AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-11-1031-2018An intercomparison of stratospheric gravity wave potential energy
densities from METOP GPS radio occultation measurements and
ECMWF model dataRappMarkusmarkus.rapp@dlr.dehttps://orcid.org/0000-0003-1508-5900DörnbrackAndreashttps://orcid.org/0000-0003-0936-0216KaiflerBerndhttps://orcid.org/0000-0002-5891-242XDeutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, GermanyMeteorologisches Institut München, Ludwig-Maximilians-Universität München, Munich, GermanyMarkus Rapp (markus.rapp@dlr.de)22February20181121031104824September20179October201722December201716January2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://amt.copernicus.org/articles/11/1031/2018/amt-11-1031-2018.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/11/1031/2018/amt-11-1031-2018.pdf
Temperature profiles based
on radio occultation (RO) measurements with the operational European METOP
satellites are used to derive monthly mean global distributions of
stratospheric (20–40 km) gravity wave (GW) potential energy densities
(EP) for the period July 2014–December 2016. In order to test
whether the sampling and data quality of this data set is sufficient for
scientific analysis, we investigate to what degree the METOP observations
agree quantitatively with ECMWF operational analysis (IFS data) and
reanalysis (ERA-Interim) data. A systematic comparison between corresponding
monthly mean temperature fields determined for a
latitude–longitude–altitude grid of 5∘ by 10∘ by 1 km is
carried out. This yields very low systematic differences between RO and model
data below 30 km (i.e., median temperature differences is between -0.2 and
+0.3 K), which increases with height to yield median differences of
+1.0 K at 34 km and +2.2 K at 40 km. Comparing EP values
for three selected locations at which also ground-based lidar measurements
are available yields excellent agreement between RO and IFS data below
35 km. ERA-Interim underestimates EP under conditions of strong
local mountain wave forcing over northern Scandinavia which is apparently not
resolved by the model. Above 35 km, RO values are consistently much larger
than model values, which is likely caused by the model sponge layer, which
damps small-scale fluctuations above ∼ 32 km altitude. Another reason
is the well-known significant increase of noise in RO measurements above
35 km. The comparison between RO and lidar data reveals very good
qualitative agreement in terms of the seasonal variation of EP,
but RO values are consistently smaller than lidar values by about a factor of
2. This discrepancy is likely caused by the very different sampling
characteristics of RO and lidar observations. Direct comparison of the global
data set of RO and model EP fields shows large correlation
coefficients (0.4–1.0) with a general degradation with increasing altitude.
Concerning absolute differences between observed and modeled EP
values, the median difference is relatively small at all altitudes (but
increasing with altitude) with an exception between 20 and 25 km, where the
median difference between RO and model data is increased and the
corresponding variability is also found to be very large. The reason for this
is identified as an artifact of the EP algorithm: this
erroneously interprets the pronounced climatological feature of the tropical
tropopause inversion layer (TTIL) as GW activity, hence yielding very large
EP values in this area and also large differences between model
and observations. This is because the RO data show a more pronounced TTIL
than IFS and ERA-Interim. We suggest a correction for this effect based on an
estimate of this “artificial” EP using monthly mean zonal mean
temperature profiles. This correction may be recommended for application to
data sets that can only be analyzed using a vertical background determination
method such as the METOP data with relatively scarce sampling statistics.
However, if the sampling statistics allows, our analysis also shows that in
general a horizontal background determination is advantageous in that it
better avoids contributions to EP that are not caused by gravity
waves.
Introduction
It has long been known that momentum and energy transport by gravity waves
(henceforth abbreviated as GWs) are of major importance for the mean thermal
and dynamical state of the middle atmosphere
(). Being mainly excited in the troposphere
by flow over terrain, by convection, or by spontaneous emission, GWs may
propagate both vertically and horizontally over large distances to deposit
their momentum and energy far away from their source upon instability or
transience
e.g.,.
Thus, GWs are an important mechanism that couples the middle and upper
atmosphere to the troposphere e.g.,and references
therein. In addition, it has recently been shown that GWs also
couple the middle atmosphere downward to the troposphere and
references therein. With minimum horizontal scales as small
as 10 km GWs must still be parameterized in global climate models with
typical horizontal resolutions of a few hundred kilometers. Hence, the
development of physics-based parameterizations of GWs and their effect on the
mean flow have been identified as a major research focus in the climate
research community .
Given this large importance of GWs, it is not surprising that efforts have
been undertaken to try to characterize GW sources, their propagation, and
their dissipation and wave–mean-flow interaction with complementary
experimental, theoretical, and numerical techniques see, e.g.,for
recent reviews, overview papers, and text books. Ground-based
remote sensing with lidars and radars and in situ observations with balloons,
research aircraft, and sounding rockets are critically important for process
studies. However, global satellite observations are needed to determine
dominant tropospheric source regions and processes as well as global
propagation pathways and the resulting gravity wave drag imposed on the mean
flow to constrain GW parameterizations for climate and weather prediction
models (). Since the pioneering
work by , , and there
have been many attempts to characterize the global distribution of gravity
wave activity using such different remote-sensing techniques as Limb
e.g., and Nadir sounders
e.g.,, as well as GPS-based radio
occultation (RO) measurements
e.g.,.
