Dear Prof. Klees,
we would like to thank all the reviewers for their comments and suggestions which helped to
improve the paper significantly. We uploaded the revised version of our manuscript together with
the newly added supplementary material to be considered for a possible publication in Journal of
Geodesy.
Yours sincerely, Kamil Teke
Dear Dr. Teke,
Reviewers have now commented on your paper. All 4 reviewers are very positive about the
manuscript. They made a number of suggestions for improvements, which should be addressed
when revising the manuscript. I noticed 3 remarks, which I would like to bring to your attention:
1. You may consider moving the theory about atmospheric delay to a separate section
(reviewer # 3).
 Done. The theory about atmospheric delay has been moved to a separate section
after the introduction.
2. The description and the interpretation of the ZTD and atm. gradients could be extended
(reviewer # 3).
 In addition to Figure 4, a new plot (Figure 5) was added to the manuscript showing
the dependence of the ZWD agrement between GNSS and VLBI on mean ZWD (e.g.
humidity).
 The interpretation of the ZTD was extended. The following points are picked out and
the bold italic text is added to the paper:
 First paragraph after Figure 3: The standard deviations between GNSS and
VLBI ZTD series at Ny-Ålesund (NYA1) are similar for all CONT campaigns and
smaller than 4 mm. This is due to the low humidity at Ny-Ålesund where all
mean ZWD are below 8 cm during CONT campaigns (see Figure 4 and 5).
 First paragraph after Figure 3: At Onsala (ONSA) and Wettzell (WTZR) the
standard deviations are 4.2 to 5.4 mm, whereas at Tsukuba (TSKB) and Kokee
Park (KOKB) the standard deviations are larger than 8 mm. This is due to the
higher humidity at Tsukuba and Kokee Park compared to Ny-Ålesund,
Onsala and Wettzell(see Figure 5).
3. You may consider to provide tabular values for some plots as ESM (reviewer # 1).
 Tabular values for all plots are provided as Matlab files (*.mat).
Moreover, I expect that everything from page 34 onwards is provided as ESM. Please confirm.
 Yes, we confirm. Everything from page 34 onwards is provided as ESM.
Reviewers' comments:
Reviewer #1: Very well written and thorough paper and I believe should be published in its current
form. My only minor suggestion is the dash-dot line used for WVR is a little difficult to see, maybe
that could be re-considered.
 The dash-dot line used for showing the WVR derived ZTD series were changed to
solid line for all plots in the supplementary material.
1
Are tabular values for the plotted values to be made available? This could be useful for others to
explore the results and would have a small impact on the size of the supplemental material. For
the Figure 3 in the main paper, the addition of another frame that shows the residuals of each
technique to the mean of all techniques might show features that are difficult to see in the totals
shown in the figure.
 All tabular values of ZTD and troposphere north and east gradients for CONT
campaigns are provided as Matlab, (*.mat) files. (Please, consider that the submitted
ZTD are not corrected for the troposphere ties between techniques.)
 We agree that a plot with the residuals would be interesting; however, we don’t
want to have too many plots in the paper but the reader should rather refer to the
supplemental material and the Matlab files.
Reviewer #2: This is a competent discussion of an important series of experiments. There are no
great surprises, but that is not a criticism. I recommend it be published. I can recommend some
small changes, but I would not insist on them.
When discussing ZD predictions derived from NWMs, it is useful to discuss how the analysts
addressed the problem of the difference between the model terrain (DEM) used by the NWM
versus actual terrain. If the (smoothed) model terrain lies under the actual terrain (at a given
geodetic station location) then it is possible to interpolate the NWM fields so as to estimate
pressure, ZD, gradients, etc. at the GPS stn. If the model terrain lies above the actual terrain, and
the geodetic station lies 'underground' according to the NWM, then one could extrapolate instead
- but this is significantly more problematic. But some groups, historically, have made no attempt to
account for the model surface being vertically offset from the actual ground surface at the GPS
station. So they have equated NWM's "surface pressure" prediction (i.e on the model terrain) with
pressure on the real-world ground surface, etc. In this case, the lower the horizontal resolution of
the NWM, the worse that 'terrain bias' effects tend to be. I think the authors might wish to clarify
this particular issue for the case of their study.
