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JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 113, D03102, doi:10.1029/2007JD008725, 2008

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ArticleDetection of migrating diurnal tide in the tropical upper troposphereand lower stratosphere using the Challenging Minisatellite Payload
radio occultation data
Zhen Zeng,1,2,3 William Randel,4 Sergey Sokolovskiy,1 Clara Deser,4 Ying-Hwa Kuo,1,2
Maura Hagan,4 Jian Du,5 and William Ward5
Received 29 March 2007; revised 25 September 2007; accepted 24 October 2007; published 2 February 2008.

[1] The atmospheric limb sounding technique making use of radio signals transmitted by

the Global Positioning System (GPS) has already proven to be a promising approachfor global atmospheric measurements. In this study, we assess for the first time thepotential of GPS radio occultation soundings for detecting the migrating diurnal tide.
Retrieved temperatures between 10 and 30 km in the tropics from the Challenging
Minisatellite Payload (CHAMP) occultation observations during May 2001 to August
2005 are analyzed using space-time spectrum analysis to isolate diurnal waves. Because ofincomplete local time (LT) coverage of the monthly CHAMP occultation data in any given
year, data from all available years are merged to obtain complete 24-h LT coverage.
The effects of aliasing associated with uneven data sampling and measurement noise are
estimated using synthetic data. The results show the feasibility of determining tidal
structures from the composite CHAMP occultation data, and the vertical, seasonal, andlatitudinal structures of the diurnal tide are presented. The estimated diurnal amplitude
generally increases with altitude, exhibiting a maximum of order 1 K at 30 km. The
estimated phase indicates an upward propagating mode above 14 km with a vertical
wavelength about 20 km. The observed diurnal tide at 30 km exhibits a distinct seasonallatitudinal variation. Comparison of the observed diurnal tide to the simulated tide in
the extended Canadian Middle Atmosphere Model (CMAM) and Global-Scale Wave
Model Version 2 (GSWM02) indicates that CMAM overestimates the amplitude but
reproduces the seasonal-latitudinal variation of the diurnal tide while GSWM02
simulates well the annual mean amplitude but lacks the seasonal-latitudinal variation of
the diurnal tide.
Citation: Zeng, Z., W. Randel, S. Sokolovskiy, C. Deser, Y.-H. Kuo, M. Hagan, J. Du, and W. Ward (2008), Detection of migrating
diurnal tide in the tropical upper troposphere and lower stratosphere using the Challenging Minisatellite Payload radio occultation data,
J. Geophys. Res., 113, D03102, doi:10.1029/2007JD008725.

1. Introduction[2] Atmospheric tides play an important role in thegeneral circulation and the coupling between the lower
and the middle and upper atmosphere [Vial, 1989; Kato,
1989; Forbes, 1995; Hagan, 2000]. Knowledge of tides isalso important for analysis of satellite data sampled at
different local solar times, data assimilation [Swinbank etal., 1999] and temperature trend estimation [Keckhut et al.,1
University Corporation for Atmospheric Research, Boulder, Colorado,USA.
2
Also at National Center for Atmospheric Research, Boulder, Colorado,USA.
3
Now at Department of Earth and Ocean Sciences, University of British
Columbia, Vancouver, British Columbia, Canada.
4
National Center for Atmospheric Research, Boulder, Colorado, USA.
5
Physics Department, University of New Brunswick, Fredericton, New
Brunswick, Canada.

Copyright 2008 by the American Geophysical Union.
0148-0227/08/2007JD008725$09.00

2001]. Observational and theoretical studies indicate that
migrating tides (components that propagate westward withthe apparent motion of the Sun) are primarily excitedthrough the zonally symmetric absorption of solar infraredand ultraviolet radiation by tropospheric water vapor andstratospheric ozone, whereas nonmigrating tides (modes
which are not Sun-synchronous) are generated by zonallyasymmetric thermal forcing (such as planetary boundary
layer heat flux, latent heat release in the tropical troposphere) and nonlinear interactions between tides and otherpropagating waves [Lieberman, 1991; Teitelbaum and Vial,
1991; Oberheide et al., 2002; Hagan and Forbes, 2002,
2003; Lieberman et al., 2004; Grieger et al., 2004].
[3] The characteristics of tides have been established in a
number of theoretical works by employing classical tidal
theory [Chapman and Lindzen, 1970], mechanistic models[Forbes, 1982; Hagan et al., 1995; Hagan, 2000] andgeneral circulation models [Tokioka and Yagai, 1987;
McLandress, 1997, 2002a]. Ground-based lidar/radar

