Contributors: Kalev Rannat (Tallinn University of Technology, TUT), Hannes Keernik (TUT), Fabio Madonna (CONSIGLIO NAZIONALE DELLE RICERCHE – ISTITUTO DI METODOLOGIE PER L'ANALISI AMBIENTALE, CNR-IMAA), Emanuele Tramutola (CNR-IMAA), Fabrizio Marra (CNR-IMAA)
Issued by: CNR-IMAA / Fabio Madonna
Date: 03/06/2021
Acronyms
Introduction
This document provides a short description of GNSS IPW datasets provided in the CDS catalogue and retrieved using the GNSS data of the IGS network and the EPN-repro2 dataset. Additional information about GNSS measuring principles, hardware and raw data processing can be found in related ATBD document.
The GNSS IPW provided in the CDS are retrieved from ZTD that is a final product from GNSS raw data processing. In the remainder of this document the expression “GNSS troposphere product” refers to ZTD with its formal 1σ error as ZTD uncertainty. Although the data for IGS and EPN-repro2 come from different sources and in a different format, the methodology for converting ZTD to IPW is the same for both, except for some minor technical details.
The IGS collects, archives, and freely distributes GNSS observation data sets from a cooperatively operated global network of more than 500 ground tracking stations. The IGS network is classified as a reference network on the basis of the Measurement System Maturity Matrix (MSMM) approach [1], ensuring open access, high-quality GNSS data products since 1994. The product used for retrieval of IGS IPW is produced by 12 licensed Analysis Centres (AC).
The EPN-repro2 dataset [2] is a reprocessed product from EUREF Permanent Network (EPN, http://www.epncb.oma.be/) with more than 300 continuously operating GNSS reference stations with precisely known coordinates. It consists of 18+ years of GNSS data, making it a valuable database for the development of a climate data record of GNSS tropospheric products over Europe. The product name, EPN-repro2, denotes the second reprocessing campaign of the EPN, where five ACs homogenously reprocessed the EPN network for the period 1996–2014. Finally, the individual contributions of each AC are combined to provide the official EPN reprocessed products. The combined product (based on the methods presented in Pacione, et al., 2011 [3] is described along with its evaluation against radiosonde data and European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-Interim) data.
Meteorological information used as ancillary data required for the IPW estimation is obtained from the ECMWF ERA5 atmospheric reanalysis. The methods for requesting data co-located with the GNSS sites can be found in the ATBD document.
In the following sections, a short retrospective overview is given about GNSS IPW quality assurances, data and metadata sources (Section 2), data processing for both IGS and EPN-repro2 (Section 3), data validation issues (Section 4), examples of IPW comparison (Section 5), data and metadata format in CDS (Section 6) and data licensing (Section 7).
The maturity matrix of IGS network can be found from Appendix A. Description of data units and conversion is given in Appendix B.
Data and metadata sources
The IGS datasets are available since 22th October 2000 through the CDDIS data portal (https://cddis.nasa.gov/archive/gnss/products/troposphere/zpd/ , Fig. 1):
Figure 1: Map of the global distribution of IGS sites.
Metadata consists of additional necessary data used in a process of estimating GNSS IPW and its uncertainty, described in the ATBD document. The metadata can be found from IGS sites’ specifications and from the headers of SINEX TRO files (described in M311a_Lot3.3.2.3_2020, Section 4.1 and at https://igs.org/formats-and-standards/ ). However, technical implementation of IPW software package uses SEMISYS (referred in product availability and licenses section).
The GNSS troposphere product available at CDDIS is delivered by the IGS data processing agencies in a unified SINEX TRO format. However, these ZTD time series are still not corrected for any effects derived from instrumental changes (receivers, antennas, radomes) or changes in station environment. This kind of analysis needs extensive data reprocessing. A comprehensive description of the IGS GNSS troposphere data products can be found in the ATBD document. While not best suited for long-term climate studies, the IGS daily dataset is relevant in atmospheric analyses due to its worldwide network and relatively short data latency (ca 2 weeks).
Good examples of GNSS reprocessed time series are EPN-repro1 and EPN-repro2 (available for EUREF, http://www.epncb.oma.be/_productsservices/analysiscentres/repro2.php). The dataset is freely available from BKG (Bundesamt für Kartographie und Geodäsie): ftp://igs.bkg.bund.de/EPNrepro2/products/.