This paper focusses on the derivation of gravity wave potential energy
densities (EP) from GPS RO measurements
on board the operational METOP-A and METOP-B satellites operated by EUMETSAT
(European Organisation for the Exploitation of Meteorological
Satellites) and the subsequent systematic comparison of EP fields
with ECMWF (European Centre for Medium-Range Weather Forecasts) operational
forecast and reanalysis data. This is done to answer the question of whether the
sampling and data quality of the two operational METOP satellites is
sufficient to characterize the global stratospheric gravity wave activity
(measured in terms of EP) on a monthly basis. Furthermore, we
investigate whether the METOP observations agree quantitatively with the
ECMWF model fields such that the latter can be used for the interpretation of
observational results. Accordingly, this paper is organized as follows: in
Sect. we describe the database of METOP-A and METOP-B RO temperature data obtained between July 2014 and December 2016. In
addition, we give a brief introduction to the ECMWF data sets used for
comparison with the RO data. We compare both temperature data sets (RO and
ECMWF data) as a baseline for the subsequent comparison of derived
EP values. In Sect. we describe our approach to
derive EP, followed by Sect. , where we
thoroughly compare RO EP data to corresponding ECMWF data sets.
Similarities and differences are discussed in Sect. , in
which we will also derive and discuss a correction for erroneous
interpretation of the tropical tropopause inversion layer (TTIL) as gravity
wave activity. Finally, the major findings of this study are summarized in
Sect. , in which suggestions for future work will also be
made.
DatabaseMETOP-A/B GPS RO data
The METOP-A and B satellites orbit the Earth in a polar low Earth orbit and
are the platforms for a variety of instruments supporting the European
Weather Services including the Global Navigation Satellite System Receiver
for Atmospheric Sounding (GRAS) with which GPS RO measurements are performed,
delivering tropospheric humidity and tropospheric and stratospheric
temperature profiles. During typical months, these two satellites record a
total of ∼ 35 000–40 000 radio occultations. A typical sampling
pattern in terms of the latitude and longitude distribution of the number of
RO per month is shown in Fig. . This sampling is
determined by the orbital geometry of the METOP satellites on the one hand
and the GPS satellites on the other. Figure reveals that
there are typically between 10 and 50 occultations per 5∘ latitude
and 10∘ longitude interval with maximum sampling at latitudes between
20 and 60∘ north and south and minima near the poles and at the
Equator. Note that we will use a corresponding gridding of 36 × 36
grid points (i.e., 5∘ latitude by 10∘ longitude bins)
throughout this entire study.
(a) Number of METOP-A and B radio
occultations per 5∘ latitude and 10∘ longitude bin in June
2015. The total number of occultations in this month is about 35 000.
(b) Number of occultations per 5∘ latitude bin integrated
over all longitudes.
The METOP RO data are provided by the Radio Occultation Meteorology Satellite
Application Facility (ROM SAF) on an operational basis in near-real time and
can be downloaded from www.romsaf.org. The primary measured quantity is
the bending angle of the GPS radio waves as they transverse the refracting
atmosphere. From bending angle profiles corresponding refractivity profiles
can be derived, from which in turn also temperature profiles can be
determined (). The latter can be done either by
assuming that the refraction is entirely due to dry air (resulting in
so-called “dry” temperatures) or by accounting for tropospheric water vapor
by using additional information, e.g., from operational numerical weather
forecast data in the framework of a one-dimensional variational algorithm
that uses ECMWF Integrated Forecast System (IFS) data as a priori information
and references therein. The latter approach is
pursued by the ROM SAF and corresponding temperature data are denoted “wet”
temperatures. For the current study we will mainly use dry instead of wet
temperatures since the latter have been derived using model output and might
not be considered as “pure” measurements. Nevertheless, we will also
briefly consider wet temperatures and compare them to the more “original”
dry ones. Note that the ROM SAF provides dry and wet temperatures from
July 2014 onwards only. Hence, in this study we restrict ourselves to the
period from July 2014 to December 2016, i.e., a total of 30 months of data.
METOP temperature profiles are provided on geopotential heights, which will be
used here as the vertical coordinate. The fundamental vertical resolution of
the technique, Δz, is limited by diffraction as the GPS rays pass
through the atmosphere and results in about Δz=1-1.4 km in the
altitude range between 15 and 40 km. Over this vertical interval, the
horizontal line-of-sight resolution can be estimated to be around
190–270 km due to the limb geometry of the observations seefor
details.
ECMWF operational analysis and reanalysis data
For comparison to the METOP RO data we use two different data sets provided
by the ECMWF; one is the operational analyses from the IFS. These have a horizontal grid spacing of about 16 km
(TL1279) and were evaluated on 25 pressure levels between 1000
and 1 hPa which we converted to geopotential heights and interpolated them
on a regular vertical grid with 1 km spacing. We note that according to
only scales exceeding the grid spacing by several times
are resolved. Model output is available every 6 h. Details about the
model can be found in and in references therein.
The second model data set that we use is the ERA-Interim reanalysis.
ERA-Interim is a global atmospheric reanalysis starting from 1979 which is
based on a 2006 release of the IFS. The horizontal grid spacing of the data
set is approximately 80 km (T255). For the current study, model fields were
evaluated on 37 standard pressure levels between 1000 and 1 hPa which we
converted to geopotential heights and interpolated them on a regular vertical
grid with 1 km spacing. For details about ERA-Interim see .
Please note that ECMWF does assimilate RO bending angle data (among many
other data sets) from a variety of instruments including (but not limited to)
the METOP data for both the ERA-Interim reanalysis and the IFS
analyses seeas well as the ECMWF website. Thus,
ECMWF model fields and METOP RO data are obviously not completely
independent.