 We fully agree with the comments above. In case of the ECMWF data, we use
pressure level data. Thus we don’t have to deal with surface pressure fields or
surface model levels, but the inter- and extrapolation is straightforward and depends
on whether the site height is above or below the lowest (1000 hPa) pressure level.
This information was added to the manuscript.
I think the discussion could be made more interesting if you pointed out that geodesists should
distinguish between NWMs that have assimilated large amounts of geodetic (i.e. GPS) delay
estimates via 3DVAR or 4DVAR, and those that have not. Almost certainly assimilating zenith delay
time series at many points using state of the art assimilation methods (along with co-located
pressure measurements where available) would lead to very significant improvements in the
NWM's humidity fields. This, in turn, would probably improve the models ability to predict delay
anisotropy (e.g. the delay gradient parameters). Sadly, state of the art variational assimilation
methods are rarely used (i.e. to assimilate geodetic delay measurements) outside of Europe, and
not often used in Europe by groups other than the ECMWF. Even so, this is a very exciting prospect
for future research.
 This is an interesting point. We added corresponding sentences to the Conclusions.
Congratulations on a very solid piece of work.
Michael Bevis
Reviewer #3: The authors' interest and motivation is very important and the results described in
the manuscript are useful for all researchers who are interested in the atmospheric propagation
2
delay issue on the space geodesy. The authors demonstrate full details of comparison results of the
zenith total delay and gradient due to the neutral atmosphere in the four CONT VLBI campaigns.
Though similar studies have been published so far, it is the first paper to investigate variations of
atmospheric effects on VLBI and GNSS including analyses of numerical weather prediction data.
Thus the manuscript well presents originality and gives useful information to readers. However,
the present structure of the manuscript spoils such usefulness unfortunately. I recommend the
authors reconsider a whole structure of the manuscript.
#Structure of the manuscript
As already mentioned above, though the author's motivation and aim shown in the manuscript is
very important, the present structure of the manuscript have to be improved. For example, the
"Introduction" is verbose. It contains not only author's motivation, but also a theoretical
description of the atmosphere delay.
 We moved the theoretical description of the atmosphere delay to a separate section.
Next, descriptions of implications based on the ZTD and atmospheric gradient results are
insufficient in the "Data analysis" section. For example, though the authors mention "the ZTD
differences are large or small", "the gradient values are large or small", and "the standard
deviation is smaller", the authors don't sufficiently describe the cause of such large differences.
Since the author use the numerical weather prediction data sets, they can investigate what
meteorological phenomena occured during the CONT campaigns.
 We agree. But the discussion of meteorological phenomena would be out of the
scope of the paper and might be too speculative. We believe that Figure 4 nicely
shows the relation between atmospheric condition (mean value and variability of the
humidity) and the agreement in the troposphere parameters. In addition to Figure 4
we have added a correlation plot (new Figure 5) showing the dependence of the
agreement of ZTD between GNSS and VLBI on mean ZWD (amount of humidity). We
used that information for further discussions in the text.
Discussion and summary are intermingled in the "Conclusions" section.
 Yes, we agree. But it would be difficult to change that because each conclusion
requires the summary of a particular result.
Finally, a supplementary material is too huge!! Are the huge amount of materials really required ?
When some contents of the supplementary material are cited in the main body, it is very hard to
find them from the huge materials.
 We agree that the supplemental material is extremely long. But with the final version
of the paper published in Journal of Geodesy, the reader will not be confronted with
the auxiliary material, but will only get it upon request.
#The term of "troposphere delay"
The authors should concern a correct usage of "troposphere delay". Though I think the authors
well understand the usage, descriptions in the manuscript are sometimes confused. The
troposphere is the lowest portion of earth's atmosphere between the earth's surface and the
tropopause, which is characterized by decreasing temperature with increasing altitude. The
tropopause ranges in height from an average of 9 km at the poles, to 17 km at the equator. Thus,
the "troposphere delay" is only about 70% of the total delay due to the neutral atmosphere.
 We are well aware that the troposphere is only one part of the neutral atmosphere.
However, since in the geodetic literature the term “tropospheric delay” is commonly
used as a name for the delay in the neutral atmosphere, we also use this term in our
paper. However, we clarified this in the revised manuscript.
