented by Gardner and Liu (2007). In this
algorithm, the lidar-measured temperature variations at each lidar altitude
during the whole night are linearly fitted to a straight line to calculate
the background temperatures, T0. The justification is that large-scale
waves, such as tides and planetary waves, are all slowly varying and can be
treated as constant during the night. T0 is later subtracted from the
lidar-measured temperature to retrieve temperature perturbations. This is
followed by another linear fit in the vertical direction on the resulting
temperature perturbation profiles to calculate and remove the potential
tidal biases residual in the first fitting attempt. The justification is
that the tidal waves, especially the dominant semidiurnal tide at
midlatitudes, usually have very long vertical wavelength compared to the
lidar range. Thus, their effects could be further removed by this second
linear fitting. These resulting profiles from this approach, termed nightly
linear fit (NLF), are presented in Fig. 2 as well, alongside with those
derived from the LSF on the lidar's full diurnal cycle data.
The 5-year USU Na lidar September monthly climatology of (a) temperature
variance and (b) potential energy density (PED) obtained from the
LSF method (black) and the linear fit for nightly linear fit (NLF) (red).
WACCM results of (a) mean temperature, (b) temperature variance, and
(c) potential energy density (PED) calculated by utilizing LSF (black), ZW10R
(red), and the two-step NLF (blue). (d) Zonal mean zonal wind (red) on 5 September
in WACCM and the lidar-measured mean zonal wind (green).
Lomb spectra power of the lidar-measured temperature perturbations
during each of the six lidar campaigns in September between 2011 and 2015.
The DOY of each campaign is listed in the parentheses. Only components with
significant level larger than 50 % are plotted.
WACCM data
To evaluate this new LSF algorithm described above and see whether the
deduced GW perturbations can precisely represent those of the whole large-scale GW temporal spectrum, we introduce the latest high-resolution WACCM
simulations in September that are able to simulate global GW distribution
and variations. Here the WACCM data are processed in two completely
different approaches and two sets of GW perturbation results are generated.
The first approach is to process WACCM's high-resolution outputs at the USU
location with the same LSF tidal fitting algorithm discussed above,
generating background T0 and GW-induced temperature perturbations T′ and
variances T′2 along with the associated PED profiles. Again, we also
conduct the two-step fitting algorithm (NLF) for nighttime WACCM data at USU
to study the potential differences. The second approach is to fit each
hour's WACCM temperature data from longitude 0 to 360∘
at each altitude at 41∘ N with global-scale wave components of
zonal wavenumbers 0–10. These removed components with zonal wavenumber 0–10
are most likely induced by the major large-scale waves, such as tidal waves
and planetary waves, including the large-scale waves due to tidal-planetary
wave interactions. The background temperature T0 can be reconstructed
with these low wavenumber components, along with the zonal mean temperature.
The fitting residual of this zonal wavenumber removal approach, named as
ZW10R for the rest of the paper, are treated as GW-induced temperature
perturbations, T′. Finally, the T0,T‾ (zonal mean temperature)
and T′ at USU location are chosen to calculate T′2 and PED profiles, as
shown in Fig. 3b and c. This algorithm distinguishes GWs and
tidal/planetary waves from their spatial scale. Thus, with the complete
24 h coverage in this WACCM simulation, the associated results should be
the most reliable representation for the GW-induced temperature
perturbations and the related body forcing results generated within this
model simulation.
Results and discussion
As shown in Fig. 2a and b, the derived lidar temperature variance and PED
profiles for September share very similar vertical structure within the
mesopause region between 84 and 99 km. The errors represent the averaged
goodness of fit (chi-square divided by the difference between the numbers of
sampling and fitting parameters) in the temperature variance, T′2 profiles
and those propagating into the PED calculation. Both slowly
decrease to their minima from 84 km up to ∼ 89–90 km and
then increase quickly above this altitude. For example, the GW-induced
temperature variance drops from ∼ 50 K2 at 84 km to
∼ 20 K2 near 90 km but starts to increase fairly quickly
going into higher altitudes, close to 90 K2 near 99 km. It is worth
mentioning that such a “node” feature in temperature perturbation within
mesopause region has been reported in nocturnal lidar observations at
another midlatitude location (Rauthe et al., 2006) and low-latitude station
(Lu et al., 2009). This lidar-observed “node” feature suggests a strong GW
dissipation/saturation region near/below 90 km, where GWs deposit their
momentum and energy to the mean flow. This statement is partially supported
by large turbulence between 65 and 90 km observed by the Middle and Upper
atmosphere Radar (MU Radar) (Tsuda, 2014). This feature could be interpreted
by the simplified relation between the GW horizontal wind perturbations,
u′, and its vertical wavenumber, m, proposed by Booker and Bretherton (1967),
which indicates dramatic increasing of u′ and m when the GW is approaching its
critical level. However, to our knowledge no theoretical simulation has been
able to duplicate these observations, and therefore no solid dynamic scheme
has been provided.
