We present a new ground-based system for measurements of
middle-atmospheric carbon monoxide (CO) at Ny-Ålesund, Svalbard, and
the altitude profiles of CO volume mixing ratios (VMRs) measured during the
2017/2018 winter. The Carbon Monoxide Radiometer for Atmospheric
Measurements (CORAM) records spectra from CO spectral emissions in the
middle atmosphere with the aid of a low-noise amplifier designed for the
230 GHz spectral region. Altitude profiles of CO VMRs are retrieved from the
measured spectra using an optimal estimation inversion technique. The
profiles in the current dataset have an average altitude range of 47–87 km,
with special consideration to be given to data at >∼70km altitude. The estimated uncertainty in the CO profile
peaks at ∼12 % of the a priori data used in the inversion. The
CORAM profiles are compared to co-located CO measurements from the Microwave
Limb Sounder (MLS) aboard the Aura satellite and show a difference of 7.4–16.1 %, with a maximum absolute difference of 2.5 ppmv at 86 km altitude.
CO profiles are currently available at 1 h resolution between November 2017
and January 2018. The instrument measures during Arctic winter because
summer time CO concentrations are so low as to be undetectable by CORAM.
Introduction
Millimetre-wave (also referred to as microwave) radiometers are powerful
tools for measuring the composition of the atmosphere. This is particularly
true for areas where there are prolonged night-time periods, such as the
poles. The instruments can measure emissions from molecules in the
atmosphere, in contrast to solar absorption measurements that rely on the
sun. Coherent detection of the atmospheric signal, achieved through
heterodyne receivers, and electronic manipulation of that signal, make it
possible to detect and resolve spectral lines with very low intensities,
especially when the electronics are cooled to low temperatures, thus
producing lower thermal noise (Janssen, 1993). Ground-based measurements in
the thermal IR band generally do not have the capability to distinguish the
mesospheric and stratospheric parts of the carbon monoxide (CO) profile
(Kasai et al., 2005; Velazco et al., 2007).
Altitude profiles of CO concentrations in the middle atmosphere are useful
in quantifying dynamical processes. Because the lifetime of CO during polar
night is on the order of months (Solomon et al., 1985; Allen et al., 1999),
it is a good tracer for atmospheric dynamics. The generally increasing
volume mixing ratio (VMR) of CO with altitude has a strong gradient, which
helps to identify the origin of increases or decreases in concentration.
During polar night, CO concentrations increase in the middle atmosphere due
to the vertical branch of the residual mean circulation bringing CO-rich air
from higher altitudes (Smith et al., 2011; Garcia et al., 2014). Similarly,
a decrease in middle-atmospheric CO in polar spring is linked to a change in
direction of the residual mean circulation at this time. The breakup of the
polar vortex in spring also allows more CO-poor air to be transported
poleward from lower latitudes (Manney et al., 2009, 2015), adding complexity
to the quantitative link between dynamical processes and variations in CO.
Changes in CO (and other tracers) VMRs can be caused by chemical
production and loss (night-time CO is lost through reaction with a layer of
hydroxyl at ∼82km; Solomon et al., 1985; Brinksma et al.,
1998; Damiani et al., 2010; Ryan et al., 2018) and by dynamical processes:
vertical and horizontal advection, eddy transport, and to a lesser extent,
molecular diffusion (Garcia and Solomon, 1983; Andrews et al., 1987;
Brasseur and Solomon, 2005; Smith et al., 2011). While vertical advection
is, in general, the dominating process, modelling studies of
middle-atmospheric CO indicate that the vertical transport rates calculated
from trace gas measurements do not accurately represent the mean
descent or ascent of the atmosphere because the “true” effect of vertical
advection is masked by other processes (Hoffmann, 2012; Ryan et al., 2018).
The general increase in middle-atmospheric CO VMR during polar night is seen
in multiple datasets (e.g. Allen et al., 2000, Forkman et al., 2005; Funke
et al., 2009; Hoffmann et al., 2011; Ryan et al., 2017), and the phenomenon
has been observed for other tracers, e.g. H2O (Lee et al., 2011;
Straub et al., 2012); N2O, CH4, and H2O (Nassar et al.,
2005); and NO, CH4, and H2O (Bailey et al., 2014). The calculated
rates of vertical tracer transport in the above studies range from -1200 to
+450md-1 (negative numbers indicate descent), with the values
representing varying averages in space and/or time. Variations in tracer
VMRs on smaller timescales (minutes to hours) can be caused by waves that
displace air parcels from their equilibrium positions and perturb trace gas
profiles (e.g. Zhu and Holton, 1987; Eckermann et al., 1998; Fritts and
Alexander, 2003; Noguchi et al., 2006; Chane Ming et al., 2016). Data from
ground-based radiometers with high time resolution (order of an hour or
less) have been used to investigate small periodic fluctuations in ozone
(O3) and water vapour (Hocke et al., 2006; Moreira et al., 2018;
Schranz et al., 2018). The positive gradient of polar CO VMRs with altitude
throughout the middle atmosphere, coupled with the time resolution of the
presented measurement system at Ny-Ålesund (≤1h), means that
the dataset discussed here is well-suited to observing these periodic
fluctuations, which are likely to be caused by vertical advection of air
parcels by gravity waves (Zhu and Holton, 1997; Eckermann et al., 1998; Hocke
et al., 2006). As with the ground-based and satellite-borne instruments in
the works cited above, the analyses must be performed within the context of
the limited spatial resolution of the measurements.
The Kiruna Microwave Radiometer, KIMRA, is also currently making
measurements of middle-atmospheric CO at 67.8∘ N (Raffalski et al.,
2005; Hoffmann et al., 2011; Ryan et al., 2017), and the addition of a new
instrument at Ny-Ålesund provides a needed increase in Arctic
coverage and an excellent opportunity for comparison of CO at locations near
the polar vortex edge and inside the vortex, particularly during dynamic
events such as sudden stratospheric warmings. CO profiles from satellite
measurements have been used regularly to study processes in the polar winter
atmosphere (e.g. Damiani et al., 2014; Lee et al., 2011; Manney et al.,
2009; McLandress et al., 2013), but recent ground-based CO datasets in the
polar (and nearby) regions have been sparse: the Onsala Space Observatory
instrument (57∘ N, 12∘ E) (Forkman et al., 2012), which
produced data for 2002–2008 and from 2014; the ground-based
millimetre-wave spectrometer (GBMS) at Thule Air Base (76.5∘ N,
68.7∘ W), used to investigate the Arctic winter of 2001/2002
(Muscari et al., 2007) and the sudden stratospheric warming (SSW) in 2009
(Di Biagio et al., 2010); and the British Antarctic Survey (BAS) radiometer data
at Troll Station (72∘ S, 2.5∘ E), which covers February 2008 to
January 2010 (Straub et al., 2013). These instruments also measure the
rotational transitions of CO and can operate during polar night.
The high time resolution of the CO Radiometer for Atmospheric Measurements
(CORAM) is achieved primarily with a high-frequency low-noise amplifier
(LNA), which operates on the atmospheric CO signal at 230.54 GHz before the
signal is mixed with the radiometer's local oscillator. CORAM is discussed
in Sect. 2, as well as the inversion method, CO profile characteristics,
and error estimates. Section 3 shows the results of a comparison with
co-located data from the Microwave Limb Sounder (MLS). Section 4 shows the
CORAM profile time series and discusses the usage of the data, and Sect. 5
offers some concluding remarks.