Zonal mean temperatures as a function of latitude
and altitude for the months March, June, and December 2015 (a–i)
from METOP-A and B radio occultations (a, d, g) and from ERA-Interim
(c, f, i).
Scatter plots (a) between RO-dry
temperatures and corresponding IFS data for 30 months of data between July
2014 and December 2016 for three selected altitudes. The red line shows a
linear fit to the data with slope b, y intercept a, and correlation
coefficient R (see insert). Panel (b) shows histograms of the
corresponding temperature differences between IFS and RO-dry data for the
same selected altitudes.
(a) Correlation coefficients as a
function of altitude for the correlation between ERA-Interim and IFS data
(black line), RO-wet and IFS data (blue line), and RO-dry and IFS data (red
line). (b) Corresponding median temperature differences (thick
lines) along with 10 and 90 % percentiles (thin lines) as a function of
altitude (same color code as in a).
Comparison between RO and ECMWF temperature data
In this subsection we systematically compare RO temperatures with ERA-Interim
and IFS model data. As a start, Fig. shows zonal mean
temperatures for the months March, June, and December 2015 derived from METOP
GPS RO dry data (left column), GPS RO wet data (middle column), and from ERA-Interim. Note that from now on we will refer to METOP GPS RO dry and wet
data as “RO-dry” and “RO-wet” data for brevity. Overall, all data sets
agree well, with, however, notable differences between the dry temperatures
and the other two data sets in the troposphere and at the highest altitudes
above 40 km. These findings are not surprising given that the retrieval for
wet temperatures uses ECMWF IFS data as a priori information, the
assumption of dry conditions is certainly violated in the troposphere, and
the quality of RO observations in general decreases significantly above
∼ 40 km altitude . In the following, we hence
restrict our comparison to altitudes between 20 and 40 km.
For a more quantitative comparison, we have binned the ECMWF data sets on the
same space and time grid as the RO data; i.e., mean profiles were determined
for the period of 1 month and a latitude–longitude–altitude grid of
5∘ by 10∘ by 1 km. Figure shows
corresponding scatter plots between RO-dry temperatures and corresponding IFS
data for all 30 months of data considered in this study (i.e.,
July 2014–December 2016) as well as histograms of the temperature
differences between the data for three selected altitudes. This reveals very
large correlation coefficients close to 1 between the data with a general
degradation of the (still very good) correlation as well as an increasing
bias between the data with increasing altitude. The full altitude variation
of the correlation coefficients between the considered data sets as well as
the median temperature differences along with corresponding 10 and 90 %
percentiles is shown in Fig. . This again shows an
almost perfect correlation between ERA-Interim and IFS data (as expected) and
between the RO-wet temperatures and the IFS. Again, only the dry temperatures
show a notable disagreement from the other data sets at altitudes above
∼ 35 km. This is further quantified with the median biases (and
percentiles) shown in panel b of Fig. , which shows a
median bias of +1 K (+2 K) between RO-dry temperatures and the IFS
(i.e., IFS temperatures are larger than dry RO temperatures) at 34 km
(40 km), with a corresponding large variability range as indicated by the
percentiles. We note that these values are in excellent quantitative
agreement with a previous study in which GPS RO observations were compared to
ECMWF data (). Compared to this bias of
the RO-dry data, it is again not surprising to see that the RO-wet
temperatures show a much smaller bias (close to zero) to the IFS and that
also the corresponding variability range is greatly reduced. Both derived
biases and variability ranges agree well with previous findings of an
analysis of the ROM SAF as described in the corresponding validation report
(). In all, RO temperatures agree well with ERA-Interim
and IFS temperatures such that it appears justified to proceed and next
compare corresponding EP values.
(a) Sample radio occultation
temperature profiles from December 2015 (black lines) with background
profiles (red lines) as determined with a fifth-order Butterworth filter with
15 km cutoff wavelength following .
(b) Corresponding temperature perturbation profiles (radio
occultation profile minus background profile).
Monthly mean latitude–longitude cross
sections of EP at selected altitudes of 30, 33, 36, and 39 km
(a–l) for December 2015. (a, d, g, j) METOP RO-dry data,
(b, e, h, k) IFS data, and (c, f, i, l) ERA-Interim data.
In all panels, black contour lines show zonal wind values from ERA-Interim.
Derivation of EP
We next turn to the derivation of EP from the various input
temperature data sets considered in this study. EP is defined as
follows:
EP(z)=12g2N2(z)T′(z)T0(z)2‾,
where g is acceleration of gravity,
N2=gT0⋅(dT0dz+gcp)
is the (squared) buoyancy frequency with the specific heat capacity of air
for constant pressure cp, T′ is the temperature perturbation
owing to the GW, and T0 is the background temperature. The overbar denotes
averaging, which is here carried out over the spatial domain of the
latitude–longitude grid and the time period of 1 month. In
Eq. () all quantities depend on height z except for g, for
which we use a constant value of 9.81 ms-2. The main challenge in
deriving EP(z) from measured temperature profiles lies in the
separation between background and perturbations. Different studies have used
various approaches such as filtering of profiles in the vertical or in the
horizontal provided that the horizontal sampling is sufficient. See
and for recent critical
discussions of the advantages and disadvantages of different techniques.
Comparison of time series of monthly mean
EP from RO and model data (ERA-Interim and IFS) for three
different locations: Sodankylä (a–c), Bavarian
Forest (d–f), and Lauder (g–i). The color code is
explained in the insert. (a, d, g), (b, e, h) and
(c, f, i) are for altitude ranges of 15–25, 25–35, and 35–45,
respectively.