Although T′2 and PED from WACCM do not have minima at 90 km as in the
observations, they do show local minima in the upper mesosphere
(∼ 82 km in ZWR10 and ∼ 78 km in LSF,
respectively). Based on GW linear saturation theory, when approaching its
critical level, the GW becomes unstable and experiences saturation, leading
to damping of GW amplitude due to the dissipation process (Fritts and
Alexander, 2003). Therefore, the “node” or the local minimum feature is
likely associated with the GW critical level decided by the local horizontal
wind profile. Figure 3d shows the comparison between the lidar-measured mean
zonal wind in September and the zonal mean zonal wind profile on 5 September
in WACCM. As shown in the figure, although the two wind profiles
are both eastward in the mesopause region, they are quite distinct. The
lidar-measured zonal wind is mostly slowing down as the altitude increases,
while the zonal mean zonal wind in WACCM reverses from westward to eastward
near 79 km and is accelerating eastward below 90 km. It is worth noting
that, in the mesopause region below 88 km, the lidar mean zonal wind is much
larger than the zonal wind of the model. The difference in background wind
and wind reversal level, which is most likely due to deficient GW forcing in
this simulation, probably results in the altitude difference of GW filtering
between the simulation and observations. This difference prevents direct
comparisons between the model results and those based on the lidar
measurements.
Compared to the LSF results with those based on nocturnal data using NLF
algorithm, there are considerable differences below around 90 km in the
lidar results, shown in Fig. 2. For example, near 86 km, the results deduced
from NLF are ∼ 50 % more than those derived from LSF. This
is most likely due to the bias from the diurnal tide, which peaks during
equinox at midlatitudes (Yuan et al., 2010), that could not be completely
removed in the NLF algorithm. The figure also shows that, although NLF
results are still mostly larger than LSF results, the differences between
the two are much less above 90 km. When we apply the two algorithms on WACCM
local outputs, the two sets of WACCM profiles (Fig. 3b and c) show
even larger differences: near 75 km and between 80 and 92 km, the results
from NLF overestimate the GW perturbations by almost or more than 100 %.
The significant differences between 80 and 92 km in WACCM results are,
again, most likely related to the bias caused by diurnal tide, while those
near 75 km could be induced by planetary waves. Indeed, it has been reported
that transient planetary waves could be quite active during autumnal equinox
(Liu et al., 2007). Although not completely removed in the LSF tidal remove
algorithm, the planetary wave effects are decreased considerably in the
24 h fitting window.
To validate the new LSF algorithm and the associated GW results above, we
present the two kinds of profiles derived from WACCM data using the two
completely different algorithms mentioned in the previous Section. Figure 3a
shows the mean temperature T¯ calculated from LSF algorithm at
single location is mostly the same as the mean temperature derived globally
from the ZWR10 approach. As indicated in Fig. 3b, the two T′2 profiles are
also very similar and the differences between the two are mostly within the
fitting uncertainties. Within the mesopause region, both profiles indicate the
temperature variance is less 20 K2 near 80 km and grow quickly and
continuously above, as large as ∼ 50 K2 near 95 km.