3
<<miscellaneous>>
# a typo in Page 6.
2.1 Space geodetic solutions
2.1.1 Very Long Baseline Interferometry (VLBI) "IRCF2" -> "ICRF2"
 Corrected.
# Page 7, line 10th
"Most radiometers were operated in the so called sky mapping mode, meaning that the WVR was
moving around making measurements in several different directions."
Q: What azimuth and elevation angles are observed by WVR?
 Many different azimuth and elevation angles were observed (for example, at Onsala
over 50 different directions were used). The exact azimuth and elevation angles
observed varied between the different radiometers, and also varied slightly between
the CONT campaigns. Thus we chose not to explicitly add all different directions
observed to the paper; we have only added the text “covering the whole sky (above
20° elevation angle) quite well”.
#Page 8
"As analysis models are generated every three hours and the horizontal resolution is
approximately 10 km (5 km after March 2010), the MANAL data sets are a suitable choice for
modelling atmospheric path delays ..."
-> The spatial resolution of the MANAL data was updated on Apr. 7th of 2009 (10 km -> 5 km).
 The grid spacing of the JMA MANAL data-sets changed on Apr. 7th, 2009, providing a
5 km spacing instead of the 10 km grid which has been used before. The
corresponding sentence in the manuscript has been changed as:
As analysis models are generated every three hours and the horizontal grid spacing is
approximately 10 km (changed on April 7, 2009, providing a 5 km spacing instead of
the 10 km grid), the MANAL data sets are a suitable choice for modelling atmospheric
path delays in the East Asia ...
#Page 12, line 22
"During the last three CONT campaigns, large negative biases between GNSS (TSKB) and the WVR
at Tsukuba ..."
Q: What cause is the large biases between WVR-based ZTD and other ZTDs as shown in a time
series plot of the ZTD at TSUKUBA. It looks the WVR data at TSUKUBA were well not calibrated.
Were the WVR at each site calibrated prior to or after the each CONT campaign?
 It is likely a calibration error. First of all no tip-curve calibration was performed since
the radiometer only measured in zenith. Furthermore, there could be an error in the
conversion factor between brightness temperature and wet delay. We did not derive
any specific conversion factors for the CONT campaigns, instead some typical values
were used. A comment about this was added at the end of the paragraph.
#Page 13, line 28-30 and Page 14, line 34 "Although the standard deviations between troposphere
gradients from the different techniques at Tsukuba during CONT11 are rather larger (on the order
of 0.6 to 0.9 mm), most of the correlations are strong at about 0.7."
and
4
"The worst agreement of gradients between GNSS and VLBI is found at Zelenchukskaya, Kokee
Park (KOKB) and Tsukuba (TSKB)."
 The above sentence is changed to "The worst agreement (largest standard
deviations) of gradients between GNSS and VLBI is found at Zelenchukskaya, Kokee
Park (KOKB) and Tsukuba (TSKB)."
Q: It looks the first sentence is inconsistent with the second one. In the first sentence, the author
mentions the tropospheric gradients from the each techniques at Tsukuba have strong correlation.
On the other hand, in the second one the author mentions the worst agreement of gradients
between GNSS and VLBI at Tsukuba.
 Even if the correlations are strong between ZTD estimates from different techniques,
the standard deviations of the ZTD differences can be large (the agreement might be
worse) like what is seen at Tsukuba. We have added to the paper that the largest
standard deviation is interpreted as the worst agreement and the smallest standard
deviation is interpreted as the best agreement (ignoring the biases).
The reader can grasp that the first sentence is correct based on the results as shown in Figure 6.
Though an amplitude of GNSS gradient is much larger than that of VLBI gradient, phases of both
time series seem well consistent. What's the basis for the second sentence?
 The basis of the second sentence are the standard deviations of the ZTD differences
between GNSS and VLBI at Tsukuba during CONT which are plotted in Figue 4. Please,
see also the biases and standard deviations which are tabulated in the
supplementary material between all co-located techniques at Tsukuba.
Reviewer #4
Reviewers comments on Teke et al: Troposphere delay from radio space geodetic techniques,
WVR, etc.