(a–c) Comparison of time
series of monthly mean EP from METOP RO data for different
altitude ranges (black, blue, and red curves; see insert for color code) with
local Rayleigh lidar measurements of EP for the stations of
Sodankylä (a, d), the Bavarian Forest (b, e), and
Lauder (c, f). Lidar EP are shown as yellow
(25–35 km), light blue (35–45 km), or green (45–55 km) lines.
(d–f) Number of RO profiles (black lines) and nightly mean lidar
profiles (light blue lines) entering the monthly mean shown in the panels
above.
For this study, we follow the approach of ; i.e., we apply a
fifth-order Butterworth filter with a cutoff wavelength of 15 km to vertical
temperature profiles from the RO measurements, ERA-Interim, the
IFS, and ground-based lidar measurements. Applying this filter to
altitude profiles implies that scales longer than 15 km are assumed to be
the “background” (climatological structure plus planetary waves), denoted
T0(z), while shorter scales are assumed to be fluctuations due to
atmospheric gravity waves. This separation is expected to work well except
for in the tropical stratosphere, where Kelvin waves are known to occur with
vertical wavelengths well below 15 km e.g.,.
Hence, EP must be expected to be biased high in the tropics.
Nevertheless, we stick to this approach since it has the advantage that all
data sets analyzed in this study can be treated with identical analysis
routines, thus allowing us to directly and quantitatively compare
EP values from four independent data sets.
Resulting T0(z) profiles are then used to derive N2(z) profiles.
Arbitrarily chosen sample profiles from RO-dry data are shown in
Fig. . Figure shows both cases with
strong (middle panel) and weak GW activity (left panel). These sample
profiles further show that the background temperature determination has
weaknesses in cases with a very pronounced tropopause as in the right panel.
We will come back to this issue in more detail in Sect. .
Here, neither the pronounced tropopause (at around 17 km) nor the inversion
layer above (i.e., between 20 and 25 km) is well captured by the Butterworth
filter, resulting in unrealistically large temperature perturbations which
might not be confused with real gravity-wave-induced temperature
perturbations. This is a general problem with all techniques that analyze
vertical temperature profiles, which has motivated many authors to exclude
the tropopause region and the lowest altitudes above it from further analyses
see, e.g.,for a detailed discussion and an approach to derive GW
properties in the vicinity of the tropopause. For this reason,
we will exclude altitudes below 20 km from our analysis and focus on the
altitude range between 20 and 40 km only, knowing, of course, that the
largest altitudes need to be treated with care since noise of RO data is
known to pick up significantly above ∼ 35 km altitude
().
Comparison of METOP EP values with ECMWF model data
and ground-based lidar measurements
We next present a systematic comparison of EP values derived from
METOP RO-dry temperatures, the IFS, and ERA-Interim. As an initial
impression, Fig. shows monthly mean
latitude–longitude cross sections of EP at selected altitudes of
30, 33, 36, and 39 km for December 2015. At 30 km, the RO data reveal
pronounced GW activity over Scandinavia, over the Iberian peninsula and north
Africa, and in a band in the vicinity of the Equator, with strongest activity
in the tropical central Pacific (135–180∘ E). Moving to 33 km
altitude, EP values increase with pronounced activity still over
Scandinavia, strong activity at around 40∘N in the Atlantic storm
track region, and an additional activity center over the northern part of
South America. At larger altitudes, these general features remain, but become
smeared out geographically. Generally speaking, this overall morphology of GW
activity is well reproduced by both the IFS and ERA-Interim with some notable
differences. First of all, EP values from the IFS and ERA-Interim
are generally smaller than corresponding RO values, with this discrepancy
increasing with increasing altitude. Secondly, ERA-Interim does not capture
the GW activity over Scandinavia that is clearly seen in the RO data and also
in the IFS data. The latter finding is likely due to the significantly
coarser horizontal resolution (and hence also a coarser resolution of
orography) which keeps the ERA-Interim reanalysis from capturing rather
localized orographic gravity wave activity as that seen over Scandinavia.
Note that we have checked this interpretation by also comparing
EP distributions over the well-known Patagonian GW hot spot for
June 2015. While METOP and IFS data show clear signatures of moderate GW
activity in this region (see Fig. ), ERA-Interim again
misses to reproduce this GW activity (not shown).
For a more detailed comparison, we have next extracted time series of
EP for different altitude bands at selected locations. We have
selected three locations at which we have conducted extended ground-based
Rayleigh lidar observations of EP and are hence in a position to
compare RO data not only with the two different model data sets but also with
the ground-based data. These locations are Lauder, New Zealand
(45∘ S, 169.7∘ E), where ground-based Rayleigh lidar
measurements were conducted from June through December 2014
(); Sodankylä, Finland
(67∘ N, 26∘ E), where observations were taken from
September 2015 until May 2016 (); and finally in
the German Bavarian Forest (48.8∘ N, 13.7∘ E), with
measurements from May until December 2016. Figure shows
time series of monthly mean EP from July 2014 through December
2016 for these locations and for the altitude ranges 15–25, 25–35, and
35–45 km, respectively. Note that we have binned the model data to the same
latitude–longitude grid as the RO data for a proper comparison.
