the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 07 Mar 2025
China Coastal GNSS Network: Advancing Precipitable Water Vapor Monitoring and Applications in Climate Analysis
Abstract. The Global Navigation Satellite System (GNSS) offers precise, continuous monitoring of atmospheric water vapor, essential for weather forecasting and climate research. This study presents a high-accuracy precipitable water vapor (PWV) dataset from 55 GNSS stations along China’s coast (2009–2019). PWV retrievals utilized weighted mean temperature (Tm) and zenith hydrostatic delay (ZHD) derived from fifth-generation European ReAnalysis (ERA5) products. After rigorous quality control, the dataset achieved an average completeness rate of 70 %. Validation against ERA5 PWV products showed strong agreement (mean bias: 0.80 mm; RMS error: 2.52 mm), while comparisons with radiosonde profiles yielded a mean bias of 0.90 mm and an RMS error of 3.01 mm, confirming its accuracy and reliability. Spatial analysis revealed PWV values ranging from 0 to 88.57 mm, with minima decreasing with increasing latitude and concentrated around the Yangtze River estuary. Temporal patterns exhibited prominent annual and semi-annual cycles, particularly in higher latitudes. PWV showed a strong positive correlation with sea surface temperature (SST; r = 0.76), with a 1 K SST increase leading to a 2.4 mm (7 %) PWV rise. This dataset supports high-precision applications, including PWV validation, extreme weather prediction, and climate trend analysis. The processed ZTD and PWV datasets from 55 CGN stations are accessible at https://zenodo.org/records/14639032.
- Preprint
(22608 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 14 May 2025)
-
RC1: 'Comment on essd-2025-24', David Adams, 02 Apr 2025
reply
Review of "China Coastal GNSS Network: Advancing Precipitable Water Vapor Monitoring and Applications in Climate Analysis" by Zhilu Wu et al. April 2025
David K. Adams - dave.k.adams@gmail.com
General Commnents.
I think, in general, this article is very straightforward and should be accepted after some minor corrections. There are a few issues I raise in the paper and I have made lots of small corrections to the English. One thing I would recommend, is to include some yearly timeseries distributions of a couple of sites of PWV from different stations, as in Figure 12, but just for one year so that the reader can have a better idea of the accuracy and general climate in terms of humidity. Also, I thought your limits of PWV maximum and minimum values are a bit extreme (see below)
Specific Comments.
Line 25. You should probably be a bit more precise here instead of just referring to "water vapor". With respect to water vapor as a variable, what is typically most valuable for modeling, weather prediction, global climate studies is its vertical distribution and the total column water vapor or "precipitable water vapor".
Line 32. There have also been numerous field campaigns around the world employing GNSS meteorology, you should mention some from different regions of the world. I will let you choose and make no specific recommendation.
Line 47. Write "Recent research has utilized GNSS ..."
Line 48. What do you mean " project proposing water vapor products from ..." ? This idea is unclear.
Line 74. Write " ...providing reference positions for coastal research..."
Line 87 Write " At the outset, only observations from the GPS and GLONASS satellite constellations were available."
Line 87 Write " In recent years, with the advancement of the Galileo..."
Line 99 Write" ...University (Shi et al., 2008; Liu and Ge, 2003) using the static precise point position ..."
Line 102 Write " and an elevation-dependent weighting function was applied."
Line 110 Write "...ZTD consists of a hydrostatic part..."
Line 111 Write " ...(pressure and temperature) provided by Global Pressure and Temperature...,"
Line 114 This idea is a bit unclear. What do you mean by " Batch least-squares estimator" ?
Line 127 c. Validation of GNSS ZTDs based on ERA5 products
You do not have any local surface meteorological stations collocated or near the GNSS antennas?
You could use these surface met. stations for the surface pressure and to derive Tm with a simple model and then calcuate PWV. This would be good to compare against ERA5 since these ERA5 data are very smoothed in some respect (~ 25km x 25km grid
Line 164 Your PWV limiting values for outliers are very strange (0.72 mm and 86.21mm)
It is not physically possible to have PWV values near 0 nor near 90mm. Even under typhoon/hurricane conditions, the maximum PWV should be near 80mm at the highest. And PWV can never be near 0mm in these region under any conditions.
Line 166 " In addition, a station-specific outlier detection method was employed."
Line 167 " the median PWV value was calculated within a 15-day moving window centered on the specific day." Why do you employ such a long moving window? It is typically hourly or daily change in PWV that is of interest.
Line 182 I would not call ERA5 Numerical Weather model data. It is reanalysis data which include both observations and model output.
Line 240 " Additionally, the quality of the RS profiles may contribute to the larger biases observed."
Another thing to consider is that the RS can be biased if they rise through cloud/rainy conditions leading to higher PWV values than the GNSS PWV which has a large cone of observation ( ~20km diameter) can may contain clear skies in addition to the cloudy/rainy skies. These "saturated" soundings can be easily identified visually,
Line 325 Write "In addition, the spatiotemporal characteristics of coastal PWV in China were analyzed" And again, 0mm PWV values are not possible, there should always be a couple of mm of PWV even in very cold, dry weather in this region.
Citation: https://doi.org/10.5194/essd-2025-24-RC1
Data sets
China Coastal GNSS Network: Advancing Precipitable Water Vapor Monitoring and Applications in Climate Analysis Zhilu Wu and Bofeng Li https://zenodo.org/records/14639032
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 230 | 37 | 5 | 272 | 7 | 7 |
- HTML: 230
- PDF: 37
- XML: 5
- Total: 272
- BibTeX: 7
- EndNote: 7
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1