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[Manson et al., 2002; She, 2004] and satellite observations[Hays et al., 1994; Burrage et al., 1995; McLandress et al.,
1996; Wu et al., 1998] provide an assessment of tidal
structures in nature and their variability on different scales.
Although progress has been made in tidal studies over the
past 30 years, because of the limitations of both the spaceborne (limited local time coverage and coarse verticalresolution) and ground-based instruments (limited spatial
coverage), specification of the global structure and shortterm variability of the tides still merits further improvement.
[4] To further investigate the tidal characteristics and theirvariability, we introduce a relatively new type of data, GPSradio occultation (RO) data, for tidal analysis. These data
provide coverage in the upper troposphere and lower
stratosphere (UTLS), an altitude range that is lower thanthat examined in most observational tidal studies. In the
GPS RO, radio signals from the GPS satellites, received
onboard the LEO (Low Earth’s Orbit) satellite, probe theEarth’s atmosphere in a limb viewing mode. Propagation of
the signals is influenced by the atmospheric refractivity. The
fundamental GPS RO observable is the phase delay which
is precisely calibrated by ultrastable GPS clock synchronized with the most precise atomic clock on the ground. The
phase of the occulted GPS signal is used for deriving
atmospheric refractivity, and then temperature, pressureand humidity as functions of altitude [Kursinski et al.,1997]. The proof-of-concept GPS/Meteorology (GPS/
MET) experiment and follow-on CHAMP and SAC-C(Satellite de Aplicaciones Cientificas-C) missions havedemonstrated high accuracy and vertical resolution and all
weather capability of the GPS RO. These and other importantcharacteristics inherent to GPS RO such as self-calibrationand mission independence (that allow keeping long-term
records without intersatellite calibration) make GPS RO a
unique technique for remote sensing of the atmosphere.
Another feature of the GPS RO relative simplicity of the
instrument (GPS RO receiver), allows launching constellations consisting of large number of GPS RO satellites by
increasing density of the observations and providing globally
uniform coverage in local time which is important for
observation of tides and specification of their short-term
variability. The recently launched (15 April 2006) sixsatellite COSMIC (Constellation Observing System forMeteorology, Ionosphere and Climate) mission is alreadyin operation and will provide global uniform coverage inlocal time at the end of 2007 [Anthes et al., 2008].
Following up, there is a plan for launching a 100-satellite
GPS RO constellation CICERO (Community Initiative forContinuing Radio Occultation) (http://www.geooptics.com),
providing unprecedented global coverage of GPS RO
soundings.
[5] In order to assess the feasibility of RO data for tidal
study, we first analyze the tropical tides in terms oftemperature estimated from the CHAMP RO observations
at 10– 30 km from May 2001 through August 2005. The
German CHAMP mission was launched in July 2000, and
its RO soundings have been available since May 2001
[Wickert et al., 2001]. Comparisons of CHAMP RO datawith correlative data indicate that CHAMP RO soundings
possess the temperature accuracy of 1 – 2 K in the upper
troposphere and lower stratosphere [Kuo et al., 2004, 2005].
Next we compare the observed tidal characteristics with

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those simulated by the Global-Scale Wave Model Version 2(GSWM02) [Hagan and Forbes, 2002, 2003] and extended
Canadian Middle Atmosphere Model (CMAM) [Beagley etal., 2000; Fomichev et al., 2002].
[6] The outline of the paper is as follows: Section 2
provides a detailed description of the temporal and spatialsampling characteristics of the CHAMP RO data and ouranalysis methodology, with special attention paid to theerror estimation. Section 3 briefly describes the two models,
CMAM and GSWM02 for comparisons with the CHAMPRO observations. Section 4 presents the results of the tidal
analysis, including the vertical structure of the migrating
diurnal tide, as well as its latitudinal and seasonal variation.
Section 5 summarizes the main results.