However, above ∼ 97 km, the two become quite different: those
deduced from ZW10R are considerably larger (close to 70 K2 at 100 km)
than those calculated from local LSF at the same altitude (∼ 40 K2 at 100 km). One possibility is the presence of GWs with
periods near 6 and 8 h, whose amplitudes grow significantly above
this altitude. These waves would be excluded from the LSF time-fitting
method but will remain in the ZWR10 spatial filtering method. The
associated PED profiles in Fig. 3c behave similarly to their related
temperature variance profiles except that, above ∼ 97 km, the
differences LSF and ZWR10 results are all within the fitting uncertainties
in the two PED profiles. Intriguingly, there is no “node” structure in
both the LSF profiles and ZW10R profiles. Overall, based on the comparison
between the WACCM GW results derived from LSF and ZW10R, both algorithms
generate very similar GW temperature perturbations and the PED. Therefore,
both can be utilized to investigate the GWs with high confidence. For the
ground-based studies without horizontal coverage, the LSF tidal removal
algorithm on full diurnal cycle measurements provides the most reliable GW
results.
Figure 4 illustrates the Lomb spectrum power for the temperature
perturbations during each of the lidar campaigns in September between 2011
and 2015. The lidar temperature perturbations are derived from the
aforementioned LSF tidal removal algorithm. Here, only modulations with
significance level larger than 50 % are shown. The figure shows the
dominance of long-period large-scale GWs' modulations in the mesopause
region with periods between 3 and 5 h during almost all of the six
campaigns, except the campaign during DOY 251 and 253 in 2012. This
indicates that the GW perturbations in the mesopause region are mostly generated
by this part of the GW spectrum during autumnal equinox around midlatitudes. To
confirm this conclusion, we re-analyzed the lidar data with 15 min
resolution and conducted the same LSF algorithm. This extends the highest GW
frequency in this study from the current 0.5 to 2 h-1, covering more high-frequency components in the total GW
spectrum. The Lomb–Scargle results based on these high-resolution data
again demonstrate the consistent dominance by this part of GW spectrum in
midlatitude mesopause region during this part of the year.
Summary and conclusion
By applying a newly developed LSF tidal removal algorithm to the unique full
diurnal cycle Na lidar observations at USU in September between 2011 and
2015, the monthly averaged profiles of temperature variance and the
associated PED induced by the large-scale GWs are derived in the mesopause
region during the boreal autumnal equinox near midlatitudes. The study
covers the GW spectrum from 2 h up to the inertia period, providing the
most comprehensive large-scale GWs results in MLT. It reveals a “node”
structure near the middle of mesopause region in both profiles, decreasing
GW modulations between 84 and 90 km, but a reversed trend above 90 km,
where temperature variance increases from its minimum of less than 20 K2 up to over 90 K2 near 99 km.
This “node” feature indicates a
GW dissipation layer near the middle of the mesopause region that cannot be
resolved or well presented in current general circulation models (GCMs). The
possible mechanism may be related to the GW critical level near 90 km that
prevents some GWs propagating beyond this altitude, while the GWs at other
parts of the spectrum could penetrate or leak through the layer due to their
fast horizontal phase speed. The dominance of long-period GWs in MLT region,
especially those with periods between 3 and 5 h, is evident in the lidar
observations, indicating that these GWs likely contribute the most to the GW
energy and momentum in the MLT region. On the other hand, the results based
solely on nighttime observations and NLF approach are found to overestimate
the GW perturbations by ∼ 50 % near and below 90 km due to
the bias of diurnal tide that cannot be removed completely in this
algorithm. A similar test on the WACCM local results indicates an
overestimate the GW perturbations of close or more than 100 % near the 75 and 80–90 km range, using NLF on nocturnal data alone. Thus, full diurnal
measurements are critical for precise calculation on the GW perturbations.
To validate this new algorithm for de-trending lidar measurements, the
latest high-resolution simulation results from NCAR WACCM are introduced.
Besides applying the same LSF tidal removal approach on the WACCM data at the
USU location, we also apply a spatial zonal wavenumber removal algorithm
to the WACCM data of all longitudes at 41∘ N to remove all
tidal/planetary-scale modulations with zonal wavenumber less than and equal to 10. The
fitting residuals from the two completely different algorithms are treated as
the GW-induced temperature perturbations. The investigation reveals that,
within fitting uncertainties, these two sets of WACCM GW temperature
variance and PED profiles are almost identical in the MLT. This
comparison clearly demonstrates that the new LSF tidal removal approach
generates the most accurate GW results for the ground-based observations.