Instrument and measured dataCORAM
CORAM is a total-power radiometer housed at Ny-Ålesund, Svalbard
(78.9∘ N, 11.9∘ E), and is part of the joint
French–German Arctic Research Base, AWIPEV. CORAM measures the J=2→1 rotational transition of CO at 230.54 GHz. The instrument was installed
in 2017 and made first measurements of CO in the winter of 2017/2018. During
the summer period, middle-atmospheric concentrations of CO are so small that
they are not detectable by CORAM. The atmospheric signal enters the lab
through a foam window that is transparent to millimetre-wave frequencies,
and meets the pointing mirror of CORAM, angled at 21∘ elevation.
This angle was chosen by performing a series of atmospheric radiative
transfer simulations at different elevation angles, using a climatological
polar winter atmosphere, and determining which angle provided the strongest
CO spectral line. The choice of angle is a trade-off between maximum path length
through the target gas in the atmosphere and minimum attenuation of the
target signal by atmospheric water vapour that is primarily in the
troposphere. The azimuth angle of the atmospheric signal is 113∘,
defined by the laboratory in which CORAM is held. After the pointing mirror,
the atmospheric signal is directed by a series of quasioptical components
through a mylar window in a cryocooler and fed into a corrugated horn
antenna. The quasioptical setup has an antenna pattern with a
half-power beamwidth of ∼5∘. After the horn, the
signal is amplified by a 230 GHz LNA. The unwanted sideband at
∼213.5GHz is suppressed with a waveguide filter before the
signal is mixed with the local oscillator (LO) signal (111 GHz) using a
sub-harmonic mixer. Now at an intermediate frequency of 8.5 GHz, the signal
exits the cooler and is amplified with another LNA before being further
down-converted to 0.5 GHz and analysed by a fast Fourier transform
spectrometer (FFTS).
Schematic of the CORAM receiver and simplified version of the
quasioptics. A rotatable mirror selects a signal from either the atmosphere,
warm target, or cold target. The signal is directed by a parabolic mirror to
a path length modulator that is comprised of a polarising wire grid, an absorber,
and an oscillating rooftop mirror. The signal passes through a window in the
cryocooler, where it is directed to the receiver with an elliptical mirror.
The signal enters the corrugated feed horn and encounters the radio frequency (RF) LNA, a
waveguide filter (BPF), and a sub-harmonic mixer (SHM). At the SHM the
signal is down-converted to an intermediate frequency (IF) of 8.5 GHz. The IF
signal exits the cryocooler and passes through a room temperature LNA. The
RF (atmospheric) signal is mixed at the SHM with a local oscillator (LO),
which is an 18.5 GHz signal from a phase-locked dielectric resonator
oscillator (PDRO) that is passed through a 6x frequency multiplier, to
provide 111 GHz. The IF out signal will be further down-converted to 0.5 GHz
before being analysed by a fast Fourier transform spectrometer (not shown
here). Further details on quasioptical components can be found in Goldsmith
(1998).
Figure 1 shows a schematic drawing of the receiver, including the components
in the cryocooler, as well as a simplified version of the quasioptical
layout. The alignment of the quasioptical components was checked using a
laser positioned at the entrance to the cryocooler. The elevation angle of
the instrument was measured using a self-levelling laser (Bosch GLL 3-80),
which provides a horizontal line with an accuracy of 0.2 mmm-1 (0.2 mrad).
Two horizontal lines, one directly from the laser and one passing through
the quasioptical setup, were aligned on a screen approximately 5 m from the
instrument. A sun scanning method has been used with other ground-based
instruments for alignment and to identify a pointing offset, e.g. for
MIAWARA-C (Straub et al., 2010) and GROMOS-C (Fernandez et al., 2015), for
which the offsets in the elevation angle were found to be 0.01 and 0.07∘, respectively.
The measured atmospheric signal is calibrated using two black body targets at
known temperatures (measured with mounted sensors): a cold target in the
cryocooler at ∼70K and a warm target at ∼293K. The integration time for each black body is the same as that for the
atmospheric signal. A path length modulator is part of the setup that
directs the atmospheric signal to the feed horn, in order to reduce the
amplitude of any standing waves in the quasioptics. The FFTS is an Acqiris
AC240 and has a bandwidth of 1 GHz with 16 384 channels, providing
∼61kHz resolution. A high spectral resolution, depending on
the Doppler width of a spectral line (∼300kHz in this case),
is required for resolving the mesospheric contribution to the spectrum.
CORAM performs the Fourier transform in real time and the fully resolved
spectrum is stored. The cryocooler makes use of a CTI Cryogenics 350 CP
cold head and a CTI Cryogenics 8200 compressor, as well as a helium cooling
machine.
Each electronic component in a signal chain will add noise to the
atmospheric signal of interest, which will also be amplified with any
subsequent amplifiers. Because of the better availability, price, or quality of
amplifiers that operated at several GHz, radiometers used for atmospheric
measurements at frequencies >∼200GHz have
generally employed LNAs after the atmospheric signal has been mixed with the
LO and has been down-converted to a lower frequency. The first LNA in CORAM,
produced by Radiometer Physics GmbH (RPG), operates at a relatively high
frequency of 230 GHz and allows for the atmospheric signal to be amplified
before it encounters the mixer, ultimately providing an increased
signal-to-noise ratio (SNR) for an atmospheric measurement. This
configuration has been used before for similar instruments, e.g. MIAWARA-C
and GROMOS-C, which measure water vapour at 22 GHz, and ozone at 110 GHz,
respectively.
An estimate of the improvement in the receiver temperature (Janssen, 1993)
can be made using a noise temperature cascade analysis. A variation in
Friis' equation (Vowinkel, 1988) for two components in succession is T=T1+T2/G1,
where T1 andT2 are the respective noise temperatures of the first
and second components, G1 is the linear gain of the first component, and
T is the total noise temperature. The noise temperature of the LNA plus the
waveguide filter was measured to be 1350 K at room temperature, and the
linear gain was measured at 158 (corresponding to 22 dB) (Fig. 2b). The
noise temperature of the sub-harmonic mixer is ∼1500K at
room temperature and has a linear gain of ∼0.16
(corresponding to -8dB). Applying Friis' equation with the LNA preceding
the mixer gives a noise temperature of ∼1360K. The same
calculation with the mixer as the first component gives a noise temperature
of ∼9800K. The dominant contribution to the receiver
temperature of CORAM is from the LNA, filter, and mixer. Cooling the components
can considerably reduce their noise temperature. Figure 2b shows the noise
temperature and gain of the LNA + filter, measured at room temperature.
Figure 2c shows the receiver temperature for CORAM measured at the exit of
the cryocooler, with the cryocooler components at a typical temperature of
39 K. At 8.5 GHz, the receiver temperature is below 350 K. Figure 2a shows
the frequency response of the waveguide filter with a suppression of
∼-45dB at 213.5 GHz.
(a) The frequency response of the waveguide filter (BPF in Fig. 1)
used in CORAM to suppress the unwanted sideband signal at 213.5 GHz. (b) The
noise temperature and gain of the RF LNA + BPF (Fig. 1) at room
temperature. (c) The noise temperature for CORAM after down-conversion to
8.5 GHz. This measurement is made after the first IF LNA (Fig. 1) and before
the second down-conversion to 0.5 GHz. The cryocooler components are at 39 K.
The single sideband system temperature for CORAM is ∼600K
(Sect. 2.1).
The system temperature can be described as Tsys=Trec+Ta (Parrish et al., 1988; Janssen, 1993; Campbell, 2002).