2. Data and Analysis Method
2.1. CHAMP RO Data and Sampling[7] In this study, we use CHAMP RO temperature profiles between 30°S and 30°N from May 2001 to August
2005 at altitudes 10– 30 km. In this altitude range RO
temperatures are known to be accurate within 1 – 2 K.
Normally, CHAMP produces 200 – 250 occultations daily,
but after the quality control applied during the inversion, thenumber of retrieved temperature profiles typically is less.
[8] An important problem arising from using CHAMP
RO data for the study of diurnal variations is the local time(LT) coverage. Because of the CHAMP orbit (87° inclination) precession in local time at a rate of 5.6 min/d,complete 24-h LT coverage would be obtained every
130 d. However, reception of RO signals from different
GPS satellites within approximately ±60° azimuth range allows
sampling occultations within approximately ±2000 kmband around CHAMP orbit by increasing the coverage andeffectively reducing the repeat period to 108 d at theequator [Wu et al., 2005]. Figure 1a shows the distributionof CHAMP RO data between 5°S and 5°N as a function of
day of year and LT. Each color corresponds to observations
from a particular year. The local time coverage at otherlatitudinal bands within 30°S – 30°N is similar. It is seen thatnearly complete LT coverage can be obtained if the daily
data from all years are merged. However, before merging
the data, it is important to remove interannual and intraannual variations (zonally averaged) that may alias into thediurnal cycle. Merging the data from consecutive days
further increases the density and uniformity of the data
sampling distribution. This will be further described in
section 2.2.
2.2. Tidal Analysis[9] All CHAMP RO temperature profiles are interpolated
onto a standard vertical grid with 0.2 km resolution from 10
to 30 km altitude. The observations within 30°S – 30°N are
subdivided into narrower latitude bands 10° in width andoverlapping by 5°. Figure 2a shows 90-d running mean RO
temperatures zonally averaged between 5°S and 5°N as afunction of altitude and time (note the long-term mean foreach altitude has been removed for ease of visualization). A
strong annual cycle is observed near 18 km with amplitude
around 6 K. Above 20 km, the quasi-biennial oscillation(QBO) is prominent, with maximum amplitude around 5 Knear 25 km. The QBO signal progresses downward at a rate

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Figure 1. (a) Distribution of CHAMP RO soundings between 5°S and 5°N in the coordinates of local
solar time (LT) and month. Different colors correspond to different years as indicated. (b) Distribution of
CHAMP RO residual temperatures (K) at 30 km for 90-d composite data centered at 15 January between
5°S and 5°N in longitude and UT coordinates.
of approximately 1km/month. Temporal spectral analysis of
the temperature residuals shown in Figure 2a confirms thedominance of the annual and QBO signals (Figure 2b). We
note that since this study is related to low latitudes (30°S–
30°N), in each latitudinal band CHAMP successive samples
are separated by about 12 h in LT. Thus the diurnal signal,
which is dominant in the LT representation, manifests itself
as the high-frequency signal (with CHAMP orbital period
about 1.5 h) in the UT representation. This signal is well
suppressed by running average and will not alias into theinterannual and intra-annual signals significantly. These
zonally averaged signals are removed from the daily datafrom each individual year. Then the daily data from all4+ years are merged into the composite subsets. Althoughthese subsets cover all LT, their spatial sampling distribution
is too sparse and nonuniform for robust spectral analysis. In

order to increase the sampling density and uniformity of thespatial coverage, these composite daily subsets from consecutive days are further merged into a composite set, after
removal of the daily mean from each subset. Removal of thedaily mean is necessary to prevent aliasing of the seasonal
signal into the diurnal signal when merging data forconsecutive days. Figure 1b shows the distribution of 90-dcomposite RO data centered at 15 January for 5°S–5°N at
30 km as a function of longitude and universal time (UT).
The colors correspond to the temperature residuals from thedaily mean. It is evident that the 90-d composite data
provide sufficient data density and uniformity for robust
estimation of the tidal parameters by space-time spectral
analysis and the 90-d composite sets are used in this study.
The use of 90-d composite data sets for analysis may result