This study builds a concrete foundation for future investigation on the
climatology of GW perturbations and forcing for other ground-based
observations.
Data availability
The lidar data of this study are available at the CRRL Madrigal database (2017) at http://madrigal.physics.colostate.edu/htdocs/.
The authors declare that they have no conflict of interest.
Acknowledgements
This study was performed as part of a collaborative research program
supported under the Consortium of Resonance and Rayleigh Lidars (CRRL),
National Science Foundation (NSF) grant AGS-1135882. Han-Li Liu's effort is partially
supported by NSF grant AGS-1138784. The National Center for Atmospheric
Research is sponsored by the National Science Foundation.
The topical editor, K. Hosokawa, thanks one anonymous referee for help in evaluating this paper.
References
Booker, J. P. and Bretherton, F. P.: The critical layer for internal gravity
wave in a shear flow, J. Fluid Mech., 27, 513–539, 1967.Bossert, K., Fritts, D. C., Pautet, P.-D., Williams, B. P.,
Taylor, M. J., Kaifler, B., Dörnbrack, A., Reid, I. M.,
Murphy, D. J., Spargo, A. J., and MacKinnon, A. D.: Momentum flux estimates
accompanying multiscale gravity waves over Mount Cook, New Zealand, on 13
July 2014 during the DEEPWAVE campaign, J. Geophys. Res.-Atmos., 120,
9323–9337, 10.1002/2015JD023197, 2015.Cai, X., Yuan, T., Zhao, Y., Pautet, P.-D., Taylor, M. J., and Pendleton Jr., W. R.:
A coordinated investigation of the gravity wave breaking and the
associated dynamical instability by a Na lidar and an Advanced Mesosphere
Temperature Mapper over Logan, UT (41.7∘ N, 111.8∘ W),
J. Geophys. Res.-Space, 119, 6872–6864, 10.1002/2014JA020131, 2014.CRRL Madrigal database: Lidar data, available at:
http://madrigal.physics.colostate.edu/htdocs/, last access: 24 January
2017.Ern, M., Preusse, P., Alexander, M. J., and Warner, C. D.: Absolute values of
gravity wave momentum flux derived from satellite data, J. Geophys. Res.,
109, D20103, 10.1029/2004JD004752, 2004.Fritts, D. C. and Alexander, M. J.: Gravity wave dynamics and effects in the
middle atmosphere, Rev. Geophys., 41, 1003, 10.1029/2001RG000106,
2003.Fritts, D. C., Pautet, P.-D., Bossert, K., Taylor, M. J., Williams, B. P.,
Iimura, H., Yuan, T., Mitchell, N. J., and Stober, G.: Quantifying gravity wave
momentum fluxes with Mesosphere Temperature Mappers and correlative
instrumentation, J. Geophys. Res.-Atmos., 119, 13583–13603,
10.1002/2014JD022150, 2014.Garcia, R. R., Marsh, D. R., Kinnison, D. E., Boville, B. A., and Sassi, F.:
Simulation of secular trends in the middle atmosphere, 1950–2003, J.
Geophys. Res., 112, D09301, 10.1029/2006JD007485,
2007.Gardner, C. S. and Liu, A. Z.: Seasonal variations of the vertical fluxes of
heat and horizontal momentum in the mesopause region at Starfire Optical
Range, New Mexico, J. Geophys. Res., 112, D09113, 10.1029/2005JD006179,
2007.
Krueger, D. A., She, C.-Y., and Yuan, T.: Retrieving mesopause temperature and
line-of-sight wind from full-diurnal-cycle Na lidar observations, Appl.
Opt., 54, 9469–9489, 2015.Liu, H. and Vadas, S. L.: Large-scale ionospheric disturbances due to the
dissipation of convectively-generated gravity waves over Brazil. J. Geophys.