The receiver temperature, Trec, considers the contributions from CORAM,
and the antenna temperature, Ta, considers the contributions from the
atmospheric background and signal being measured. The system
temperature is related to the measurement time through the so-called ideal
radiometer equation: σT=Tsys/(Bt)1/2, where σT is the statistical noise on a measured spectrum, B is the frequency
bandwidth of the measurement, and t is the integration time for the
measurement. This relationship determines the measurement time required to
provide a given SNR. The single sideband Tsys for CORAM operating at
Ny-Ålesund is ∼600K. The atmospheric measurements are
all made with the same elevation angle and so the individually recorded
spectra can be averaged together to reduce the SNR. The measurements used
here have been spectrally averaged over approximately 1 h, including time
used to calibrate the signal. Finer time resolutions that still yield
SNRs that are high enough to be useful are possible. Since Tsys, as defined here, contains a
component from the atmospheric background, the SNR of a given measurement
will vary with the atmospheric conditions at the time, with a more opaque
troposphere giving rise to a smaller SNR. An ad hoc indication of “bad”
weather conditions was found to be a measurement with a baseline temperature
>230K and these measurements were discarded.
CO profile retrievalDefining the inversion problem
Schwarzchild's equation describes radiative transfer through a medium in
local thermodynamic equilibrium. In the millimetre-wave region, at a given
frequency, the measured intensity can be expressed in terms of brightness
temperature, Tb, where
Tb=Tb0e-τ(l0)+∫0l0T(l)α(l)e-τ(l)dl,
with l denoting the path through the atmosphere from a point l0 to the
measurement point at l=0. The initial intensity is Tb0, the
optical depth of the atmosphere is described by τ, and the absorption
coefficient is defined as α. More details can be found in Janssen
(1993) and references therein. Tb in Eq. (1), as a function of
frequency, is generally the mathematical description of the calibrated
atmospheric spectrum, the antenna temperature (Ta) from Sect. 2.1. For a
total power radiometer such as CORAM, the calibrated antenna temperature is
found using
Ta=Vatm-VcVh-VcTh-Tc+Tc,
where Th and Tc are the temperatures of the hot and cold calibration
targets (Sect. 2.1) and Vh and Vc are the measured voltages when
observing the hot and cold targets, respectively. Vatm is the measured
voltage when observing the atmosphere.
The desired quantity, the VMR of a trace gas, is contained within the
description of the absorption coefficient, α. Equation (1) must be
inverted to retrieve this information. The form of Eq. (1) is that of a
Fredholm integral of the second kind and is inherently sensitive to small
perturbations (such as noise on a spectrum). To overcome this, the numerical
inversion is performed iteratively here using a maximum a posteriori
probability estimation.
Inversion method
Altitude profiles of CO VMR are retrieved from the measured spectra using an
optimal estimation inversion technique (Rodgers, 2000). The method uses some
a priori information of the state of the atmosphere to constrain the profile
that is retrieved from the measured spectrum. The linear solution to the
inversion problem can be expressed as
x^=Ax+(I-A)xa,
where x^ is the retrieved state vector (VMR profile),
x is the true atmospheric state vector,
xa is the a priori state vector, and
I is the identity matrix. A is the averaging kernel
matrix, which describes the sensitivity of a retrieved state to the true
state (Rodgers, 2000). The sensitivity of the retrieved state at altitude
i to the true state at altitude j is given by
Aij=∂x^i/∂xj.
The inversions are performed with Qpack2 (Eriksson et al.,
2005), which uses the Atmospheric Radiative Transfer Simulator (ARTS 2,
Eriksson et al., 2011) to model the transfer of radiation through the
earth's atmosphere. The a priori CO profile used in the inversion is the
average of one winter (September through April) of output from the Whole
Atmosphere Community Climate Model (WACCM4) (Garcia et al., 2007), provided
by Douglas Kinnison at the National Center for Atmospheric Research (NCAR).
Model output for the grid point encompassing Ny-Ålesund is used. The
output is on a 132-layer pressure grid approximately between ground level and
130 km altitude. A standard deviation of 100 % at all altitudes was found
to provide enough freedom for expected changes in CO VMR to be captured by
the inversion and to give enough regularisation of the solution.
Oscillations in the CO profile, a sign of over-fitting to the measurement
(Rodgers, 2000), were found in several profiles. The oscillations were large
in these cases so the CO profiles were considered unphysical and rejected.
CO emissions are attenuated by absorption due to water vapour in the
atmosphere (mostly in the troposphere), and this is accounted for by
including the water vapour continuum by Rosenkranz (1998) in the forward
model and inversion. O3 is also simultaneously retrieved with CO, as an
O3 spectral line is centred at 231.28 GHz. The molecular oxygen
(O2) and nitrogen (N2) continua (Rosenkranz, 1993), as well as
nitric acid (HNO3) spectral lines, are included in the inversion but
are not retrieved and are considered model parameters. The spectroscopic
line data used here are from the high-resolution transmission molecular
absorption database (HITRAN) 2008 catalogue (Rothmann et al., 2009). The a
priori information for O2, O3, and water vapour is from the
same WACCM4 run as for CO, and the information for HNO3 and N2 are
from the FASCOD (Fast Atmospheric Signature Code) subarctic winter scenario
(Anderson et al., 1986).
The information for the altitude, pressure, and temperature in an inversion
is constructed from European Centre for Medium-Range Weather Forecasting
(ECMWF) profiles and from the NRLMSISE-00 empirical model of the atmosphere
(MSIS from herein) (Picone et al., 2002). ECMWF information is available
daily at 6 h intervals, beginning at midnight, and covers up to 0.01 hPa
altitude, and above that the temperature profile information is from MSIS.
The temperature data are smoothed around the point where the profiles are
merged to avoid discontinuities.
An estimate of the measurement noise on a spectrum is made by fitting a
second-order polynomial to a wing of the spectrum and calculating the
standard deviation of the fit. Qpack2 provides the capability to fit a
series of functions to the baseline of the measured spectra (a baseline fit)
to account for errors in the baseline which are likely caused by standing
waves in the instrument. The baseline fit is included in the optimal
estimation and forms part of the overall fit to the measurement (inversion
fit). All of the CORAM measured spectra were first inverted without a fit to
the baseline, and a periodogram of the residuals was evaluated to determine
the periods of sinusoidal signatures in the baseline. Three primary
sinusoids were found with respective estimated periods of 125, 62.5, and
41.67 MHz, and amplitudes of 0.2, 0.1, and 0.02 K. The periods of the
sine waves are large compared with the width of the CO spectral line, which
has a typical full width at half maximum (FWHM) of ∼0.7MHz,
and so are uniquely distinguishable from it. The broad wings of a CO
spectral line are produced by CO molecules at altitudes below the
retrievable altitude limit of CORAM (approximately 47 km; see Sect. 2.3). A
first-order polynomial is also included in the baseline fit to account for
offsets. The zeroth- and first-order coefficients have estimated
uncertainties of 1 and 0.5 K, respectively.
The altitude grid for the forward model is between the ground and 125 km,
with approximately equally spaced points. The retrieval grid is between
approximately 2 and 124 km, and is a 62-layer subset of the forward model
grid. CO VMRs are retrieved as a fraction of their a priori levels for numerical
stability due to the strong gradients in atmospheric CO. The inversion
method is nonlinear and uses a Marquardt–Levenberg iterative minimisation
scheme (Marquardt, 1963).
CO profile characteristics
The CORAM CO data spans from 18 November 2017 to 18 January 2018.
The instrument required maintenance after the latter date and was not in
full operation for the remainder of the winter, unfortunately missing the
SSW in February. Nonetheless, the data shown here consist of 875
atmospheric profiles in that time with a time resolution of ∼1h.
(a) Upper: an example spectrum measured by CORAM on 24 December 2017 between 20:04 and 21:03 UTC. The inversion fit to the measurement is
shown (red line). Lower: the residual of the measurement and the
inversion fit (solid black line). The dashed red line shows the baseline fit
for the inversion, which is part of the inversion fit shown in the upper
panel (Sect. 2.2.2). (b) The CO profile retrieved from the measurement
(solid blue line) and the a priori profile that is used as input to the inversion
(dashed black line).