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least 64 points  64 points in the 24 h  360° longitude
domain.
[11] In order to quantitatively estimate the effects ofnonuniformity of the data sampling in the UT and longitude
coordinates and of the measurement noise on the derived
tidal Pparameters, we use synthetic data based on the funcAn,s cos(nWt + sl fn,s) + z, where the first term
tionn;srepresents
tidal component and second term is the noise; W
is the angular frequency of Earth’s rotation, t and l are UT
and longitude, respectively; n is the frequency in cycles per
day and s is zonal wave number (positive for westward
propagation); An,s and fn,s are amplitude and phase (time of
maximum in UT at the longitude of the Greenwich meridian) of the corresponding tidal component. The measurement noise z is random and normally distributed withstandard deviation of 2 K, which is close to the upper
estimate of the RO measurement error. We sample thesynthetic function at the UTs and longitudes of the CHAMP
RO data, then interpolate to the uniform 6464 mesh,calculate the spectrum and compare the spectral amplitudes
and phases with tshe original ones as specified in thefunction.
[12] Figure 3 shows an example of the spectrum forsimulated diurnal tide (n = 1, s = 1) with amplitude A1,1 =1K sampled on the composite distribution centered at
January (shown in Figure 1b). Solid and dashed lines show
the spectra with and without the measurement noise, respectively. Nonuniformity of the sampling distribution results inaliasing and appearance of high-order harmonics with
nonzero amplitudes (this effect is small but still visible as
the dashed line is above zero in Figure 3). This results inmean reduction of the tidal amplitudes: by 0.5% and1.5% for diurnal and semidiurnal migrating tides, respectively. There is a bias in the phase of about 0.35 h for bothFigure 2. (a) Month-altitude cross section of the deviationof 90-d running mean CHAMP RO temperatures from thelong-term mean averaged between 5°S and 5°N (contour
interval: 1 K). (b) Vertical structure of temporal power
spectra of the residual temperature residuals shown in
Figure 2a (contour interval: 0.5 K).

in certain smoothing of the seasonal variability of the tidal
parameters.
[10] Before applying the space-time spectral analysis, weinterpolate the unevenly sampled data (such as that shownin Figure 1b) onto a uniform grid with dimensions of
longitude and UT. This is done at each altitude (0.2 km
vertical resolution from 10 to 30 km) and latitude (10°bands overlapping by 5° from 30°S to 30°N) separately. The
resolution of the uniform grid must allow sufficient spectral
bandwidth for higher-order harmonics in order to minimize
spectral aliasing that would result in errors in amplitude andphase of the lower-order harmonics. We tested the nearest
neighbor interpolation on uniform grids with different
resolutions, and found that the results of the spectral
analysis become stable, i.e., do not change substantially,in the frequency domain of interest with a grid mesh of at

Figure 3. An example of the power spectrum of thesimulated diurnal tide sampled by a 90-d composite
distribution centered at 15 January, with (solid) and without(dashed) measurement noise. See text for details.

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diurnal and semidiurnal tides. The standard deviations of thespectral amplitudes induced by 2 K measurement noise areabout 0.1 K for both diurnal and semidiurnal tides. The
noise-induced standard deviations of the phases for 1 K tidal
amplitude are about 0.2 h and they substantially increase as
the tidal amplitude approaches 0.1 K.
2.3. An Example of the Tidal Analysis Results[13] Figure 4 gives an example of the spectral analysis
results for the composite data set for the latitude band
5°N – 5°S in winter (December, January, February: DJF)
(Figures 4a, 4c, and 4e) and summer (June, July, August:
JJA) (Figures 4b, 4d, and 4f). Figures 4a and 4b show
contour plots of the spectral amplitudes, as functions ofzonal wave number and period at 30 km altitude; dashedlines show the locations of migrating tides. In both seasons,the main spectral power is concentrated in the migrating
diurnal component with amplitudes 1 K. In DJF, an
additional spectral peak corresponding to the migrating
semidiurnal tide is evident. The spectral power in thenonmigrating tidal components is very small in both seasons. The vertical structures of the diurnal and semidiurnal
tidal amplitudes are presented in Figures 4c and 4d andFigures 4e and 4f respectively. Dashed lines, as before,show the locations of migrating components. In bothseasons, the strongest spectral amplitudes correspond tothe migrating diurnal tide, with larger amplitudes in DJF
than JJA. The diurnal tidal amplitudes are largest near 30 km(1.0 K versus 0.8 K), and rather weak below 17 km (thetropopause). The semidiurnal migrating tide (Figures 4eand 4f), in general, is much weaker (especially for JJA)
and thus is less clearly distinguishable from the measurement noise (to an even greater extent this is related to otherspectral components). In the remainder of this study we will
focus only on the migrating diurnal tides (by addressing
studies of other components in the tidal spectrum in the
future when more dense and uniform data sets are available).