General comment:
This paper does a nice job of documenting the level of agreement of measurements of troposphere
parameters for the best of the CONT series. Although there are no surprises, it is gratifying to see
the good agreement between VLBI and GPS. The paper is generally well written, although I have a
number of grammatical corrections to recommend, which I will mail to the editor.
 Thanks a lot for the annotated manuscript. We applied those corrections.
Specific comments:
1. p. 3 third paragraph:
Why are results given for some papers and not others?
 There are many papers where ZTD estimated from different techniques are
compared. It is not possible to mention all of them in our paper, thus we selected
only a few that we think are the most relevant ones to our study.
How were these papers chosen?
 We have chosen these papers since they especially focused on inter-technique
comparisons of ZTD.
2. p. 6:
VLBI and DORIS are not elevation down-weighted, but GNSS is. What is the effect?
 Yes, VLBI and DORIS are not elevation down-weighted, but GNSS is. Down-weighting
of low elevation angle GNSS observations is needed due to multipath etc. VLBI, for
example, does not have multipath problems thus down-weighting is not needed. Not
5
applying down-weighting to GNSS might make the GNSS results worse, and applying
it to VLBI (using the same model as for GNSS) might degrade the VLBI accuracy, thus
we did not want to do neither of these in order to keep the highest accuracy of both
techniques. We have not investigated the magnitude of the effect of down-weighting
GNSS observations on the ZWD estimates of GNSS.
Is there a way to determine the magnitude of the effect?
 Yes, there should be a way of determining the magnitude of the effect of downweighting observations on ZWD estimates. However, our aim in this paper is to focus
only on comparing the ZTD and troposphere gradients, estimated from the state-ofart observations and state-of-the-art models of the techniques, by means of
optimizing parameterization of the analyses exclusively for each technique.
Has the magnitude of the effect been determined for the CONT sites by someone else’s analysis?
 To our knowledge there has not been any study focusing on the effect of elevationdependent down-weighting for GNSS focusing on the CONT campaigns.
3. p. 7:
For WVRs the weakest link in determining delays is the set of regression coefficients used to
convert brightness temperature to delay. What data were used for determining the coefficients for
each instrument?
 We did not analyse the raw WVR observations ourselves, but we obtained the final
estimates, i.e. the wet delays, directly from the sites. Thus the exact analysis
strategies applied and the way the conversion coefficient are determined varied
between the different radiometers. At Tsukuba, for example, the coefficients were
obtained by fitting the observed brightness temperatures to the wet delays
measured by nearby radiosondes. At Onsala the coefficients derived by Jarlemark
(1997) were used, i.e. calculating both brightness temperatures and wet delays from
radiosonde data and using these to estimate the coefficients.
Sect 2.3.2 CReSS: I think the authors are confusing resolution with grid spacing. The resolution of
most NWMs is 3-4 times the grid spacing. Please specify which is being referred to here and for the
rest of the NWM descriptions.
 We now use grid spacing instead of spatial resolution throughout the paper.
4. p. 9 Sect 3.1:
Some detail is needed to describe the calculation of the tropo tie, not just a reference to another
paper.
 The corresponding part of the Section 4.1 has changed as follows:
We define troposphere hydrostatic and wet ties ( ZHD and ZWD ) as the corrections to
ZHD and ZWD estimates of a technique at an estimation epoch due to the differential delay
between the technique’s antenna reference point and the reference height at a co-located
site. In Table 5 the height differences and mean troposphere ties for CONT11 for the GNSS,
DORIS, and WVR stations w.r.t. VLBI are shown. For this study we computed troposphere
ZHD and ZWD from the analytical equations of Brunner and Rüeger (1992) based on
the height differences and 6 hourly ECMWF data of water vapor pressure, total pressure, and
temperature (Teke et al. 2011) as shown in Equations (3) to (5)
6
g
  ( H  H 0 )   RL
p  p0 1 
 ,
T0


ZHD 
ZWD 
(3)
0.0022768( p  p0 )
,
1  0.00266 cos(2 0 )  0.28 106 H 0
(4)

2.789 e0  5383
 0.7803   ( H  H 0 ),

2
T0
 T0

(5)
H 0 denotes the VLBI antenna reference point height. The parameters e0 , p0 , and T0 are the
water vapor pressure, total pressure, and temperature at the reference height, and they are
derived from data of the ECMWF; H and p are the height and total pressure at the colocated site,  denotes the average temperature lapse rate, g is the gravity at the site, and
RL the specific gas constant. All the meteorological quantities mentioned above were
interpolated to the ZTD estimation epochs. Then, time dependent (epoch-wise) troposphere
ties were calculated and reduced from each ZTD estimate before comparisons. In the case of
WVR, only wet troposphere ties...