In the framework of the EPN-repro2, the second reprocessing campaign of the EPN, five ACs homogeneously reprocessed the EPN network (Fig. 2) for the period 1996–2014 [2]. ZTD screening, which is the process of inspecting data for errors and correcting them prior to performing data analysis, has been applied to all initial ZTD time series [2]. By doing this, all estimated shifts (biases) from GNSS antenna replacements are removed [2]. Therefore, this data record can be used as a reference for a variety of scientific applications (e.g. validation of regional numerical weather prediction reanalyses and climate model simulations) and has a high potential for monitoring trends and the variability in atmospheric water vapour.
Figure 2. Map of the EPN stations, 89 out of these sites also belong to the IGS network.
The EPN-repro2 data is recorded by GNSS weeks (834–1825) with combined solutions recorded in SINEX TRO mixed format. Unlike CDDIS for IGS, the EPN ftp service supports traditional anonymous data access. One of the major differences compared to IGS datasets is that EPN-repro2 data is not recorded at full hours, but with a 30 minutes shift (00:30, 01:30, …, 23:30).
The daily ZTDs provided by the ACs are combined on a weekly basis. Only sites with a corresponding coordinate solution and with three individual AC contributions are presented. Estimates with a given STDDEV > 15 mm are excluded. Rough outlier detection is performed to find strong outliers. Details on the combination process can be found from [3].
For estimating GNSS IPW based on ZTD, ancillary meteorological data are needed. This are obtained from the ERA5 reanalysis dataset available via the CDS. Geopotential, specific humidity and temperature values are downloaded for 37 pressure levels in order to calculate water-vapor-weighted mean temperature of the atmosphere, Tm, which is crucial for converting ZTD to IPW.
GNSS data processing and retrieval of IPW
The GNSS IPW provided through the CDS is estimated based on using tropospheric products delivered by IGS data ACs and EPN-repro2. The data processing schema for retrieval of GNSS IPW for both IGS and EPN tropospheric product is described in more detail in ATDB document. Here only a simplified description of the data flow and data processing steps for retrieval of GNSS IPW from GNSS tropospheric products is depicted (Fig. 3).
Figure 3: Retrieval of IPW for IGS and EPN-repro2
- Pre-processing of GNSS troposphere product (steps 1–4). First, ZTD with its uncertainty is downloaded from IGS and EPN data repositories (steps 1–2, Fig. 3). The site metadata (coordinates, altitude, city, agency etc.) for both networks are obtained from SEMISYS [4] (step 3). Due to the fact that the site latitude and longitude are given with height from WGS84 ellipsoid, the AMSL (height above the mean sea level) used in IPW retrieval, must be calculated first (step 4).
- Pre-processing of ancillary data (steps 5–7). The supporting meteorological data at all ERA5 pressure levels and at ground surface level are downloaded, the water-vapour-weighted mean temperature of the atmosphere (Tm) is calculated and bilinearly interpolated to the site coordinates and altitude (step 5). Since the time resolution of both, ERA5 and IGS ZTDs, is one hour there is no need to interpolate Tm and ground surface pressure (p0) in time. On the other hand, this is done in case of EPN-repro2 since its data are provided at half-hours. Depending on dry gases between receiver and satellite, the ZHD is calculated using p0, latitude and height of the site above the mean sea level (step 6). The ZWD, on the other hand, depends on water vapour amount and is found by subtracting ZHD from ZTD. The conversion factor that is needed to relate IPW to the ZWD is calculated using a function of Tm (step 7).
- Calculating IPW and its uncertainty (steps 8–10). IPW estimation (in units kg m-2, equivalent to mm) is given using ZWD and the conversion factor (step 8). For calculating GNSS IPW uncertainty (step 9), the approach chosen for C3S_311a_Lot3 is based on the GRUAN GNSS data processing [5], and accounts for all of the uncertainty sources due to the measurement systems. The IPW uncertainty calculation relies on previously published values for input variables used as constants and their uncertainties [6]. In case of uncertainties for p0 and Tm, these values, statistically determined using IGRA and GRUAN radiosonde measurements, have been found to be in a good agreement with the results presented in several papers specifically focusing on ERA5 [7, 8]. After the values for IPW and its uncertainty are calculated, the results are ingested to the database (step 10).
Data validation process
IGS
The integrity of the data in daily SINEX TRO files is guaranteed by the IGS ACs. However, for assuring the data quality for retrieval of GNSS IPW and its uncertainty (methodology described in Ning et al., 2016 [5]) the data processing chain for CDS comprises additional steps as follows:
- Checking ZTD values for range. The valid range is [1.00, 3.00] m;
- Checking formal error of ZTD (σZTD). A ZTD value is accepted if σZTD <= 10 mm or σZTD < 2.5 · median (the latter is applied only if the 24 hours median < 3.0 mm)
If any of these checks fails, the ZTD or σZTD will be assigned a “null” value (indicating a missing or unacceptable value).