The mean averaging kernels for the CORAM inversions. The
measurement response (sum of the rows of the averaging kernels) divided by 4
is shown by the thick solid blue line. The dashed black line and the dotted black
line indicate a measurement response of 1.0 and 0.8, respectively. (b) The
mean altitude resolution of the CORAM CO profiles, calculated from the FWHM
of the averaging kernels.
Figure 3 shows an example spectrum measured by CORAM on 24 December 2017 and the matching inversion fit and residual. The retrieved CO profile
is also plotted in Fig. 3 alongside the a priori profile. The mean of the
averaging kernels for the whole CO dataset are shown in Fig. 4 alongside
the average of the estimated altitude resolution of the CO profiles. The
estimated altitude resolution of the profiles is calculated here as the FWHM
of the averaging kernels. A common way to estimate the altitude limits of a
retrieved profile is to define the sum of the rows of the averaging kernels
as the measurement response and assign a cut-off value. The measurement
response can generally be thought of as a rough measure of the fraction of
the retrieved state that comes from the data, rather than the a priori state
(Rodgers, 2000). As noted by Payne et al. (2009), this is only a rough
measure, and the measurement response often exceeds 1 at some altitudes. The
choice of the cut-off value is rather arbitrary but 0.8 is regularly used
(e.g. Forkman et al., 2012; Straub et al., 2013; Schranz et al., 2018) and
is also used here. With the above definitions, the CO profiles from CORAM
during winter 2017/2018 have an average altitude range of approximately 47–87 km, with an average altitude resolution varying between approximately
12.5 and 28 km over that range. The retrieval range can change depending on
the distribution of CO in the atmosphere (the lower limit can decrease in
altitude when there are higher CO values at lower altitudes) and the value
provided here is the mean range over the time span of the data.
The retrieval limits will vary from measurement to measurement and
individual profiles should be considered in combination with the
accompanying averaging kernels (see Fig. 4). The centres of the averaging
kernels, when represented in VMR, are shifted down in altitude compared to a
representation in relative units (Hoffmann et al., 2011). The lower limit of
the retrieval here is defined by the SNR in the measurement and the upper
limit is set by a transition from a pressure-broadening regime to a doppler-broadening one. The result of this change is that above approximately 70 km
in the VMR representation the centres of the averaging kernels do not
increase in altitude with their corresponding retrieval altitudes. The
retrieved CO values above ∼70km altitude do contain
information from the atmosphere that corresponds with the retrieval
altitude, but the VMR representation of the profile should be considered
with care. Hoffmann et al. (2011) provides a detailed discussion on the
representation of data for ground-based CO measurements. Hoffmann emphasises
that the limited vertical resolution of the data must be taken into account
for the use and interpretation of the data by considering each realisation
of the averaging kernels, and thus the a priori and averaging kernels form an
essential part of the dataset.
CO profile error estimates
The error contributions to the CO profiles are calculated using optimal estimation method (OEM) error
definitions, which are defined in detail in Rodgers (2000). The estimates of
the errors are found by perturbing the inputs to the inversion, using the
following uncertainties. Error in the temperature profile is the same as
that used in Hoffmann et al. (2011): 10 % above 100 km, 5 % below 80 km, and
linearly interpolated in between. An uncertainty of 1∘ is chosen
for the pointing of the instrument to the sky, an overestimate of the motor
(Faulhaber 3564K024B CS) uncertainty by an order of magnitude, to account
for changes that may occur in the orientation of the instrument table. The
uncertainty in the warm and cold calibration targets is 2 K, an overestimate
that accounts for variations and drifts in the temperatures. The HITRAN 2008
catalogue is used for uncertainties in the CO line parameters: 1 % for the
line intensity, 2 % for the air broadening parameter, and 5 % for the
temperature dependence of the air broadening. The uncertainties related to
self-broadening of CO are not considered due to the relatively low
concentration of the gas (Ryan and Walker, 2015). The uncertainty in the
line position is ignored because the frequency grid used in the inversion is
shifted to centre a measurement.
The estimated error contributions to the CORAM CO profiles. The
spectrum noise is calculated as an average of the noise on all CORAM
measurements, and the other estimates are calculated through perturbations
about the a priori CO profile (Sect. 2.4).
The error estimates, including the average of the error arising from
statistical noise on the spectrum, are plotted in Fig. 5. The sum in
quadrature of the error estimates is also plotted, as well as the a priori
CO profile for the dataset. The statistical noise on the spectrum and the
uncertainty in the temperature profile are the biggest contributors to the
total error profile, with the temperature error surpassing that of the
spectrum noise at ∼84km, near the average upper retrieval
altitude limit. As a fraction of the a priori profile, the total error
estimate has a maximum of ∼12 % at ∼48km,
near the average lower retrieval altitude limit, and there is also a peak of
11.5 % near 70 km altitude. The uncertainty in the temperature profile
begins to become more pronounced above 50 km altitude.
Comparison with Aura MLS
MLS is a radiometer aboard the Aura satellite. A description of the
instrument is given in Waters et al. (2006). Version 4.2 of the MLS CO data
(Schwartz et al., 2015) is used here and is described in Livesey et al. (2018). The atmospheric pressure range of the data is 215–0.0046 hPa. The
precision of the CO VMR profile reaches a maximum (largest) value of
1.1 ppmv at the upper limit of the MLS CO retrieval altitude. The data have
an estimated average positive bias (larger VMRs) of ∼17 %
above 40 km altitude (Sheese et al., 2017) compared to the Atmospheric
Chemistry Experiment – Fourier Transform Spectrometer (ACE-FTS) satellite
instrument. Sheese et al. (2017) use versions 3.3 and 3.4 MLS CO data, which
show good agreement with version 4.2 (Livesey et al., 2018), and have not
included data from the summer months when CO concentrations are very low.
Co-located measurement comparison
MLS measurements are subset to within ±2∘ latitude and
±10∘ longitude of CORAM, calculated at 60 km altitude along
the line of sight of CORAM (∼156km horizontally from the
lab). The CO VMRs are expected to vary more in latitude than in longitude
because the atmospheric composition generally varies more in the meridional
direction compared to the zonal. A longitude space of ±5∘ was tested, but there were not significant changes to the results shown here
and the number of coincident MLS measurements were halved. Above 0.001 hPa,
MLS CO profiles have a constant VMR value. Because CORAM has some
sensitivity to CO at these altitudes, the MLS profiles were instead linearly
extrapolated in pressure space above 0.001 hPa. A more physically realistic
profile shape is produced, an example of which can be seen in Fig. 4 of Ryan
et al. (2017). To reduce the effect of atmospheric variability between
individual measurement locations, the CORAM and MLS profiles are averaged by
day to produce daily mean profiles. These MLS profiles were smoothed
(Rodgers, 2000) with the averaging kernels of the corresponding CORAM
profiles to account for the finer altitude resolution of MLS CO profiles:
6–7 km in the upper mesosphere and 3.5 to 5 km in the upper troposphere to
the lower mesosphere (Livesey et al., 2018).
(a) The mean of the daily CORAM and MLS CO profiles above
Ny-Ålesund. The mean of the unsmoothed MLS profiles is also shown, as
well as the a priori profile used for the CORAM inversions. (b) The
absolute difference of the mean CORAM and smoothed MLS profiles, with the
standard deviation of the differences as the whiskers on the line. (c) The
same as for (b) but with the difference as a percentage of the mean CORAM
and MLS profiles. (d) The correlation coefficients of the CORAM and smoothed
MLS data (solid line) or unsmoothed MLS data (dashed line).