Please confirm if my understanding is correct: The ‘tie’ is the correction of the zenith total delay
(ZTD) at each epoch to give a ZTD at the height of the reference mark that is common to all of the
instruments at a collocated site. When this is done correctly for each technique to the common
reference mark, the difference of the ZTDs should be zero.
 Yes, you are right. Troposphere ties are the corrections introduced to the estimated
ZTD at each epoch due to the height differences between two antennas co-located at
a site. Here, we selected the VLBI antenna reference point height as reference height
for each co-located site and get all the techniques’ ZTD at that height. Then, as you
have already mentioned the differences of the ZTD from different space geodetic
techniques which use microwave frequencies in principle should be zero if everything
from observations to models are perfect.
So why is this called a tie? Am I missing something? (Don’t spend a lot of time on an answer if ‘tie’
is a customarily used term.)
 We need “troposphere ties” to form constraints (similar to the usage of local ties) in
order to combine ZTD from different space geodetic techniques at predefined
epochs. Because, in the future ZTD will probably be combined across different
techniques and troposphere ties will be needed for each co-located site and each
epoch of ZTD estimate.
5. p. 10:
Table 6: What does “which are optimized” refer to?
 “which are optimized” refers to “the parameterization of the analyses of the space
geodetic techniques for the troposphere estimates”. The caption of Table 6 is
changed to:
Table 6 Optimized parameterization of the analyses of the space geodetic techniques
for the troposphere estimates in the second, third, and fourth columns and the types
and intervals of the troposphere data available for the comparisons.
7

In order to estimate ZWD as accurately as possible from the observations of a space
geodetic technique an exclusive parameterization to the corresponding technique
should be selected. For this reason the term “optimized” is used.
Table 8 and 9: Standard deviation values should not be written as ±. That is the sign for uncertainty
or error. Instead, std dev could be placed in parentheses or just in a separate column with headers.
(The difference is that standard deviation doesn’t change with the size of the sample but the
uncertainty should decrease with increasing sample size (in general, assuming Gaussian statistics),
although I’m sure the authors know this.)
 The standard deviation values in Table 8 and 9 have been re-written according to
your comment as you have examplified in the annotated text of the paper, e.g. 1.4 (4.2) (0.98), where the standard deviation is written in the first paranthesis and
the correlation coefficient is written in the second paranthesis. This correction has
also been implemented in the text of the paper.
6. p. 11: Paragraph above caption for Figure 4, which begins “At Westford …”:
Although the biases for Westford are the largest of the GNSS-VLBI values in the table, the scatter
among the different CONTs is the smallest except for Onsala. Therefore the difference is likely to
be a systematic error, such as the phase center offset of the GPS antenna.
 Yes, except for Ny-Alesund, Wettzell, and Onsala the Westford standard deviations
(GNSS-VLBI) are smallest for the all CONT campaigns. These large positive biases of
the ZTD differences between GNSS and VLBI for all CONT campaigns at Westford as
you mentioned might be the result from a systematic error such as phase center
offset of the GNSS antenna.
An elevation cutoff test done for the WES2 GPS antenna (which is mounted on a 10m tower)
before the advent of satellite antenna corrections showed that there was a 2.5 cm change in
apparent height going from 15º to 5º, which would correspond to an approximately 10 mm change
in ZTD. (See Niell et al 2001 Figs 7 and 9.) The point is that there is a possible source of bias in the
GPS data that would be common to all CONTs, and it is not possible to know which minimum
elevation angle gives the more correct values.
This is an area where the paper could be improved by investigating whether the elevation cutoff
angle sensitivity illustrated in Niell et al is still present. This is also a crucial point for the accuracy
of ZTD by GNSS (and possibly by DORIS).