Res.-Space, 118, 2419–2427, 10.1002/jgra.50244, 2013.Liu, H.-L., Li, T., She, C.-Y., Oberheide, J., Wu, Q., Hagan, M. E., Xu, J.,
Roble, R. G., Mlynczak, M. G., and Russell III, J. M.: Comparative study of
short-term diurnal tidal variability, J. Geophys. Res., 112, D18108,
10.1029/2007JD008542, 2007.Liu, H.-L., McInerney, J. M., Santos, S., Lauritzen, P. H., Taylor, M. A., and
Pedatella, N. M.: Gravity waves simulated by high-resolution Whole Atmosphere
Community Climate Model, Geophys. Res. Lett., 41, 9106–9112,
10.1002/2014GL062468, 2014.Lu, X., Liu, A. Z., Swenson, G. R., Li, T., Leblanc, T., and McDermid, I. S.:
Gravity wave propagation and dissipation from the stratosphere to the lower
thermosphere, J. Geophys. Res., 114, D11101, 10.1029/2008JD010112, 2009.
Pautet, P.-D., Talor, M. J., Pendleton Jr., W. R., Zhao, Y., Yuan, T., Esplin,
R., and McLain, D.: Advanced mesospheric temperature mapper for high-latitude
airglow studies, Appl. Optics, 53, 5934–5943, 2014.Rauthe, M., Gerding, M., Höffner, J., and
Lübken, F.-J.: Lidar temperature measurements of gravity waves
over Kühlungsborn (54∘ N) from 1 to 105 km: A
winter-summer comparison, J. Geophys. Res., 111, D24108,
10.1029/2006JD007354, 2006.Rauthe, M., Gerding, M., and Lübken, F.-J.: Seasonal changes in gravity wave
activity measured by lidars at mid-latitudes, Atmos. Chem. Phys., 8,
6775–6787, 10.5194/acp-8-6775-2008, 2008.She, C. Y., Li, T., Collins, R. L., Yuan, T., Williams, B. P., Kawahara, T.,
Vance, J. D., Acott, P., Krueger, D. A., Liu, H. L., and Hagan, M. E.: Tidal
perturbations and variability in mesopause region over Fort Collins, CO
(41∘ N, 105∘ W): Continuous multi-day temperature and wind
lidar observations, Geophys. Res. Lett., 31, L24111,
10.1029/2004GL021165, 2004.
Tsuda, T.: Characteristics of atmosphere gravity waves observed using the MU
(Middle and Upper atmosphere) radar and GPS (Global Positioning System)
radio occultation, P. Jpn. Acad. B-Phys., 90, 12–27, 2014.Vadas, S. L. and Liu, H.-L.: Numerical modeling of the large-scale neutral
and plasma responses to the body forces created by the dissipation of
gravity waves from 6 h of deep convection in Brazil, J. Geophys. Res.-Space, 118, 2593–2617, 10.1002/jgra.50249,
2013.Yuan, T., Schmidt, H., She, C. Y., Krueger, D. A., and Reising, S.: Seasonal
variations of semidiurnal tidal perturbations in mesopause region
temperature and zonal and meridional winds above Fort Collins, Colorado
(40.6∘ N, 105.1∘ W), J. Geophys. Res., 113, D20103,
10.1029/2007JD009687, 2008.Yuan, T., She, C. Y., Forbes, J., Zhang, X., Krueger, D., and Reising, S.: A
collaborative study on temperature diurnal tide in the midlatitude mesopause
region (41∘ N, 105∘ W) with Na lidar and TIMED/SABER
observations, J. Atmos. Sol.-Terr. Phy. 72, 541–549, 2010.Yuan, T., Pautet, P.-D., Zhao, Y., Cai, X., Criddle, N. R., Taylor, M. J., and Pendleton Jr., W. R.:
Coordinated investigation of mid-latitude upper mesospheric
temperature inversion layers and the associated gravity wave forcing by Na
lidar and Advanced Mesospheric Temperature Mapper in Logan, Utah, J.
Geophys. Res.-Atmos., 119, 3756–3769,
10.1002/2013JD020586, 2014.Yuan, T., Heale, C. J., Snively, J. B., Cai, X., Pautet, P.-D., Fish, C.,
Zhao, Y., Taylor, M. J., Pendleton Jr., W. R., Wickwar, V., and Mitchell, N. J.: Evidence of
dispersion and refraction of a spectrally broad gravity wave packet in the
mesopause region observed by the Na lidar and Mesospheric Temperature Mapper
above Logan, Utah, J. Geophys. Res.-Atmos., 121, 579–594, 10.1002/2015JD023687,
2016.