Figure 6 shows the mean CO profiles for CORAM and MLS over the time of
measurement overlap (19 November to 18 January), as well as
the absolute and percentage (relative to the mean of the MLS and CORAM
profiles) differences in the profiles. The correlation of the CO VMRs at
each retrieval altitude is also plotted. The maximum absolute difference in
the mean profiles is 2.5 ppmv at 86 km altitude, corresponding to an
11.3 % difference. The percentage difference varies between
∼7.4 % at the lowest retrieval altitude and 16.1 % at
72 km, with MLS having a low bias in comparison to CORAM over the entire
altitude range. This contrasts with the estimated high bias of MLS compared
to ACE-FTS mentioned above. The standard deviation of the differences in
the profiles is largest (in percentage) at 58 km with a value of 14.4 %.
The correlation of the CORAM and smoothed MLS CO profiles is greater than
0.80 at all retrieval altitudes, reaching a maximum of 0.92 at 47 km. After
smoothing, the MLS and CORAM data are not truly independent, so the
correlation of CORAM with the unsmoothed MLS data is also calculated and
shows more variation over the retrievable altitude range, with a minimum of
0.59 and a maximum of 0.81. The statistics here show some similarities to
the comparison of MLS CO and ground-based CO measurements from KIMRA
(67.8∘ N), where MLS showed a low bias (peaking at
∼0.65 ppmv) up to ∼74km, with a maximum
relative bias of 22 % at 60 km (Ryan et al., 2017). The correlation
between KIMRA and MLS was slightly higher than that for CORAM and MLS,
remaining greater than 0.90 up to 82 km altitude.
Time series of the daily CORAM and MLS CO VMR values at altitudes
of 48, 58, 68, 78, and 88 km.
Figure 7 shows the daily time series of the MLS and CORAM profiles at 48,
58, 68, 78, and 88 km. The largest differences in CO are found at higher
altitudes (≥68km) in November and the first days of December, after
which the values become closer in VMR, indicating better agreement between
the instruments. The reason for the larger difference over this time is
unknown, but it is clear that these high values contribute to the bias
between the instruments shown in Fig. 6. Despite the absolute differences, a
similar variability in CO is captured by both instruments over the whole
time series.
CORAM CO profiles at 1 h resolution from mid-November 2017 to
mid-January 2018. Blank areas are gaps in the data record. The zoomed-in
plot shows measurements over a 43 h period beginning at 18:00 UTC on 31 December 2017. The isoluminant colour map from Kindlmann et al. (2002) is
used. Contour values are [0.4, 2.3, 4.1, 6.0, 10.4, 14.8, 19.2, 23.6, 28.0],
chosen and filled for readability. Gaps in the data record correspond to
periods of non-operation or bad measurement data.
CORAM data and usage
Figure 8 shows the currently available CORAM CO data for winter 2017/2018 at
1 h time resolution. The anomalously high values above ∼70km altitude are visible in November and first days of December. At lower
altitudes over this time, there is still some downwelling of CO due to the
residual mean circulation before a levelling off in mid-December. Figure 8
also shows a 43 h segment of the data, beginning at 17:00 UTC on 31 December 2017, to illustrate the advantage of continuous measurements.
Below ∼75km altitude, there is apparent downwelling of CO
for about the first 25 h, peaking before VMR values start to decrease
over the next 18 h. There are two relatively strong increases in
lower-altitude CO at approximately 14:00 UTC, 1 January, and 01:00 UTC,
2 January, evident from the 2.3 ppmv contour line moving down from 60 to
50 km altitude. Over this same time, between 60 and 70 km, there is an
oscillation in the 4.1 and 6 ppmv contour lines, with peaks occurring every
1–2 h. The VMR values above approximately 75 km tend to show similar
short timescale variations but with opposite sign, i.e. a peak at a higher
altitude corresponds with a trough at a lower altitude. This inverted
pattern is observable over the whole 43 h time period. Variations on
these timescales cannot be directly observed by non-geostationary
satellites, illustrating the unique capability of ground-based instruments.
These are broad descriptions of the data because one cannot fully
characterise the variations in CO without the use of other data sources and
model output. Variations on timescales of an hour to weeks are visible
in the data and require detailed study to elucidate the underlying dynamical
processes, such as the polar vortex shift, Rossby wave activity, SSW events, and
gravity wave perturbations (timescales of minutes to hours). Periodicities
in trace gas data have previously been analysed using spectral decomposition
techniques on ground-based measurements of water vapour and ozone (e.g.
Hocke et al., 2006; Struder et al., 2014; Schranz et al., 2019) to identify
waves with periods of days to weeks.
As mentioned in Sect. 2.3, the CORAM profiles should be used with
consideration of the accompanying averaging kernels. Ground-based
measurements have limited altitude resolution, often much coarser than the
altitude grids onto which the data is retrieved. The representation of the
data on a fine grid adds stability to the inversion (Eriksson, 1999) and can
give rise to substantial smoothing error in the profiles (Rodgers and Connor, 2003).
The smoothing error can be accounted for when comparing CORAM to instruments
with higher resolution by convolving the data from the other instrument with
the CORAM averaging kernels, as was done for MLS in Sect. 3. The error
should be assessed if one is to use the CO profiles without considering the
sensitivity distribution described by the averaging kernels. This is not a
recommended use of the data and is why the smoothing error is not assessed in
Sect. 2.4. In other words, if one is to say something of a CORAM CO VMR at a
given grid point, one must be aware that the VMR value at that grid point
contains information from a range of altitudes, with a sensitivity governed
by the associated averaging kernel.
CORAM profiles can be used independently to describe changes in CO over
time, providing the averaging kernels do not significantly change over this
time, which would change the measurement response. The measurement response
for CORAM should not show significant variation inside the retrievable
altitude range, but care should be taken at altitudes near the edges of the
retrieval range of the profiles, where the measurement response has a strong
gradient and can change quickly when there are rapid changes in CO
concentrations at those altitudes. CORAM is currently under maintenance due
to a fault in the LO signal generator and is expected to be back in
operation for the winter of 2019/2020 and beyond.
Conclusion and future work
This work presents a new ground-based radiometer, CORAM, which has been
installed at the high Arctic location of Ny-Ålesund, 78.9∘ N, for the measurement of middle-atmospheric CO. The instrument makes use
of a high-frequency LNA, before the down-conversion of the atmospheric
signal, to achieve high SNRs at time resolutions on the order of an hour or
less. CO profiles were retrieved from measurements in the Arctic winter of
2017/2018. Error estimates show that the uncertainty in the temperature
input for the inversions and the statistical noise on the spectrum are the
largest contributions to the error budget, giving a maximum in the error
profile of ∼12 % of the a priori profile. The mean of the
averaging kernel matrix for the CORAM dataset gives an average retrieval
altitude range of 47–87 km, with an average altitude resolution of 12.5 to
28 km over this range. Data at higher altitudes should be treated with care
as the VMR representation of the averaging kernels do not peak at the
corresponding retrieval grid points above ∼70km altitude. A
comparison with MLS shows a negative bias (MLS – CORAM) at all altitudes,
with a maximum of 16.1 % of the average profiles occurring at 72 km
altitude. A comparison of the instruments' time series indicate abnormally
high CO measured by CORAM above ∼68km in November 2017 that
contributes to the observed bias, after which the MLS and CORAM values show
improved agreement. Correlations between the instruments range from 0.80 to
0.92 over CORAMs retrievable altitude range for MLS data smoothed with the
CORAM averaging kernels, and from 0.59 to 0.81 when using the unsmoothed MLS
data. CO profiles above Ny-Ålesund with a 1 h time resolution
between 19 November 2017 and 18 January 2018 are currently
available. Future work with CORAM will include the following elements: integration of a newly
manufactured local oscillator, due to a failure of the original, and
investigation of possible attenuation of the atmospheric signal by the
laboratory foam window.