Figure overall reveals a very good fit between RO and
model data: in the altitude range from 15 to 25 km, RO and model data fit
very well in terms of both absolute values and month-to-month variation for
all three locations (Fig. a, d, g). At altitudes between
25 and 35 km (Fig. b, e, h), the agreement is still very
good, but peak RO values are underestimated by both models. This is
particularly pronounced for Sodankylä (left), where local mountain wave
activity is likely causing the strong wintertime EP peak.
Consistent with the results shown in Fig. this
peak is qualitatively well reproduced (but still slightly underestimated) by
the IFS but completely missed by ERA-Interim due to the much coarser
horizontal resolution of the latter. Finally, at the highest altitudes, the
overall seasonal variation of EP that is observed with the RO
sensors is reproduced by the models, but modeled EP values are
smaller than those derived from RO observations by factors between 2 and 3.
This is expected since the sponge layer in the ECMWF models starts strongly
damping any small-scale structures above 10 hPa or ∼ 32 km
() and since RO measurement noise is
picking up substantially above 35 km seeand our analysis in
Fig. and corresponding discussion.
Next, we compare the same RO time series to local EP observations
obtained with Rayleigh lidar (see Fig. ). The
portable lidar systems as well as the data analysis procedure used during the
three campaigns have been described in detail in
. In short, Rayleigh lidar
measurements yield relative density profiles at altitudes where pure
molecular scatter accounts for the signal, i.e., from above the stratospheric
aerosol layer. Hence, data are available for altitudes above ∼ 30 km
(and below ∼ 90 km) but may be extended to lower altitudes after
careful analysis, ensuring that stratospheric aerosol scatter did not
contribute to the signal. Relative density profiles are then converted to
temperatures, applying hydrostatic downward integration. Finally,
EP values are derived in the same manner as for the RO data
described above (see Sect. ).
(a, c, e) Scatter plots between
EP values derived from the IFS and RO-dry data for three
different altitudes, i.e., 22 km (a, b), 28 km (c, d),
and 38 km (e, f). The red line shows a linear fit to the data with
slope b, y intercept a, and correlation coefficient R (see insert).
(b, d, f) Corresponding histograms of the difference between
the two data sets.
The comparison shown in Fig. reveals that lidar
and RO data generally show very similar seasonal variation. However, the
comparison also shows that the local lidar observations yield significantly
larger EP values by up to a factor of ∼ 2. This is likely
because the lidar observations are sensitive to a larger part of the gravity
wave spectrum than the RO observations. As described in
Sect. , the horizontal line of sight of RO observation is
approximately 190–270 km. Hence, depending on the orientation of the wave
vector relative to this line of sight, the RO technique may not resolve waves
with horizontal wavelengths shorter than these 190–270 km (if the phase
fronts are aligned with the line of sight; the RO technique might, however,
be able to detect GWs with shorter horizontal wavelengths than is the case if
the phase fronts are perpendicular to the line of sight; see
and for details). Hence, it is
clear that RO observations are only sensitive to GWs with rather large
horizontal wavelengths whereas lidar observations may also detect much
smaller-scale gravity waves. Note that there is also a (moderate) difference
in vertical resolution, which is 900 m for the lidar temperatures and
∼ 1.4 km for the RO data (). In
addition, we also need to realize that the spatial sampling for both data
sets is very different: while the EP values based on RO data are
typically based on 20–40 single (snapshot) temperature profiles that have
been obtained in a geographical area of 5∘ in latitude and
10∘ in longitude, the lidar data shown here are based on 10–20
nightly means, each consisting of several hours of GW observations (see lower
panels in Fig. ). While it is difficult to assess
the quantitative impact of this very different sampling on the resulting
EP values, it is conceivable that the large geographical area
over which the RO data are obtained might result in a smearing out of local
GW maxima and should hence tend to smaller values compared to local
observations.
(a) Correlation coefficients as a
function of altitude for the correlation between ERA-Interim and IFS data
(black line), RO-wet and IFS data (blue line), and RO-dry and IFS data (red
line). (b) Corresponding median temperature differences (thick
lines) along with 10 and 90 % percentiles (thin lines) as a function of
altitude (same color code as in panel a).
(a, c, e) Latitude–longitude
distributions of EP based on GPS RO-dry data for December 2015
and altitudes of 20, 28, and 38 km (a–fs). (b, d, f) Same
as panels (a, c, e) but based on IFS data. In all panels black contours
show zonal wind values from ERA-Interim.
In all, we conclude from the comparison of time series at the three
considered locations that the fit between GPS RO and IFS and ERA-Interim data
is generally very good whereas comparison to local observations indicates
that RO EP values are low biased – which is likely due to
different observational filters of both techniques see, e.g.,for a
thorough discussion of observational filters of different
techniques. Next, we finally compare GPS RO with
IFS and ERA-Interim data on a global basis. For all 30 months between
July 2014 and December 2016 we have computed EP on a grid of
5∘ in latitude, 10∘ in longitude, and 1 km in the vertical
for the whole considered altitude range of 20–40 km. For each altitude, we
have then analyzed the relation between the two GPS RO data sets and the
model data sets in terms of correlation coefficients as well as in terms of
absolute differences. An initial impression of the statistical relation
between EP values from RO-dry data and from IFS data is presented
in Fig. , which shows corresponding scatter plots
along with a linear regression to the data as well as histograms of the
absolute difference between the two data sets for three selected altitudes.
Figure shows a very large correlation of R=0.94 at
22 km, a minimum value of R=0.45 at 28 km, and a slightly larger value of
R=0.56 again at 38 km altitude. Furthermore, it is common to all three
histograms that IFS values are biased low with respect to the RO data.