These checks are similar to those used by screening of ZTD time series for IGS repro-products [2, 9]. However, it must be noted, that a range check on formal errors should be station-specific [2] and the time series have not passed all the procedures applied on reanalysis (e.g., EPN-repro2).
IGS sites’ metadata (site coordinates) are checked on-line by SEMISYS and corrected by proofed values from SEMISYS if the horizontal displacement exceeds 90 meters (equivalent of 3 arc seconds). Coordinate check avoids using erroneous AMSL values from a geoid model.
EPN
EPN-repro2 time series do not need any additional quality check. All necessary quality checks are performed already during reprocessing. EPN-repro2 is known as a reference quality data product [2].
Unlike many other measurements in environmental physics (atmospheric temperature, humidity, etc.) the GNSS IPW is retrieved from GNSS ZTDs characterised with uncertainties expressed in terms of formal (not instrumental) errors.
The characteristic values depend on GNSS data processing software and the IGS specifies σZTD limits to 4 mm [10] for observations in mostly ideal conditions with instrumental installations following strict IGS technical requirements. The sources of uncertainties contributing to the final σIPW are extensively analysed in Ning et al., 2016 [5] and in GAIA-CLIM D2.8, Annex IX, Product Traceability and Uncertainty for the GNSS IPW Product [11].
Comparing σZTD values between IGS daily product and EPN-repro2, the user may notice remarkable differences. In case of IGS daily data these values stay around 2 mm. At the same time, EPN-repro2 σZTD values can reach 5–6 mm. As a direct consequence to this (σZTD contributes over 75% to the total IPW uncertainty), the total σIPW values may reach slightly over 1 mm. It should be noted that EPN-repro2 is a combined product [3] of the results provided by different ACs which use different software — Bernese, GIPSY and GAMIT. While the ZTD values estimated by different software agree within around 3 mm, the σZTD values may differ up to three times. This is due to the fact that the initial constraints for the models used by the software packages are different (a dedicated reader may refer to the software manuals). The Bernese and GIPSY show σZTD values usually around 1.5–2 mm while σZTD provided by the GAMIT is around 4–5 mm. Eventually, the higher σZTD values from GAMIT lead to relatively higher values of σZTD in EPN-repro2. An extensive analysis by comparing the differences from diverse GNSS data processing software and data processing strategies on GNSS IPW values itself can be found from [12, 13], where mean values of IPW estimated from GNSS observations agree within 0.5 mm.
The differences between σZTD values derived from different software is a reason why, in addition to using σZTD as given by data providers, the uncertainty of GNSS IPW product could be calculated with fixed σZTD = 4.0 mm (as suggested by IGS [10]). While also σZTD values are available to the CDS users, these values should not be used mechanically. Depending on the application, these uncertainty estimates may need to be scaled according to intercomparison experiments (for example, GNSS versus VLBI, MWR or radiosonde).
Examples
A short overview about the comparison results is given in this section. The following comparisons are made by using the IGS daily tropospheric product only. The IPW estimation from 18 globally distributed IGS stations were compared for the period 2014–2019 with the IPW values calculated from four independent co-located data sets (Fig. 4):
- co-located MWR, retrieved from Onsala MWR ZWDs (January–June 2019 data only);
- ERA5 (2014–2019), using bilinear spatially interpolated values to the locations of the GNSS site (noting that to the extent ERA5 provides a priori constraints to the GNSS post-processing the two series are not entirely independent);
- simultaneous co-located radiosoundings, available in IGRA (for 15 stations, 2014–2019);
- simultaneous co-located radiosoundings from GRUAN (for Tateno and Ny-Ålesund only, 2014–2019).
The co-located stations were determined using two criteria: distance and height difference between stations were <15 km and <50 m, respectively.
Figure 4: Map of IGS stations used in comparability study. Station ONSA is co-located with MWR, others are co-located with IGRA or GRUAN radiosonde stations. The IGS stations are divided into two categories based on the distance from the co-located datasets: (1) within 2 km radius (in red) and (2) within 2–15 km radius (in blue).