Data availability
CORAM level 2 data, including averaging kernels and metadata, are available
on request via Niall J. Ryan (n_ryan@iup.physik.uni-bremen.de) and
Mathias Palm (mathias@iup.physik.uni-bremen.de). A public data archive is planned
for after CORAM resumes operation in the winter of 2019/2020. The Aura MLS
version 4.2 data are available from the Goddard Earth Sciences Data and Information
Center at https://disc.gsfc.nasa.gov (last access: 10 August 2018).
Author contributions
MP and CGH designed the project. CGH, NJR, and JG designed and built CORAM. NJR developed the inversion setups
for CORAM and performed the comparisons. NJR installed CORAM at
Ny-Ålesund, and the instrument was maintained by MP. JN
provided valuable feedback on the project. NJR prepared the manuscript
with contributions from all co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This work has been funded by the German Federal Ministry of Education and
Research (BMBF) through the research project “Role Of the Middle atmosphere
in Climate (ROMIC)”, and the sub-project ROMICCO, as well
as by a grant from the Canadian Space Agency. We would like to express our
gratitude to the MLS teams for making their CO product available. We would
also like to thank the ECMWF and MSIS teams for making their products
available, as well as the Qpack and ARTS communities for making their
software available. We thank the AWIPEV staff for all of the help provided
at Ny-Ålesund, and give thanks in particular to Benoit Laurent, who aided in the
installation and maintenance of CORAM at Ny-Ålesund. A special thank
you to Eloise and Elmo Ryan for all of their support.
Financial support
This research has been supported by the Bundesministerium für Bildung und Forschung (grant no. 01LG1214A).The article processing charges for this open-access publication were covered by the University of Bremen.
Review statement
This paper was edited by Andreas Hofzumahaus and reviewed by two anonymous referees.
ReferencesAllen, D. R., Stanford, J. L., López-Valverde, M. A., Nakamura, N.,
Lary, D. J., Douglass, A. R., Cerniglia, M. C., Remedios, J. J., and Taylor,
F. W.: Observations of Middle Atmosphere CO from the UARS ISAMS during the
Early Northern Winter 1991/92, J. Atmos. Sci., 56, 563–583,
10.1175/1520-0469(1999)056<0563:OOMACF>2.0.CO;2, 1999.
Allen, D. R., Stanford, J. L., Nakamura, N., López-Valverde, M. A.,
López-Puertas, M., Taylor, F. W., and Remedios, J. J.: Antarctic polar
descent and planetary wave activity observed in ISAMS CO from April to July
1992, Geophys. Res. Lett., 27, 665–668, 2000.
Anderson, G. P., Clough, S. A., Kneizys, F. X., Chetwynd, J. H., and
Shettle, E. P.: AFGL atmospheric constituent profiles (0–120 km), Tech.
Rep. TR-86-0110, AFGL, 1986.
Andrews, D., Holton, J., and Leovy, C.: Middle Atmosphere Dynamics, Academic
Press, San Diego, 489 pp., 1987.Bailey, S. M., Thurairajah, B., Randall, C. E., Holt, L., Siskind, D. E.,
Harvey, V. L., Venkataramani, K., Hervig, M. E., Rong, P., and Russell III,
J. M.: A multi tracer analysis of thermo- sphere to stratosphere descent
triggered by the 2013 Stratospheric Sudden Warming, Geophys. Res. Lett., 41,
5216–5222, 10.1002/2014GL059860, 2014.
Brasseur, G. and Solomon, S.: Aeronomy of the Middle Atmosphere: Chemistry
and Physics of the Stratosphere and Mesosphere, Springer, Dordrecht, 644 pp., 2005.
Brinksma, E. J., Meijer, Y. J., McDermid, I. S., Cageao, R. P., Bergwerff,
J. B., Swart, D. P. J., Ubachs, W., Matthews, W. A., Hogervorst, W., and
Hovenier, J. W.: First lidar observations of mesospheric hydroxyl, Geophys.
Res. Lett., 25, 51–54, 1998.
Campbell, D. B.: Measurement in Radio Astronomy, in: Single-Dish Radio Astronomy: Techniques and Applications, ASP Conference Series, Vol. 278, edited by: Stanimirovic, S., Altschuler, D., Goldsmith, P., and Salter, C., Astronomical Society of the Pacific, San Francisco, California, USA, 81–90, 2002.Chane Ming, F., Vignelles, D., Jegou, F., Berthet, G., Renard, J.-B., Gheusi, F., and Kuleshov, Y.: Gravity-wave effects on tracer gases and stratospheric aerosol concentrations during the 2013 ChArMEx campaign, Atmos. Chem. Phys., 16, 8023–8042, 10.5194/acp-16-8023-2016, 2016.Damiani, A., Storini, M., Santee, M. L., and Wang, S.: Variability of the nighttime OH layer and mesospheric ozone at high latitudes during northern winter: influence of meteorology, Atmos. Chem. Phys., 10, 10291–10303, 10.5194/acp-10-10291-2010, 2010.Damiani, A., Funke, B., Puertas, M. L., Gardini, A., Santee, M. L.,
Froideveaux, L., and Cordero, R. R.: Changes in the composition of the
northern polar upper stratosphere in February 2009 after a sudden
stratospheric warming, J. Geophys. Res.-Atmos., 119, 11429–11444,
10.1002/2014JD021698, 2014.Di Biagio, C., Muscari, G., di Sarra, A., de Zafra, R. L., Eriksen, P.,
Fiocco, G., Fiorucci, I., and Fuà, D.: Evolution of temperature, O3, CO,
and N2O profiles during the exceptional 2009 Arctic major stratospheric
warming observed by lidar and mm-wave spectroscopy at Thule (76.5 N, 68.8 W), Greenland, J. Geophys. Res., 115, D24315, 10.1029/2010JD014070,
2010.
Eckermann, S. D., Gibson-Wilde, D. E., and Bacmeister, J. T.: Gravity Wave
Perturbations of Minor Constituents: A Parcel Advection Methodology, J.
Atmos. Sci., 55, 3521–3539, 1998.
Eriksson, P.: Microwave radiometric observations of the middle atmosphere:
simulations and inversions, PhD thesis, Chalmers University of Technology, Gothenburg,
1999.
Eriksson, P., Jimenez, C., and Buehler, S. A.: Qpack, a general tool for
instrument simulation and retrieval work, J. Quant. Spectrosc. Ra., 91, 47–64, 2005.