Interestingly, though, the distribution is broadest at the lowest considered
altitude with much narrower distributions above. The complete altitude
variation of correlations as well as biases is shown in
Fig. , which shows correlation coefficients and
median differences (along with 10 and 90 % percentiles) between
ERA-Interim and IFS, between RO-dry data and IFS data, and last but not least
between RO-wet data and IFS data. Figure shows
several interesting features. Starting with the correlation coefficients,
those are generally large (between 1.0 and 0.5) except for the altitude range
between 25 and 30 km where the correlation of both RO data products (wet
and dry) with model data show a minimum with values as low as 0.4. Above
30 km, however, correlations coefficients increase again. Besides this
striking minimum between 25 and 30 km, the overall envelope of the altitude
variation shows larger correlation coefficients between 0.9 and 1.0 below
25 km and values between 0.8 (for the correlation between ERA-Interim and
IFS) and 0.5 (for the correlations between the RO-dry data and IFS data) at
40 km. Turning to absolute differences (right panel in
Fig. ), the median differences between ERA-Interim
and IFS data are very small (less than 1 J kg-1) with IFS values being
slightly larger than ERA-Interim values. Concerning the absolute differences
between RO and IFS data, both RO data products yield systematically larger
EP values than the IFS, where, however, the median difference
between the RO-wet data and the IFS data is significantly smaller than the
difference between the more “original” RO-dry data and the IFS data.
Interestingly, both the median difference and its variability
(indicated by the percentiles) is quite large at 20 km and decreases
significantly up to an altitude of 25 km, above which both median differences
and related variability increase again up to the maximum altitudes
considered.
Same as Fig.
but for June 2015.
Zonal mean distribution of N2 as a function of
latitude and altitude for the months June 2015 (a, c) and
December 2015 (b, d) based on GPS RO-dry data (a, b) and
IFS data (c, d).
(a, b): Monthly mean zonal mean
distributions of EP as a function of latitude and altitude for
December 2015 based on RO-dry data (a) and IFS data (b).
(c, d) Zonal mean apparent EP values derived from
applying the EP algorithm to monthly mean zonal mean temperature
profiles. (e, f) Difference between (a, b) and (c, d).
(a) Monthly mean zonal
mean distribution of EP from IFS data derived after detrending in
the horizontal with T42 IFS fields. (b) Monthly mean zonal mean
distribution of VE =12w2.
Discussion
In order to identify the reason for the reduced correlation between RO and
IFS data between 25 and 30 km as well as the relatively large bias below
∼ 23 km, we next consider a comparison of latitude–longitude
distributions of EP values at selected altitudes based on RO-dry
data and IFS data. Corresponding results for December 2015 and June 2015 are
presented in Figs. and ,
respectively. We start with a discussion of the relatively low correlation
coefficients at altitudes between 25 and 30 km. Inspection of
Figs. and reveals that
the likely reason for this is that apparently the IFS is hardly simulating
any gravity wave activity at the altitude levels of lowest correlation
whereas the observations do show some weak but clearly detectable GW
activity. The reason why the IFS does not simulate any (very weak) GW
activity in the considered vertical wavelength range at these altitudes is
not clear at this point but is consistent for all months considered in this
study and should be further investigated in the future. As for the bias at
altitudes below 25 km, the EP distributions shown at 20 and
22 km show that the strongest (apparent) GW activity is here observed in a
band of ±20∘ around the Equator with significantly larger values
seen in RO data than in IFS data. This is, however, the region of the
tropical tropopause and its related TTIL. Note that it is on purpose that we
refer to the tropical tropopause inversion layer as TTIL instead of the more
commonly known TIL, since the latter term has usually only been used for the
midlatitude TIL and not the tropical one that we are dealing with here
(). That this is indeed
the case for the here considered data set is demonstrated in
Fig. , which shows zonal mean N2 values based on RO and IFS
data. Note that the N2 values in Fig. were computed from
monthly mean zonal mean temperatures that must not be confused with the N2
values used in our EP calculation, which is based on T0
profiles. Remember that T0 profiles result from filtering individual
temperature profiles with a fifth-order Butterworth filter with cutoff
wavelength at 15 km such that T0 profiles only contain spatial scales
larger than 15 km and hence do not contain information on the TTIL.
Figure clearly shows that it is indeed the latitude and altitude
range of the TTIL which coincides with corresponding regions of large
EP values in the considered data sets. In addition,
Fig. also shows that the TTIL is more pronounced in the RO data
than in the ERA-Interim data. Hence, it is tempting to speculate that the
large EP values seen in the tropics and the corresponding large
differences between the RO data and the IFS data is because our algorithm to
derive EP values from temperature profiles by means of separating
background temperatures from gravity-wave-induced disturbances fails in this
altitude and latitude region. In order to test this idea further, we present
zonal mean EP values as a function of latitude and altitude
between 20 and 40 km altitude based on both RO-dry data and IFS data in
Fig. . This figure clearly shows the region of large
EP values between 20 and 25 km altitude and at latitudes between
-20 and +20∘. It also shows that RO values in this region are
significantly larger than in the IFS data set. In order to test whether these
are indeed real indications of gravity wave activity or rather artifacts due
to the TTIL we have next applied our algorithm to derive
EP values to monthly mean zonal mean temperature profiles. For
those, it can safely be assumed that they do not contain any remaining
gravity wave signatures (since many profiles have been averaged) such that
any significant nonzero EP values must be artifacts due to
shortcomings of the algorithm. The result of this exercise is shown in the
middle panels of Fig. . Quite obviously this analysis
yields regions of very large apparent EP values in regions of the
TTIL. Compared to the panels in the upper row of the figure, it is also clear
that these artifacts actually dominate the EP values in the
TTIL region. In addition, we note that additional artifacts are observed at
higher altitudes and also in other latitude and altitude regions. These may
be caused by tropical Kelvin waves or other planetary-scale features such as
inertial instability e.g.,. However, for these,
their absolute values are significantly less than in the data sets in the
upper row such that the contribution of these artifacts to the overall
EP values is not significant. This is also clearly seen in the
lowermost panels of Fig. , which show the difference of
the full EP distribution (in the top row) and the contributions
from the monthly mean zonal mean profiles (in the middle). In these
“corrected” EP distributions, the maximum values in the
tropical TIL region have basically disappeared, whereas there is hardly any
change visible at other altitude and latitude regions. Coming back to
panel b of Fig. we hence conclude that the
relatively large differences seen below 25 km do not reflect real
differences in terms of gravity wave activity in RO data and model data.
