the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 Dec 2022
The use of ground-based GNSS for atmospheric water vapour variation study in Papua New Guinea and its response to ENSO events
Abstract. The spatial and temporal variability distribution of atmospheric water vapour in Papua New Guinea region is investigated using three ground-based GNSS station datasets and are compared with radiosonde data and the ERA-Interim reanalysis to generate the atmospheric precipitable water vapour (PWV) products over PNG from 2000 to 2019. From this product, PWV variations on multiple timescales are studied, with the water vapour products of GNSS and ERA-Interim in good agreement with their large-scale changes, which is reflective of the large-scale water vapour transport. At daily periods, the diurnal amplitudes of GNSS is larger at the mainland station (3.5 mm) than the two island stations (1–1.8 mm). The ERA-Interim amplitudes are smaller than GNSS on a daily basis, and do not capture the diurnal phases correctly. The estimated long-term PWV linear trends are predominantly positive and statistically significant which is in agreement in sign to the increase in moisture expected by the Clausius-Clapeyron equation under the background of global temperature rise. In addition, the regional impact of PWV in PNG in response to the El Niño- Southern Oscillation events are analysed using a correlation analysis, focusing on the dynamic influence of the large-scale nature of the 2010–2012 Bimodal La Niña and 2015–2016 El Niño events. The sea surface temperature anomaly in the Niño 3.4 and Niño 4 regions are selected to describe these two events. Both events portray overall negative correlation characteristics at the three GNSS stations with stations PNGM and RVO_ showing the strongest correlation during the 2010–2011 La Niña event significant at a 99 % confidence level.
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Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2022-1344', Anonymous Referee #1, 21 Aug 2023
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AC1: 'Reply on RC1', Ansaldi Senat, 04 Sep 2023
Hi Reviewer, truly appreciate your time in reviewing this manuscript. Your in-depth critical comments and recommendations are well acknowledged allowing the first author to see the manuscript from an expert's perspective. The manuscript will be revised based on your comments. Thank you!
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AC1: 'Reply on RC1', Ansaldi Senat, 04 Sep 2023
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RC2: 'Comment on egusphere-2022-1344', Anonymous Referee #2, 03 Oct 2023
General Comments
The work presented in the manuscript address several questions about the usefulness of ground-based GNSS estimates of the atmospheric integrated water vapour content (IWV). The time scale ranges from diurnal variability to long term linear trends over almost two decades.
My main criticism is that the results are not put into a context in terms what is the new knowledge. For example, it will be of general interest to know if IWV data from the three GNSS sites have been published before and if so what is new. One station is an IVS site so I assume data from this one have been published, although I have not been able to find IWV results presented for this specific station. From reading the title I expected to find that the GNSS data revealed new knowledge related to the El Nino phenomenon. But I do not read about any such findings. In fact I wonder if ERA Interim data already contains equivalent information? If not, what is the new findings provided by the GNSS data?
I find the section with conclusions too long. Furthermore I think it is a summary of what was done, rather than conclusions about what the results mean to the community.
The three sites are interesting because they are low latitude sites with a humid atmosphere. If these results are put into a context and assuming that new knowledge is obtained, I think it would be better to focus on one specific time scale and make a deeper, more detailed analysis, especially assessing the uncertainties of the estimated IWV. For example, carry out an error analysis for the IWV from GNSS which possibly can lead to the conclusion that the biases observed are caused by the other types of observations and not the GNSS?
I think that these GNSS data from Papua New Guinea could result in three different manuscripts: (1) One on the long term stability, (2) one on the diurnal variability, and (3) one on the information obtained related to the El Nino phenomenon.
(1) The long term stability is of interest to assess, especially (as I understand) presently no radiosondes at all are launched in this area (see additional comments below).
(2) The results for the diurnal variability are the most interesting in the present manuscript, simply because the differences seen, compared to ERA-Interim, offer information for scientists at ECMWF. However, what would make it even more interesting is to compare these results with the corresponding ones if you also compare to the more recent ERA5? Will ERA5 offer an improved agreement or will the observed differences remain?
(3) Also in this case my main question is what can GNSS meteorology add in terms of El Nino information compared to ERA Interim and ERA5? Is there a significant difference in this respect between ERA Interim and ERA5? The same question may also be relevant for the seasonal variations. Does ERA5 offer an improved agreement with GNSS IWV compared to ERA Interim?
Specific commentsIn the introduction it is first mentioned that an advantage with GNSS meteorology is the "homogeneity over the long term". Thereafter, it is mentioned that inconsistencies in the processing is a potential problem. Furthermore, I think one needs to assess the stability in terms of hardware changes (e.g. antenna) as well as changes in the nearby environment, affecting mutipath conditions. If these change over the time period studied they may also alias with estimated linear trends.