3. Models: GSWM02 and CMAM
3.1. Global-Scale Wave Model (GSWM) Version 2[14] The GSWM02, the latest version of the GSWM,
simulates monthly mean diurnal and semidiurnal tides
excited by climatological monthly mean tidal forcing. It isa two-dimensional, linearized, steady state numerical tidal
and planetary-wave model extending from the surface to thethermosphere (250 km), based on the linearized andextended Navier-Stokes equations for perturbation fields
with specified periods and zonal wave numbers upon thezonal mean background atmosphere (http://www.hao.ucar.
edu/modeling/gswm/gswm.html). The resolution of
GSWM02 is about 4 km in altitude and 3° in latitude.
Standard GSWM02 tidal results account for realistic tidal
forcing due to the absorption of solar radiation by ozone andwater vapor, latent heat release, and the effects of empiricalbackground climatology of zonal mean temperature, neutral
density, zonal wind and ozone concentration, as well as
wave dissipation. However, the GSWM02 cannot include
tidal forcing due to nonlinear wave-mean flow and wavewave interaction. Next we examine tides in CMAM which
includes nonlinear effects.

D031023.2. Extended Canadian Middle Atmosphere Model(CMAM)[15] The extended CMAM is a 3D spectral generalcirculation model (GCM) (triangular truncation at total
wave number n = 32 (T32) corresponding to a latitudinallongitudinal resolution of 6°  6°) extending from theground to an altitude of approximately 210 km [see Beagley
et al., 2000; Fomichev et al., 2002]. The model contains
70 levels in the vertical with a resolution of approximately
3 km above the tropopause. The model includes heating
calculated using comprehensive solar and terrestrial radiative transfer schemes, and parameterizations of latent heatrelease and convective adjustment. The solar radiation in themodel accounts for the absorption in the near infrared bytropospheric water vapor, in the ultraviolet by ozone in thestratosphere and mesosphere, and in the extreme ultraviolet
by molecular oxygen in the thermosphere. The terrestrial
radiation primarily involves the long-wave radiative transfer
by water vapor, ozone and carbon dioxide. Realistic surface
topography, planetary boundary layer effects, a full hydrological cycle, and the radiative effects of clouds are includedin the model. Water vapor is a prognostic variable soradiative heating/cooling associated with this constituent
exhibits considerable variability over a large range of scales.
On the other hand, the ozone heating is calculated usingozone climatology and therefore does not exhibit the fullrange of variability which would develop in the real
atmosphere. The model includes the effects of both verticaland horizontal diffusion through physically based parameterizations, as well as the heating by parameterized gravity
waves.
[16] The data used for the present study are from the thirdyear of a 4-year model run. The output for this year is
considered representative of the model behavior. The atmospheric tides in the model are the results of the combined
heating from the various sources listed above. Monthly
mean amplitudes for the migrating diurnal tides are computed using model output on 3-h interval as described by
Ward et al. [2005]. McLandress [2002a] demonstrated thecapability of this model to reproduce the migrating diurnal
tide and its seasonal variation via comparisons with themeasurements from the wind imaging interferometer on
UARS in the mesosphere and lower thermosphere. In thisstudy we validate the extended CMAM results against the
CHAMP RO observations in the UTLS. Both GSWM and
CMAM model output are further averaged in the same 10°
latitudinal bands and 3-month running windows as theobservations.

4. Results and Discussion
4.1. Vertical Structure of Migrating Diurnal Tide[17] Figure 5 shows the vertical structure of the monthly
temperature amplitudes (Figure 5a) and phases (Figure 5b)of the migrating diurnal tide for the latitude band 5°S – 5°N
based on 90-d composite data sets centered at the middle ofeach month (thin black lines). The thick blue lines give theannual mean amplitude and phase. Consistent with Figure 4,
the diurnal tidal amplitudes are small (0.1 – 0.3 K) below
17 km, and reach minimum values near the tropopause(17 km). They increase rapidly with height between 17 km

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Figure 4. Spectral amplitudes of the composite CHAMP RO temperature residuals over 5°N to 5°S(a and b) at 30 km as a function of zonal wave number and period and at 10– 30 km as a function of zonal
wave number and height for the (c and d) diurnal and (e and f) semidiurnal tides in DJF (Figures 4a, 4c,
and 4e) and JJA (Figures 4b, 4d, and 4f). The contour lines indicate the noise level.