 Yes, you are right. However, we think that this is out of the scope of this paper. We
have added a comment refering to the Niell et al (2001) paper in the Conclusions.
Paragraph following Figure 4 caption, which begins “At most of the sites …”:
The statement is made here and in the introduction that the amount of humidity (or ZWD) is
related to the standard deviations of the differences. This needs to be demonstrated convincingly,
perhaps by plotting standard deviation against average ZWD or ZTD for all days of all CONTS for all
sites. If it can’t be demonstrated, then it should not be claimed (even though we all know it is
(probably) true!)
 The standard deviations of the ZTD differences and biases between GNSS and VLBI
for all CONT campaigns and for all sites accompanying with the mean ZWD are shown
in Figure 4. A new correlation plot was added in to the manuscript (Figure 5) showing
the dependence of the standard deviations of the ZTD differences between GNSS and
VLBI on mean ZWD (amount of humidity) for all CONT campaigns and for all colocated sites.
8
7. p. 11-12:
Explain that “best” is to be interpreted as the smallest standard deviation, not the smallest bias.
 We added this to the abstract:
The best ZTD agreement, interpreted as the smallest standard deviation, was found
between GNSS and VLBI techniques with about 5 to 6 millimeters standard deviation
at most of the co-located sites and CONT campaigns.
and the first sentence of the second paragraph after Figure 3 was changed to:
The best ZTD agreement with the smallest standard deviation was found for
Ny-Ålesund (NYA1) during CONT05 between GNSS and VLBI of 3 mm and between
GNSS and ECMWF of 4 mm...
For this section, pick out two or three important points instead of just giving a series of numbers
(good advice for other sections as well). e.g.
1. agreement on NyAlesund with Teke et al and with Bock et al
 The following points are picked out and the bold italic text is added to the paper:
Last paragraph of page 10: The standard deviations between GNSS and VLBI ZTD
series at Ny-Ålesund (NYA1) are similar for all CONT campaigns and smaller than
4 mm. This is due to the lowest humidity at Ny-Ålesund where all mean ZWD are
below 8 cm during CONT campaigns (see Figure 4).
2. Change of Doris antenna at Kokee Park.
 The following conlcusion has already been drawn and written in the first paragraph
of page 12:
At Kokee Park, the standard deviations between GNSS and DORIS (kokb) and between
VLBI and DORIS are reduced from 44.7 and 41.1 mm (CONT02, koka) to 8.8 and
13.5 mm (CONT05, kolb). This is most likely due to the change of the DORIS beacon at
this site from koka to kolb. The antenna koka was a first generation DORIS beacon
and not as accurate as the modern beacons.
3. Exceptionally large std dev for Hart and Kokee for GPS-DORIS
7. p. 12: Paragraph above Table 8 caption that begins “The standard deviations between …”
GNSS-WVR last line: the speculation about the difference being due to pressure error could be
ruled out by comparison of the values from VLBI used for pressure with the values from ECMWF,
MANAL, and CReSS.
 We have removed the point about the pressure error.
8. p. 15: Conclusions
I would not call replacing a bad DORIS antenna at Kokee Park an “improvement with time”.
 This part was deleted in the conclusions.
Also, although for MANAL the std dev is smaller for the later CONT, it is a stretch to interpret the
change as an improvement with time.
 This sentence was deleted.
9. p. 16: Conclusions: Paragraph that begins “The biases of ZTD …”
I would not call replacing a bad DORIS antenna at Kokee Park an “improvement with time”.
 This was deleted.
10. Somewhere point out that the agreement within the space geodetic techniques is much better
than with the NWMs.
9

The following sentence was added to the second paragraph after Figure 6 in page 14:
“We found that the agreement within the space geodetic techniques is significantly
better than with the NWM.”
Recommended addition to the paper
Include information on conversion of WVR brightness temperatures to delay.
 Some information has been added, altough as mentioned earlier different algorithms
were applied for each WVR...
Add a figure that shows the dependence of standard deviation of the ZTD differences on humidity
or ZWD
 A new correlation plot (Figure 5) was added to the manuscript showing the
dependence of standard deviation of the ZTD differences between GNSS and VLBI on
mean ZWD.
10