Table 2: What is the expected uncertainty in the radiosonde measurements. Is it possible to assess their contribution to the bias and the differences reported in te table?
Subsection 5.1: It will be meaningful to make an IWV error budget both for the radiosonde and GNSS in order to conclude if these are consistent with the observed differences.
Technical CorrectionsBecause I do not believe that this is the final version of the manuscript I did not go through the text for all possible technical corrections. Here I just mention a few that was noted.
Although it is obvious I think the acronym PNG shall be defined.
Line 15, and many other places: insert a "space" between the value and the unit.
Table 3: The standard deviation is mentioned in the header, but it is not presented.
line 661: "A morning peak between 9 and 12 UT", this is not morning at these longitudes?
Citation: https://doi.org/10.5194/egusphere-2022-1344-RC2
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2022-1344', Anonymous Referee #1, 21 Aug 2023
-
AC1: 'Reply on RC1', Ansaldi Senat, 04 Sep 2023
Hi Reviewer, truly appreciate your time in reviewing this manuscript. Your in-depth critical comments and recommendations are well acknowledged allowing the first author to see the manuscript from an expert's perspective. The manuscript will be revised based on your comments. Thank you!
-
AC1: 'Reply on RC1', Ansaldi Senat, 04 Sep 2023
-
RC2: 'Comment on egusphere-2022-1344', Anonymous Referee #2, 03 Oct 2023
General Comments
The work presented in the manuscript address several questions about the usefulness of ground-based GNSS estimates of the atmospheric integrated water vapour content (IWV). The time scale ranges from diurnal variability to long term linear trends over almost two decades.
My main criticism is that the results are not put into a context in terms what is the new knowledge. For example, it will be of general interest to know if IWV data from the three GNSS sites have been published before and if so what is new. One station is an IVS site so I assume data from this one have been published, although I have not been able to find IWV results presented for this specific station. From reading the title I expected to find that the GNSS data revealed new knowledge related to the El Nino phenomenon. But I do not read about any such findings. In fact I wonder if ERA Interim data already contains equivalent information? If not, what is the new findings provided by the GNSS data?
I find the section with conclusions too long. Furthermore I think it is a summary of what was done, rather than conclusions about what the results mean to the community.
The three sites are interesting because they are low latitude sites with a humid atmosphere. If these results are put into a context and assuming that new knowledge is obtained, I think it would be better to focus on one specific time scale and make a deeper, more detailed analysis, especially assessing the uncertainties of the estimated IWV. For example, carry out an error analysis for the IWV from GNSS which possibly can lead to the conclusion that the biases observed are caused by the other types of observations and not the GNSS?
I think that these GNSS data from Papua New Guinea could result in three different manuscripts: (1) One on the long term stability, (2) one on the diurnal variability, and (3) one on the information obtained related to the El Nino phenomenon.
(1) The long term stability is of interest to assess, especially (as I understand) presently no radiosondes at all are launched in this area (see additional comments below).
(2) The results for the diurnal variability are the most interesting in the present manuscript, simply because the differences seen, compared to ERA-Interim, offer information for scientists at ECMWF. However, what would make it even more interesting is to compare these results with the corresponding ones if you also compare to the more recent ERA5? Will ERA5 offer an improved agreement or will the observed differences remain?
(3) Also in this case my main question is what can GNSS meteorology add in terms of El Nino information compared to ERA Interim and ERA5? Is there a significant difference in this respect between ERA Interim and ERA5? The same question may also be relevant for the seasonal variations. Does ERA5 offer an improved agreement with GNSS IWV compared to ERA Interim?
Specific commentsIn the introduction it is first mentioned that an advantage with GNSS meteorology is the "homogeneity over the long term". Thereafter, it is mentioned that inconsistencies in the processing is a potential problem. Furthermore, I think one needs to assess the stability in terms of hardware changes (e.g. antenna) as well as changes in the nearby environment, affecting mutipath conditions. If these change over the time period studied they may also alias with estimated linear trends.
Table 2: What is the expected uncertainty in the radiosonde measurements. Is it possible to assess their contribution to the bias and the differences reported in te table?
Subsection 5.1: It will be meaningful to make an IWV error budget both for the radiosonde and GNSS in order to conclude if these are consistent with the observed differences.
Technical CorrectionsBecause I do not believe that this is the final version of the manuscript I did not go through the text for all possible technical corrections. Here I just mention a few that was noted.
Although it is obvious I think the acronym PNG shall be defined.
Line 15, and many other places: insert a "space" between the value and the unit.
Table 3: The standard deviation is mentioned in the header, but it is not presented.
line 661: "A morning peak between 9 and 12 UT", this is not morning at these longitudes?
Citation: https://doi.org/10.5194/egusphere-2022-1344-RC2
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