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Figure 5. Vertical profiles of diurnal temperature (a) amplitudes and (b) phases over 5°S – 5°N forcomposite 90-d subsets centered at the 15th of each month (thin black lines) derived from CHAMP RO
observations. The colored curves show the annual mean amplitudes and phases for CHAMP RO
observations (blue) and the CMAM (red) and GSWM02 (green) model simulations.

and 18 km, reaching values 0.4 K, and increase further
with altitude above 25 km, reaching maxima 0.6– 1.1 K
around 30 km. The annual mean CHAMP RO amplitudes(blue) are in overall good agreement with the CMAM (red)and GSWM02 (green) simulations, although the observations exhibit sharper vertical structures than the model
simulations likely because of higher vertical resolution of
the observations. CMAM exhibits slightly (10 – 30%) larger
amplitudes than the CHAMP RO data above 25 km. The
phases estimated from CHAMP RO data, shown in
Figure 5b, decrease with altitude above 14 km, and remain
almost constant below 14 km. This indicates an upward
propagating diurnal tide above 14 km, and a trapped tide
below 14 km in the tropics. It is known that the ozone in thestratosphere is important for forcing the atmospheric tides
because of its absorption of solar ultraviolet radiation.
However, because of large depth of the ozone layer (around
40– 50 km) compared to the vertical wavelength of diurnal
tide (see below) the vertical modes forced by the deep ozone
heating tend to interfere destructively and do not play animportant role in the diurnal tide [Chapman and Lindzen,
1970]. On the other hand, heating from the relatively
shallow layer of tropospheric water vapor (<10 km) efficiently excites the diurnal tide [Andrews et al., 1987]. The
bifurcation of phases near 17 km for some months resultsfrom the small amplitudes that are comparable to the noiselevel. The vertical structure of annual mean phases of thediurnal tide from CHAMP RO observations agrees wellwith both models, although for the amplitudes there is finervertical structure in the observations than in the model
simulations. The turning point around 22 km in the CHAMP
RO data is not simulated by either model. Between 14 km
and 22 km, the vertical slope of the phase is smaller in the
RO data than in the models. The vertical wavelength (theheight interval corresponding to the phase lapse of one fullcycle) of the diurnal tide is around 20 km.
[18] The vertical structure of annual mean diurnal amplitude from the CHAMP RO data is in good qualitative
agreement with that estimated from four-times-daily radiosonde data from the Tropical Ocean Global Atmosphere/
Coupled Ocean Atmosphere Response Experiment [Seidel
et al., 2005]. However, the annual mean diurnal phase isalmost constant with altitude in the work by Seidel et al.
[2005], different from the results based on CHAMP RO
observations and both model simulations.
4.2. Latitudinal and Seasonal Structure of the
Migrating Diurnal Tide[19] In order to give a general view of the latitudinal
structure of the migrating diurnal tide, the annual mean
amplitudes and phases from CHAMP RO and model
simulations are shown in Figure 6. All data sources display
maximum amplitudes up to 1 K at 30 km over the equator,
but with CMAM amplitudes being roughly 20% larger than
GSWM02 and CHAMP RO. The GSWM02 simulationshows a more symmetric structure of diurnal amplitudes
with respect to the equator than CMAM or observations.
The annual mean phases from CHAMP RO and bothmodels are in good agreement between 15°S and 15°N,
decreasing with altitude. The highly variable phases from
CHAMP RO observed near 17 km are related to the small
amplitudes. GSWM02 also shows more symmetric phase
distribution around the equator than the other data sources.
[20] Figure 7 presents diurnal tidal amplitudes as functions of altitude and latitude of CHAMP RO observations(Figure 7, left), CMAM (Figure 7, middle) and GSWM02
simulations (Figure 7, right) for both solstitial and equinoctial(March, April, May: MAM; September, October, November:7 of 12

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Figure 6. Annual mean (left) amplitudes (0.2 K contour interval) and (right) phases (3 h contour
interval) of the diurnal tide as a function of height and latitude from (top) CHAMP RO observations and(middle) CMAM and (bottom) GSWM02 model simulations.
SON) seasons. Latitudes and altitudes of maximum amplitude of the diurnal tide are generally in good agreement
between CHAMP RO and CMAM. The maxima at 30 km
shift from 2– 5°N in DJF to 5 –10°S in JJA, while lower
down the region of large amplitudes shift from 15°S in DJF
to 15°N in JJA. During the equinoxes, the maxima at
30 km tend to move to the equator (with some delay for
CMAM in SON). This latitudinal-altitudinal variation has
been examined using the CMAM [McLandress, 1997,
2002b; MacKenzie, 2005], and the results showed that it
might be caused by the interference of the diurnal latitudinal
asymmetric and symmetric modes, which both are excitedaround the solstice but have slightly different vertical wavelengths. GSWM02 exhibits a rather symmetric amplitude
distribution centered at the equator, with peak amplitudes