Instead, the differences are caused by differences in the representation of the
TTIL and the difficulty to properly derive EP values in its
environment from vertical profiles alone.
We finally attempt to determine the quality of the corrected EP
values in Fig. by comparing them to EP
values using a horizontal background determination method. Horizontal
estimation of T0 was previously found to be superior to a vertical
background determination by and .
While the sampling statistics of the METOP RO data on a daily basis (i.e.,
only 1100 profiles distributed over the whole globe) is too poor to allow us
to apply a horizontal background determination to them we may easily perform
a corresponding analysis of the high-resolution IFS data. For this purpose
the spectral model output of the IFS for December 2015 has been reconstructed
at T42, i.e., at a horizontal grid spacing of 500 km. These fields have then
been used as background temperatures T0(z,λ,ϕ), where λ
is latitude and ϕ is longitude, in order to compute monthly mean zonal
mean distributions of EP. Such monthly mean zonal mean
EP distributions for December 2015 are presented in
Fig. . In the same figure we also show
corresponding fields of the vertical kinetic energy,
VE =12w2. Note that VE is a good
indicator of gravity waves in the stratosphere since vertical velocities due
to other air motions are significantly smaller. While VE values are
significantly smaller than EP values (by about a factor of 1000
in the IFS model) it is still instructive to compare the spatial morphology
of the corresponding fields. This comparison clearly reveals that the
proposed correction of EP distributions derived using a vertical
background determination (see Fig. 14 and related text) improves the
comparison between EP and VE but that it cannot eliminate all
features that are apparently not due to gravity waves. Closer inspection of
the data sets reveals that this is partly because some of the non-gravity
wave structures (mainly the TTIL) are not zonally homogeneous such that
correcting for them using zonal mean fields cannot eliminate the non-gravity
wave structures completely. We hence conclude that this correction may be
recommended for application to data sets that can only be analyzed using a
vertical background determination method such as for the METOP data with
relatively scarce sampling statistics. However, even after this correction,
regions within ±30∘ latitude around the Equator need to be
considered with care due to additional potential contamination of
EP by Kelvin waves or other planetary-scale features. In any
case, if the sampling statistics allows, our analysis clearly shows that in
general a horizontal background determination is advantageous in that it
better avoids contributions to EP that are not caused by gravity
waves.
Summary and conclusions
In this paper we compared operational METOP GPS RO temperatures and
derived gravity wave potential energy densities with corresponding ECMWF
operational analysis and ERA-Interim reanalysis data sets. This was done to
answer two questions: firstly whether the sampling and data quality of the
operational RO data set is sufficient to properly characterize the global
gravity wave activity (measured in terms of EP) on a monthly
basis and, secondly, whether the METOP observations are
consistent with the ECMWF model fields such that the latter can be used for
the interpretation of observational results.
For this purpose, we analyzed a total of 30 months of RO data for the period
from July 2014 to December 2016. We calculated monthly mean temperatures and
EP values on a grid of 5∘ in latitude, 10∘ in
longitude, and at a vertical resolution of 1 km for altitudes between 20 and
40 km. This was done for two RO data sets, namely for so-called “dry” and
“wet” data both provided by EUMETSAT's ROM SAF. Dry temperatures are
directly derived from refractivity profiles which in turn are estimated from
bending angle observations with the GPS RO technique. In contrast, wet
temperatures are the result of a one-dimensional variational retrieval that
uses additional a priori information on atmospheric humidity and temperature
from ECMWF model fields. Subsequently both temperatures and
EP values from RO observations and from ECMWF analysis and
reanalysis model fields were compared rigorously.
The comparison of temperatures showed very low systematic differences between
RO-dry temperatures and ECMWF model fields between 20 and 30 km (i.e.,
median temperature differences between -0.2 and +0.3 K), which then
increased with height to yield median differences of +1.0 K at 34 km and
+2.2 K at the maximum considered altitude of 40 km. Compared to this,
median differences between RO-wet temperatures and ECMWF model data were
below 0.16 K for all considered altitudes, which is as expected since ECMWF
model data were used to constrain the RO data retrieval.
We then introduced a method to derive EP from temperature
profiles by applying a fifth-order Butterworth filter with cutoff wavelength
of 15 km to both RO and model data. An initial comparison of
EP time series in selected altitude ranges and at three selected
locations in Sodankylä, northern Scandinavia, in the German Bavarian
Forest, and in Lauder, New Zealand, yielded overall very good agreement:
below 35 km, this agreement was both very good in terms of seasonal
variation and in terms of absolute EP values. A striking result,
however, was that for northern Scandinavia – which is known as a region of
strong orographic wave activity – the horizontally coarser-resolved
ERA-Interim data underestimated a large winter peak of EP that
was present in both the RO data and the higher-resolution IFS data.