Eriksson, P., Buehler, S. A., Davis, C. P., Emde, C., and Lemke, O.: ARTS,
the atmospheric radiative transfer simulator, Version 2, J. Quant. Spectrosc. Ra., 112,
1551–1558, 2011.Fernandez, S., Murk, A., and Kämpfer, N.: GROMOS-C, a novel ground-based microwave radiometer for ozone measurement campaigns, Atmos. Meas. Tech., 8, 2649–2662, 10.5194/amt-8-2649-2015, 2015.Forkman, P., Eriksson, P., and Murtagh, D.: Observing the vertical branch of
the mesospheric circulation at lat N60∘ using ground based
measurements of CO and H2O, J. Geophys. Res., 110, 107,
10.1029/2004JD004916, 2005.Forkman, P., Christensen, O. M., Eriksson, P., Urban, J., and Funke, B.: Six years of mesospheric CO estimated from ground-based frequency-switched microwave radiometry at 57∘ N compared with satellite instruments, Atmos. Meas. Tech., 5, 2827–2841, 10.5194/amt-5-2827-2012, 2012.Fritts, D. C. and Alexander, M., J.: Gravity wave dynamics and effects in
the middle atmosphere, Rev. Geophys., 41, 1003, 10.1029/2001RG000106,
2003.Funke, B., López-Puertas, M., García-Comas, M., Stiller, G. P., von Clarmann, T., Höpfner, M., Glatthor, N., Grabowski, U., Kellmann, S., and Linden, A.: Carbon monoxide distributions from the upper troposphere to the mesosphere inferred from 4.7 µm non-local thermal equilibrium emissions measured by MIPAS on Envisat, Atmos. Chem. Phys., 9, 2387–2411, 10.5194/acp-9-2387-2009, 2009.Garcia, R. R. and Solomon, S.: A Numerical Model of the Zonally Averaged
Dynamical and Chemical Structure of the Middle Atmosphere, J. Geophys. Res.,
88, 1379–1400, 10.1029/JC088iC02p01379, 1983.Garcia, R., Marsh, D., Kinnison, D., Boville, B., and Sassi, F.: Simulation
of secular trends in the middle atmosphere, 1950–2003, J. Geophys. Res.,
112, D09301, 10.1029/2006JD007485, 2007.Garcia, R. R., López-Puertas, M., Funke, D., Marsh, D. R., Kinnison, D. E.,
Smith, A. K., and González-Galindo, F.: On the distribution of CO2 and CO
in the mesosphere and lower thermosphere, J. Geophys. Res., 119, 5700–5718,
10.1002/2013JD021208, 2014.
Goldsmith, P. F.: Quasioptical Systems: Gaussian Beam Quasioptical
Propagation and Applications, IEEE, New York, 1998.Hocke, K., Kämpfer, N., Feist, D. G., Calisesi, Y., Jiang, J. H., and
Chabrillat, S.: Temporal variance of lower mesospheric ozone over
Switzerland during winter 2000/2001, Geophys. Res. Lett., 33, L09801,
10.1029/2005GL025496, 2006.
Hoffmann, C. G.: Application of CO as a tracer for dynamics in the polar
winter middle atmosphere, PhD thesis, Institut für Umweltphysik,
Universität Bremen, Germany, 142 pp., 2012.Hoffmann, C. G., Raffalski, U., Palm, M., Funke, B., Golchert, S. H. W., Hochschild, G., and Notholt, J.: Observation of strato-mesospheric CO above Kiruna with ground-based microwave radiometry – retrieval and satellite comparison, Atmos. Meas. Tech., 4, 2389–2408, 10.5194/amt-4-2389-2011, 2011.
Janssen, M. A.: Atmospheric remote sensing by microwave radiometry, Wiley, New
York, 37–90, 1993.
Kasai, Y. J., Koshiro, T., Endo, M., Jones, N. B., and Murayama, Y.:
Ground-based measurement of strato-mesospheric CO by a FTIR spectrometer
over Poker Flat, Alaska, Adv. Space Res., 35, 2024–2030, 2005.Kindlmann, G., Reinhard, E., and Creem, S.: Face-based lightness Matching
for Perceptual Colormap Generation, IEEE Proceedings of the conference on
Visualization 2002, available at: http://www.cs.utah.edu/~gk/papers/vis02/FaceLumin.pdf (last access: 8 July 2017), 2002.Lee, J. N., Wu, D. L., Manney, G. L., Schwartz, M. J., Lambert, A., Livesey,
N. J., Minschwaner, K. R., Pumphrey, H. C., and Read, W. G.: Aura Microwave
Limb Sounder observations of the polar middle atmosphere: Dynamics and
transport of CO and H2O, J. Geophys. Res., 116, D05110,
10.1029/2010JD014608, 2011.
Livesey, N. J., Read, W. G., Wagner, P. A., Froidevaux, L., Lambert, A.,
Manney, G. L., Millán Valle, L. F., Pumphrey, H. C., Santee, M. L.,
Schwartz, M. J., Wang, S., Fuller, R. A., Jarnot, R. F., Knosp, B. W., and
Martinez, E.: Version 4.2x Level 2 data quality and description document,
Tech. rep., Jet Propulsion Laboratory, Pasadena, 2018.Manney, G. L., Schwartz, M. J., Krüger, K., Santee, M. L., Pawson, S.,
Lee, J. N., Daffer, W. H., Fuller, R. A., and Livesey, N. J.: Aura Microwave
Limb Sounder observations of dynamics and transport during the
record-breaking 2009 Arctic stratospheric major warming, Geophys. Res.
Lett., 36, L12815, 10.1029/2009GL038586, 2009.Manney, G. L., Lawrence, Z. D., Santee, M. L., Read, W. G., Livesey, N. J.,
Lambert, A., Froidevaux, L., Pumphrey, H. C., and Schwartz, M. J.: A minor
sudden stratospheric warming with a major impact: Transport and polar
processing in the 2014/2015 Arctic winter, Geophys. Res. Lett., 42,
7808–7816, 10.1002/2015GL065864, 2015.
Marquardt, D.: An algorithm for least squares estimation on nonlinear
parameters, J. Soc. Ind. Appl. Math., 11, 431–441, 1963.McLandress, C., Scinocca, J. F., Shepherd, T. G., Reader, M. C., and Manney,
G. L.: Dynamical Control of the Mesosphere by Orographic and Nonorographic
Gravity Wave Drag during the Extended Northern Winters of 2006 and 2009, J.
Atmos. Sci., 70, 2152–2169, 10.1175/JAS-D-12-0297.1, 2013.
Moreira, L., Hocke, K., and Kämpfer, N.: Short-term stratospheric ozone
fluctuations observed by GROMOS microwave radiometer at Bern, Earth Planets
Space, 70, 1–10, 2018.Muscari, G., di Sarra, A. G., de Zafra, R. L., Lucci, F., Baordo, F.,
Angelini, F., and Fiocco, G.: Middle atmospheric O3, CO, N2O, HNO3, and
temperature profiles during the warm Arctic winter 2001–2002, J. Geophys.
Res., 112, D14304, 10.1029/2006JD007849, 2007.Nassar, R., Bernath, P. F., Boone, C. D., Manney, G. L., McLeod, S. D.,
Rinsland, C. P., Skelton, R., and Walker, K. A.: ACE-FTS measurements across
the edge of the winter 2004 Arctic vortex, Geophys. Res. Lett., 32, L15S05,
10.1029/2005GL022671, 2005.Noguchi, K., Imamura, T., Oyama, K.-I., and Bodeker, G. E.: A global
statistical study on the origin of small-scale ozone vertical structures in
the lower stratosphere, J. Geophys. Res., 111, D23105,
10.1029/2006JD007232, 2006.
Parrish, A., de Zafra, R. L., Solomon, P. M., and Barrett, J. W.: A
ground-based technique for millimeter wave spectroscopic observations of
stratospheric trace constituents, Radio Sci., 23, 106–118, 1988.Payne, V. H., Clough, S. A., Shephard, M. W., Nassar, R., and Logan, J. A.:
Information-centered representation of retrievals with limited degrees of
freedom for signal: Application to methane from the Tropospheric Emission
Spectrometer, J. Geophys. Res., 114, D10307, 10.1029/2008JD010155, 2009.Picone, J. M., Hedin, A. E., Drob, D. P., and Aikin, A. C.: NRL-MSISE-00
Empirical Model of the Atmosphere: Statistical Comparisons and Scientific
Issues, J. Geophys. Res., 107, 1468, 10.1029/2002JA009430, 2002.Raffalski, U., Hochschild, G., Kopp, G., and Urban, J.: Evolution of stratospheric ozone during winter 2002/2003 as observed by a ground-based millimetre wave radiometer at Kiruna, Sweden, Atmos. Chem. Phys., 5, 1399–1407, 10.5194/acp-5-1399-2005, 2005.