With only two exceptions out of 18 sites, ERA5−IGS, GRUAN RS−IGS and IGRA−IGS IPW differences follow the same pattern (i.e. have the same sign for specific stations; an example for selected sites is given in Fig. 5). As seen from Table 1 the average IPW bias over all comparison techniques included ranges from 0.15 mm (ERA5–IGS) to 0.64 (MWR–IGS), while its average RMSD ranges from 0.91 mm (GRUAN RS–IGS) to 1.64 mm (IGRA–IGS).
Some researchers have reported a seasonal behaviour of the GNSS–RS IPW bias [14, 15], others have pointed out only a dependency between its RMSD and latitude [16, 17] without seeing a clear effect on biases. Based on the presented examples, there is no dependency between the IPW difference and latitude. On the contrary, a strong correlation between IPW difference, RMSE and latitude is found similarly to previous studies, with highest values at the lowest latitudes.
In addition, a seasonal cycle in the standard deviation of the differences is present for most of the stations, especially at continental stations like ALIC, ANKR, BAKE and PICL. The highest values for standard deviation of differences occur in summertime when IPW reaches its maximum. Based on GNSS and GRUAN RS data collected at NYA1 and TSKB, this kind of seasonal pattern in standard deviation of IPW differences is mostly due the seasonal cycle in the IPW estimated from radiosounding data. Compared to wintertime, the uncertainty of GRUAN RS-estimated IPW in summer is three and five times higher at NYA1 and TSKB, respectively.
The uncertainty of GNSS-estimated IPW was calculated using two different approaches: (1) using σZTD = 4 mm as claimed by IGS and (2) using σZTD values provided by CDDIS. The annual average uncertainties of GNSS-estimated IPW when using σZTD = 4 mm in its calculations are 0.66 and 0.72 mm at NYA1 and TSKB. If using σZTD values provided by CDDIS, the uncertainty decreases to 0.43 and 0.49 mm, respectively. At the same time, GRUAN RS-estimated average IPW uncertainty at these stations was 0.31 and 1.07 mm. It can be concluded that the magnitude of the GRUAN RS and IGS GNSS data uncertainties is sufficiently large to explain the differences between the IPW measurements.
Table 1: Average IPW bias and RMSD between IGS data and co-located techniques.
Comparison | No. of co-located sites | Avg. points per site | Bias | RMSD | ||
Avg. [mm] | Range [mm] | Avg. [mm] | Range [mm] | |||
ERA5–IGS | 18 | 46 894 | 0.15 | -0.9 to 0.93 | 1.46 | 0.93 to 2.6 |
GRUAN RS–IGS | 2 | 704 | 0.23 | 0.11 to 0.36 | 0.91 | 0.6 to 1.23 |
IGRA–IGS | 15 | 2 054 | 0.43 | -0.9 to 1.84 | 1.64 | 0.73 to 2.27 |
MWR–IGS | 1 | 3 120 | 0.64 | – | 1.21 | – |
Figure 5: IPW differences in IGRA−IGS, ERA5−IGS, MWR−IGS and GRUAN RS-IGS co-location datasets.
An example of IPW comparison between EPN-repro2, ERA5 and GRUAN RS during 2014 for NYA1 is shown in Fig. 6. While the two datasets have different scope of application, the results obtained using EPN-repro2 are comparable to the outcome derived from IGS tropospheric product. Considering only the year 2014 and IGS data, the average ERA5–GNSS and GRUAN RS–GNSS IPW differences at NYA1 are –0.07 and 0.31 mm (RMSD values 0.67 and 0.6 mm), respectively. When EPN-repro2 is used instead of IGS daily data, these values become 0.02 and 0.44 mm (RMSD values 0.53 and 0.58 mm, respectively). Therefore, it can be concluded that in terms of IPW RMSD, EPN-repro2 shows slightly better agreement with GRUAN RS compared to IGS. This is in line with expectations due to the fact that IGS provides ZTD on a weekly basis with a delay up to 20 days without any reprocessing and homogenization.
Figure 6.: IPW differences in ERA5−EPN-repro2 and GRUAN RS−EPN-repro2 co-location datasets at NYA1.
GNSS data and metadata format in the CDS
The CDS web interface provides data in two CSV formats:
- Level-wise rows;
- Observation-wise rows.
The option to download the data NetCDF4 format will become available soon.
All CDS in-situ observations share a common data model (CDM). This format is described in the CDS documentation.
GNSS product analyses and ancillary information for C3S users
The GNSS product for IGS and EPN-repro2 and ancillary data used for retrieval of GNSS IPW is stored into the tables in the database. The data flow is depicted in Figure 7.