more comparable to those of CHAMP RO and considerably
weaker than CMAM. This equator symmetric structure
might be related to an overly weak excitation of theasymmetric mode in GSWM02. Stronger tides at equinoxes
than those at solstices are clearly evident in GSWM02 but
not in observations and CMAM. In addition, the tidal
amplitudes below 13 km in GSWM02 are smaller thanthose from CHAMP RO and CMAM. An additional perspective on seasonal-latitudinal variability of the diurnal
tide near 30 km is provided in Figure 8. The latitude of the
maximum tidal amplitude shifts with the seasons, occurring
at approximately 5°N in November-March and 10°S in
May– September in the CHAMP RO data. CMAM exhibits
the same seasonal variation, except with a 1 – 2 month delay.
Relatively weak amplitudes are observed during the transi-

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Figure 7. Seasonal (DJF, MAM, JJA, and SON) amplitudes of the diurnal tide as a function of height
and latitude from the (left) CHAMP RO observations and (middle) CMAM and (right) GSWM02 model
simulations. The contour interval is 0.2 K.
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Figure 8. Amplitudes of the diurnal tide near 30 km as a function of latitude and month from the (top)CHAMP RO observations and (middle) CMAM and (bottom) GSWM02 model simulations. The contour
interval is 0.25 K.
tion months April and October for the CHAMP RO data.
The diurnal tide derived from the UARS Microwave Limb
Sounder data [Wu et al., 1998] also exhibits similar seasonal-latitudinal structure. In contrast, GSWM02 exhibits maximum amplitudes at the equator throughout the year, withlargest values in March and October.

5. Summary[21] In this study, RO data from CHAMP are applied forthe first time to estimate the diurnal tidal structures in thetropical UTLS. Although monthly observations suffer fromlimited LT coverage, the composite data obtained by merging observations for the same day from different years over
the CHAMP period of record (51 months), after removing
potential aliasing effects from the QBO, allow examinationof the tides, and the results of spectral analysis support thevalidity of this approach.
[22] Mean climatologies of migrating diurnal tide are
estimated in terms of vertical, latitudinal and seasonalvariations of the tidal parameters. Diurnal tide temperature

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amplitudes reach maxima of about 1 K at 30 km near theequator, and show distinct seasonal variations. Diurnal
phase indicates an upward propagating diurnal tide above
14 km.
[23] Comparisons of the diurnal migrating tide obtained
from CHAMP RO and those from GSWM02 and CMAM
models show reasonable agreement in terms of amplitude,
phase and seasonality. CMAM tidal simulations are in
overall better agreement with the CHAMP RO observations,reproducing many observed features including the seasonallatitudinal variations of diurnal tide. The GSWM02 shortcomings presented herein are consistent with what Zhang etal. [2006] report for migrating tidal temperature diagnostics
in the upper stratosphere and mesosphere based uponmeasurements made by the SABER instrument on the
TIMED satellite. That is, the latitude structure of the
migrating GSWM02 tide is simpler and more symmetric
about the equator than the SABER signatures. The comparison of monthly diurnal tides for CHAMP RO and for
SABER measurements at 20 – 30 km during the 4-year
period also shows good agreement in magnitude and phase(X. Zhang, personal communication, 2006).
[24] In this study, only the climatological characteristicsof the diurnal tide were resolved because of incomplete
sampling of the diurnal cycle in any given year by the
CHAMP RO. After full deployment of the six satellites
COSMIC (Constellation Observing System for Meteorology, Ionosphere & Climate) mission by evenly separating
orbital planes, the tides will be able to be resolved within asingle year and hence their interannual variability and smallscale tidal variations may also be assessed.
[25] Acknowledgments. This study was funded by a collaborative
project between NCAR and UCAR Office of Programs. We thank Jack
Fellows and Tim Killeen for their support and encouragement. Special
thanks to Kevin Trenberth for his initial suggestions for this study. The
National Corporation for Atmospheric Research is supported by National
Science Foundation.

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