Rodgers, C. D.: Inverse methods for atmospheric remote sounding: Theory and
practice. Vol 2. Series on atmospheric and ocean physics, World
Scientific, Singapore, 2000.Rodgers, C. D. and Connor, B. J.: Intercomparison of remote sounding
instruments, J. Geophys. Res., 108, 4116, 10.1029/2002JD002299, 2003.
Rosenkranz, P. W.: Absorption of microwaves by atmospheric gases, in: Atmospheric remote sensing by microwave radiometry, edited by: Janssen,
M. A.:, Wiley, New York,
37–90, 1993.
Rosenkranz, P. W.: Water vapor microwave continuum absorption: A comparison of
measurements and models, Radio Sci., 33, 919–928, 1998.
Rothman, L. S., Gordon, I. E., Barbe, A., Chris Benner, D., Bernath, P. F.,
Birk, M., Boudon, V., Brown, L. R., Campargue, A., Champion, J.-P.,
Chance, K., Couderti, L. H., Dana, V., Devi, V. M., Fally, S., Flaud, J.-M., Gamache, R. R., Goldman, A., Jacquemart, D., Kleiner, I., Lacome, N.,
Lafferty, W. J., Mandin, J.-Y., Massie, S. T., Mikhailenko, S. N., Miller,
C. E., Moazzen-Ahmadi, N., Naumenko, O. V., Nikitin, A. V., Orphal, J.,
Perevalov, V. I., Perrin, A., Predoi-Cross, A., Rinsland, C. P., Rotger, M.,
Simeckova, M., Smith, M. A. H., Sung, K., Tashkun, S. A., Tennyson, J., Toth,
R. A., Vandaele, A. C., and Vander Auwera, J.: The HITRAN 2008 molecular
spectroscopic database, J. Quant. Spectrosc. Ra., 110, 533–572,
2009.Ryan, N. J. and Walker, K. A.: The effect of spectroscopic parameter
inaccuracies on ground-based millimeter wave remote sensing of the
atmosphere, J. Quant. Spectrosc. Ra., 161, 50–59,
10.1016/j.jqsrt.2015.03.012, 2015.Ryan, N. J., Palm, M., Raffalski, U., Larsson, R., Manney, G., Millán, L., and Notholt, J.: Strato-mesospheric carbon monoxide profiles above Kiruna, Sweden (67.8∘ N, 20.4∘ E), since 2008, Earth Syst. Sci. Data, 9, 77–89, 10.5194/essd-9-77-2017, 2017.Ryan, N. J., Kinnison, D. E., Garcia, R. R., Hoffmann, C. G., Palm, M., Raffalski, U., and Notholt, J.: Assessing the ability to derive rates of polar middle-atmospheric descent using trace gas measurements from remote sensors, Atmos. Chem. Phys., 18, 1457–1474, 10.5194/acp-18-1457-2018, 2018.Schranz, F., Fernandez, S., Kämpfer, N., and Palm, M.: Diurnal variation in middle-atmospheric ozone observed by ground-based microwave radiometry at Ny-Ålesund over 1 year, Atmos. Chem. Phys., 18, 4113–4130, 10.5194/acp-18-4113-2018, 2018.Schranz, F., Tschanz, B., Rüfenacht, R., Hocke, K., Palm, M., and Kämpfer, N.: Investigation of Arctic middle-atmospheric dynamics using 3 years of H2O and O3 measurements from microwave radiometers at Ny-Ålesund, Atmos. Chem. Phys. Discuss., 10.5194/acp-2018-1299, in review, 2019.Schwartz, M., Pumphrey, H., Livesey, N., and Read, W.: MLS/Aura Level 2
Carbon Monoxide (CO) Mixing Ratio V004, version 004, Greenbelt, MD, USA,
Goddard Earth Sciences Data and Information Services Center (GES DISC),
10.5067/AURA/MLS/DATA2005, 2015.Sheese, P. E., Walker, K. A., Boone, C. D., Bernath, P. F., Froidevaux, L.,
Funke, B., Raspollini, P., and von Clarmann, T.: ACE-FTS ozone, water vapour,
nitrous oxide, nitric acid, and carbon monoxide profile comparisons with
MIPAS and MLS, J. Quant. Spectrosc. Ra., 186, 63–80,
10.1016/j.jqsrt.2016.06.026, 2017.Smith, A. K., Garcia, R. R., Marsh, D. R., and Richter, J. H.: WACCM
simulations of the mean circulation and trace species transport in the
winter mesosphere, J. Geophys. Res., 116, D20115, 10.1029/2011JD016083,
2011.
Solomon, S., Garcia, R. R., Olivero, J. G., Bevilacqua, R. M., Schwarzt, P.
R., Clancy, R. T., and Muhleman, D. O.: Photochemistry and Transport of
Carbon Monoxide in the Middle Atmosphere, J. Atmos. Sci., 42, 1072–1083,
1985.Straub, C., Murk, A., and Kämpfer, N.: MIAWARA-C, a new ground based water vapor radiometer for measurement campaigns, Atmos. Meas. Tech., 3, 1271–1285, 10.5194/amt-3-1271-2010, 2010.
Straub, C., Tschanz, B., Hocke, K., Kämpfer, N., and Smith, A. K.: Transport of mesospheric H2O during and after the stratospheric sudden warming of January 2010: observation and simulation, Atmos. Chem. Phys., 12, 5413–5427, 10.5194/acp-12-5413-2012, 2012.Straub, C., Espy, P. J., Hibbins, R. E., and Newnham, D. A.: Mesospheric CO above Troll station, Antarctica observed by a ground based microwave radiometer, Earth Syst. Sci. Data, 5, 199–208, 10.5194/essd-5-199-2013, 2013.Studer, S., Hocke, K., Schanz, A., Schmidt, H., and Kämpfer, N.: A climatology of the diurnal variations in stratospheric and mesospheric ozone over Bern, Switzerland, Atmos. Chem. Phys., 14, 5905–5919, 10.5194/acp-14-5905-2014, 2014.Velazco, V., Wood, S. W., Sinnhuber, M., Kramer, I., Jones, N. B., Kasai, Y., Notholt, J., Warneke, T., Blumenstock, T., Hase, F., Murcray, F. J., and Schrems, O.: Annual variation of strato-mesospheric carbon monoxide measured by ground-based Fourier transform infrared spectrometry, Atmos. Chem. Phys., 7, 1305–1312, 10.5194/acp-7-1305-2007, 2007.Vowinkel, B.: Passiv Mikrowellenradiometrie, Vieweg+Teubner Verlag,
Weisbaden, 10.1007/978-3-322-86042-2, 1988.
Waters, J., Froidevaux, L., Harwood, R., Jarno, R., Pickett, H., Read, W.,
Siegel, P., Cofield, R., Filipiak, M., Flower, D., Holden, J., Lau, G.,
Livesey, N., Manney, G., Pumphrey, H., Santee, M., Wu, D., Cuddy, D., Lay,
R., Loo, M., Perun, V., Schwartz, M., Stek, P., Thurstans, R., Boyles, M.,
Chandra, S., Chavez, M., Chen, G.-S., Chudasama, B., Dodge, R., Fuller, R.,
Girard, M., Jiang, J., Jiang, Y., Knosp, B., LaBelle, R., Lam, J., Lee, K.,
Miller, D., Oswald, J., Patel, N., Pukala, D., Quintero, O., Scaff, D.,
Snyder, W., Tope, M., Wagner, P., and Walch, M.: The Earth Observing System
Microwave Limb Sounder (EOSMLS) on the Aura satellite, IEEE T. Geosci.
Remote, 44, 1075–1092, 2006.
Zhu, X. and Holton, J. R.: Mean fields
induced by local gravity-wave forcing in the middle atmosphere, J. Atmos.
Sci., 44, 620–630, 1987.