Figure 7: Ancillary data for retrieval of GNSS IPW
Being the ancillary data recorded in the same database of the GNSS IPW values, the users may access also the meteorological conditions at the same time of the GNSS IPW retrieval. Moreover, the availability of the IPW values estimated from ERA5 gives the possibility to compare the results (GNSS IPW) with ERA5 reanalysis, which is a common practice in climate studies.
For both IGS and EPN, the site metadata are obtained from SEMISYS and the sites' amsl-values calculated by EGM2008 geoid model.
IGS and EPN dataset tabular description
Table 2. Metadata table
standard name | description |
report_id | This parameter starts from 1 for the first data report provided in the data file, and is incremented for each new report. |
| report_timestamp | Observation date time UTC |
| station_name | GNSS station identifier |
| city | City name |
| organisation_name | Name of the agency responsible of the station |
| latitude | Latitude deg. North |
| longitude | Longitude deg. East |
| sensor_altitude | GNSS antenna height from WGS 84 ellipsoid (maintained by the United States National Geospatial-Intelligence Agency) |
| height_of_station_above_sea_level | Altitude above mean sea level |
| start_date | Date time since data is available for the station |
Table 3. Data table
standard name | description |
| zenith_total_delay | It is one of the final products from geodetic GNSS data processing software, characterizing a delay of the GNSS signal on the path from a satellite to the receiver due to atmospheric refraction and bending, mapped into zenith direction. Numerical value of zenith total delay correlates with the amount of total column water vapour (i.e., not including liquid water and/or ice) overhead the GNSS receiver antenna. |
| zenith_total_delay_random_uncertainty | Rough estimate of standard uncertainty equivalent to 1-sigma uncertainty of zenith total delay |
| total_column_water_vapour | Total column water vapour derived from ZTD and ancillary meteorological data |
| total_column_water_vapour_combined_uncertainty | Combined uncertainty of GNSS total column water vapour |
| total_column_water_vapour_era5 | Total column water vapour retrieved from ERA5 at the station coordinates and altitude |
Product Availability and data licenses
The data from both IGS and EPN networks is public and free for use. However, there exist some strong recommendations on usage and citing of the products.
The terms of use of IGS products can be found from IGS Data and Product Disclaimer and Terms of Use (5 August 2020) document, downloadable from:
Due to the fact that GNSS IPW is retrieved from IGS data in the CDDIS data repository, the CDDIS should be cited and acknowledged as follows:
Noll, The Crustal Dynamics Data Information System: A resource to support scientific analysis using space geodesy, Advances in Space Research, Volume 45, Issue 12, 15 June 2010, Pages 1421-1440, ISSN 0273-1177, DOI: 10.1016/j.asr.2010.01.018.
Retrieval of GNSS IPW needs also site metadata (first of all, the exact coordinates and altitude), obtained from GFZ data services:
Bradke, Markus (2020): SEMISYS - Sensor Meta Information System. GFZ Data Services, https://doi.org/10.5880/GFZ.1.1.2020.005
The GNSS data from the EPN stations are freely available through the internet: (http://www.epncb.oma.be/_networkdata/data_access/).
The EPN data is freely accessible, but must be used according to instructions given at https://www.epncb.oma.be/
Whenever using EPN Central Bureau data, products, or services, please include the citation Bruyninx, C., Legrand, J., Fabian, A., Pottiaux E., GNSS metadata and data validation in the EUREF Permanent Network, GPS Solut (2019) 23: 106. https://doi.org/10.1007/s10291-019-0880-9.
The EPN-repro2 products can be downloaded from:
ftp://igs.bkg.bund.de/EPNrepro2/products.
While using the data from EPN-repro2, the user could also refer to the following articles (the first describing the combination methods and the second the EPN-Repro2 dataset):
Pacione, et al., Combination methods of tropospheric time series, Advances in Space Research 47 (2011) 323–335, doi:10.1016/j.asr.2010.07.021
R. Pacione, et al., EPN-Repro2: A reference GNSS tropospheric data set over Europe, Atmos. Meas. Tech., 10, 1689–1705, 2017, www.atmos-meas-tech.net/10/1689/2017, doi:10.5194/amt-10-1689-2017.
The metadata from EPN originates from the same service as for IGS and should be cited as given above.
References
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[2] Pacione, R., et al., EPN-Repro2: A reference GNSS tropospheric data set over Europe, Atmos. Meas. Tech., 10, 1689–1705, 2017, www.atmos-meas-tech.net/10/1689/2017, doi:10.5194/amt-10-1689-2017.