At altitudes above 35 km, however, both models did follow the observed
seasonal variation of EP qualitatively but underestimated the
observed values by about a factor of 2. This is likely caused by the
damping of small-scale model structures by the model's sponge layer. Also, it
is well known that noise in RO data picks up substantially above 35 km such
that several previous studies have recommended restricting the useful range
of RO data for GW analysis to below 35 km e.g.,. This
previous recommendation is clearly supported by our analysis.
The same EP time series from RO observations were then also
compared to local Rayleigh lidar observations. This comparison showed a
qualitatively similar seasonal EP variation with both
experimental techniques but it also revealed that the RO technique
underestimates the locally observed values by about a factor of 2. This low
bias is likely caused by the very different observational filter of RO and
lidar observations where in particular the long line of sight of
RO observations that are carried out in limb geometry severely hampers the
detection of waves with horizontal wavelengths smaller than 190–270 km
while the lidar observations are also sensitive to much smaller horizontal
wavelengths.
Finally we compared the full 30-month data set of RO and model
EP fields. The corresponding statistical analysis shows large
correlation coefficients (0.4–1.0) between all considered data sets (RO-dry,
RO-wet, ERA-Interim, and IFS) for all altitudes between 20 and 40 km. A
minimum correlation (of still 0.4) was found at altitudes around 28 km,
where the ECMWF analysis and reanalysis fields do not seem to capture the
GW activity that is observed in the RO data. The reason for this discrepancy
could not be identified and should be investigated in a future study.
Concerning absolute differences between observed and modeled
EP values, the median difference was relatively small at all
altitudes with an exceptional feature between 20 and 25 km where both the
median difference between RO and model data increased and where the
corresponding variability was also found to be very large. The reason for this was
identified as an artifact in the EP algorithm: this erroneously
interprets the pronounced climatological feature of the TTIL at latitudes
between ±20∘ and altitudes between 20 and 25 km as gravity wave
activity, hence yielding (a) very large EP values in this area and
(b) large differences between model and observations because the RO data
show a much more pronounced TTIL than IFS and ERA-Interim. Based on that
finding we also suggested a correction for this effect based on an estimate
of this “artificial” EP using monthly mean zonal mean
temperature profiles which do reveal a very pronounced TTIL but which should
not contain any remaining GW signatures due to strong averaging. In addition,
this technique to derive and correct EP based on vertical
profiles was compared to an alternative method applying a horizontal
background temperature determination method to IFS data. We find that the
above-introduced correction may be recommended for application to data sets
that can only be analyzed using a vertical background determination method
such as the METOP data with relatively scarce sampling statistics. However,
if the sampling statistics allows, our analysis also shows that in general a
horizontal background determination is advantageous in that it better avoids
contributions to EP that are not caused by gravity waves like the
TTIL and potentially also Kelvin waves and other planetary-scale features
with short vertical wavelengths (i.e., less than 15 km).
In summary, our analysis shows good quantitative agreement between monthly
mean RO-dry and ERA-Interim and IFS data in the altitude range between
20 and 40 km altitude. Hence, both research questions posed at the beginning of
this study can be answered positively: for one, this good agreement shows
that METOP RO-dry data are a suitable database to study monthly mean global
gravity wave activity in the altitude range between 20 and 40 km (with the
caveat that the tropical latitudes need to be considered with particular
care). In addition, the good agrement between RO-dry and ECMWF data also
implies that the combination of both appears to be a versatile combined data
set for the study of processes determining the GW climatology. Future
questions to be considered are, for example, how far the strong
stratospheric jet streams influence the observed GW morphology in the
stratosphere. While model results of and more recently
also and have long suggested that the waves
should be refracted into the jet streams, observational evidence for this
process based on global data is still scarce. This and other research
questions will be investigated in future studies.
RO data are available from www.romsaf.org. Details
regarding access to ECMWF data can be found under www.ecmwf.int.
MR devised the study, wrote the data analysis code,
performed the analysis, and drafted the first manuscript version. AD provided
the ECMWF data and helped with its handling and analysis. BK provided the
lidar data and contributed to its analysis. All authors contributed to
writing the text.
The authors declare that they have no competing interests.
Acknowledgements
GPS RO data provision by EUMETSAT's ROM SAF is greatly appreciated. Access to
the ECMWF data was possible through the special project “HALO Mission
Support System”. Part of this research was conducted within the scope of the
German research initiative “Role of the Middle Atmosphere in Climate
(ROMIC)” under grant 01LG1206A provided by the German Ministry for Education
and Research. Partial funding was also provided by the German Science
Foundation (DFG) via the research unit MS-GWaves (GW-TP/DO 1020/9-1, PACOG/RA
1400/6-1). Markus Rapp would also like to thank J. Wickert for pointing his
attention to METOP RO data and W. Beer for his efficient handling of file
downloads from the ROM SAF. Markus Rapp also thanks P. Preusse and M. Ern for
valuable comments on early results achieved within this study and Natalie
Kaifler for critically reading and commenting on the text.The
article processing charges for this open-access publication
were covered by a Research Centre of the Helmholtz
Association. Edited by: Jörg Gumbel Reviewed by: two
anonymous referees
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