[3] Pacione, R., et al., Combination methods of tropospheric time series, Advances in Space Research 47, 323–335, 2011, doi:10.1016/j.asr.2010.07.021.
[4] Bradke, Markus, SEMISYS - Sensor Meta Information System. GFZ Data Services, 2020, https://doi.org/10.5880/GFZ.1.1.2020.005.
[5] Ning, T. et al., The uncertainty of the atmospheric integrated water vapour estimated from GNSS observations, Atmos. Meas. Tech., 9, 79–92, 2016, www.atmos-meas-tech.net/9/79/2016/, doi:10.5194/amt-9-79-2016.
[6] Bevis, M., S. Businger, S. Chiswell, T. A. Herring, R. A. Anthes, C. Rocken, and R. H. Ware, GPS Meteorology: Mapping Zenith Wet Delays onto Precipitable Water. J. Appl. Meteor., 33, 379–386, 1994, https://doi.org/10.1175/1520-0450(1994)033<0379:GMMZWD>2.0.CO;2.
[7] Ssenyunzi, R.C., Oruru, B., D’ujanga, F.M., Realini, E., Barindelli, S., d, Tagliaferro, G., Engeln, A., Giesen, N., Performance of ERA5 data in retrieving Precipitable Water Vapour over East African tropical region, Advances in Space Research, 2020, 65. 10.1016/j.asr.2020.02.003.
[8] Mateus, P., Catalão, J., Mendes, V.B., Nico, G., An ERA5-Based Hourly Global Pressure and Temperature (HGPT) Model, Remote Sens. 12, 1098, 2020.
[9] Bock, O. (2020) ZTD Screening, in Jones, J. et al. (eds.), Advanced GNSS Tropospheric Products for Monitoring Severe Weather Events and Climate, Springer 2020, https://doi.org/10.1007/978-3-030-13901-8.
[10] IGS, Atmospheric products: accuracy of ZTD, https://www.igs.org/products/#about.
[11] GAIA-CLIM Report/Deliverable D2.8 Final report on the measurement uncertainty gap analysis from each subtask under Task 2.1 of WP2, http://www.gaia-clim.eu/sites/www.gaia-clim.eu/files/document/d2.8.pdf.
[12] Ahmed, F., Evaluation of GNSS as a Tool for Monitoring Tropospheric Water Vapour (thesis), Department of Earth and Space Sciences, Chalmers University of Technology, Göteborg, Sweden, 2010.
[13] Baldysz, Z., Nykiel, G., Figurski, M. and Araszkiewicz, A., Assessment of the Impact of GNSS Processing Strategies on the Long-Term Parameters of 20 Years IWV Time Series, Remote Sens. 2018, 10, 496; doi:10.3390/rs10040496.
[14] Ohtani, R., and Naito, I., Comparisons of GPS-derived precipitable water vapors with radiosonde observations in Japan, J. Geophys. Res., 105(D22), 26917–26929, 2000, doi: 10.1029/2000JD900362.
[15] Deblonde, G., Macpherson, S., Mireault, Y., and Heroux, P., Evaluation of GPS precipitable water over Canada and the IGS network, J. Appl. Meteorol., 44, 2005, doi: 10.1175/JAM-2201.1, 153–166.
[16] Van Malderen, R., Brenot, H., Pottiaux, E., Beirle, S., Hermans, C., De Mazière, M., Wagner, T., De Backer, H., and Bruyninx, C., A multi-site intercomparison of integrated water vapour observations for climate change analysis, Atmos. Meas. Tech., 7, 2487-2512, 2014, doi:10.5194/amt-7-2487-2014.
[17] Vey, S., Dietrich, R., Rülke, A., Fritsche, M., Steigenberger, P., and Rothacher, M., Validation of Precipitable Water Vapour within the NCEP/DOE Reanalysis Using Global GPS Observations from One Decade, J. Climate, 23, 1675–1695, 2010, doi: 10.1175/2009JCLI2787.1.
Appendix A: IGS maturity matrix
Appendix B: Data units and conversions
GNSS IPW and uncertainty of IPW:
IPW and σIPW: in kg m-2 (equivalent to mm - both units are used in scientific literature)
Temperature: in Kelvins [K]
Pressure: in Pascals [Pa]
Conversion to SI units:
Temperature in [K] = Temperature [°C] + 273.15
SI unit Pascal [Pa], 1 hPa = 100 Pa
