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
Last updated: 25/11/2024
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This document provides a short description of GNSS Integrated Precipitable Water (IPW) datasets provided in the Copernicus Data Store (CDS) catalogue and retrieved using the GNSS data of the International GNSS Service (IGS) and the EUREF Permanent Network (EPN). Additional information about GNSS IPW measuring principles, hardware and raw data processing can be found in the related ATBD document.
The GNSS IPW datasets provided in the CDS are retrieved from GNSS tropospheric products. In the remainder of this document the expression “GNSS troposphere product” refers to Zenith Total Delay (ZTD) with its formal 1σ error as ZTD uncertainty. The methodology for converting ZTD to IPW is the same for all GNSS tropospheric datasets, except for some minor technical details.
CDS offers two categories of GNSS tropospheric products: Near Real-Time (NRT) data, which includes IGS daily, and reprocessed datasets, which encompass data from the reprocessing campaigns of IGS and EPN (IGS-repro3 and EPN-repro2, respectively).
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 based on the Measurement System Maturity Matrix (MSMM) approach [1], ensuring open access to high-quality GNSS data products since 1994. The product used for retrieval of IGS IPW is produced by 12 licensed Analysis Centres (AC).
IGS-repro3 is a product of the 3rd IGS reprocessing campaign, performed by IGS ACs. The repro3 product [2] chosen for CDS, covers the period 2000-2019 and is processed by the Center for Orbit Determination in Europe (CODE, https://www.aiub.unibe.ch/research/code___analysis_center/index_eng.html).
Both IGS and EPN follow the same high technical and quality assessment requirements. The EPN-repro2, the product of the 2nd EPN reprocessing campaign, similar to the IGS dataset, is also of reference quality [3]. It consists of 18+ years of GNSS data belonging to the EUREF Permanent Network (EPN, http://www.epncb.oma.be/), making it a valuable database for the development of a climate data record of GNSS tropospheric products over Europe. For EPN-repro2, 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 is described along with its evaluation against radiosonde data and European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-Interim) data by Pacione, et al. [4].
Meteorological information used as auxiliary 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. The IPW derived from the reprocessed tropospheric products can be recommended for climate research.
In the following sections, a short retrospective overview is given about GNSS IPW quality assurances, data and metadata sources (Section 2), data processing (Section 3), data validation process (Section 4), examples of IPW comparison (Section 5), data and metadata format in CDS (Section 6), GNSS product analysis and ancillary products (Section 7) as well as data licensing (Section 8). The maturity matrix of the IGS network can be found in Appendix A. Description of data units and conversion is given in Appendix B.
The daily (NRT) tropospheric products for IGS sites (Fig. 1) are publicly available through the CDDIS data portal https://cddis.nasa.gov/archive/gnss/products/troposphere/zpd/ . The troposphere products utilize the IGS final satellite, orbit, and Earth Orientation Parameters (EOP) products and are therefore available approximately three weeks following the observation day. Since the inception of the troposphere product, three analysis groups (GFZ, JPL, and currently USNO) have generated the IGS combination solution each using different methodologies. Therefore, the resulting file types have changed through the years. The current product consists of files containing daily ZTD estimates for selected sites in the IGS network. The original product consisted of one file per site per GPS week [https://igs.org/products/#troposphere].
Figure 1: A map showcasing the worldwide locations of IGS sites with both NRT and reprocessed datasets accessible through CDS, along with sites offering access to EPN repro2 data.
The GNSS tropospheric products available at CDDIS are delivered by the IGS ACs in a unified SINEX TRO format (description available at https://files.igs.org/pub/data/format/sinex_tropo.txt). For IGS-daily the ZTD time series are still not corrected for any effects derived from instrumental changes (receivers, antennas, radomes) or changes in the 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 2-3 weeks).
The reprocessed GNSS IWV time series in CDS, which relies on tropospheric products from IGS, is denoted as IGS-repro3. The ZTD data utilized for these series can be accessed at https://cddis.nasa.gov/archive/gnss/products/repro3 , and are also accessible through a few other IGS global data centers. IGS-repro3 consists of the results of 10 ACs (https://igs.org/news/repro3-solutions-now-available/ ), each processing its own set of GNSS sites according to their best practices and not all of them providing the corresponding tropospheric products. It must be noted that the IGS-repro3 does not resemble a combined product but, instead, the combination of the single dataset provided by each AC.
Details about the available products, main updates in the modelling since the repro2 version, and the combination strategy can be found at https://lists.igs.org/pipermail/igsmail/2021/008022.html.
ZTD screening, which is the process of inspecting data for errors and correcting them before performing data analysis, has been applied to all initial ZTD time series [3, 5]. Data reprocessing removes all estimated shifts (biases) from GNSS antenna replacements [5]. Therefore, this data record can be used as a reference for various 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.
GNSS IPW retrieved from the reprocessed ZTD time series from EPN-repro2 (http://www.epncb.oma.be/_productsservices/analysiscentres/repro2.php) is offered as the third product to the CDS users. In the framework of the EPN-repro2, the second reprocessing campaign of the EPN, five ACs homogeneously reprocessed the EPN network (Fig. 1) for the period 1996–2014 [3]. The reprocessing techniques conducted and the quality control applied by EPN ACs closely resemble those employed by IGS ACs. EPN-repro2 is known as a reference quality data product [3]. The ZTD dataset is freely available from BKG (Bundesamt für Kartographie und Geodäsie): ftp://igs.bkg.bund.de/EPNrepro2/products/.
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-minute shift (00:30, 01:30, …, 23:30).
Contrary to IGS-repro3, the EPN-repro2 is a combined product. The daily ZTDs provided by the ACs are combined weekly. 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 in [5].
The metadata consists of additional necessary data used in estimating GNSS IPW and its uncertainty, described in the ATBD document. The GNSS-related metadata can be retrieved from GNSS sites’ specifications (logfiles) and the headers of SINEX TRO files (described in M311a_Lot3.3.2.3_2020, Section 4.1 and at https://kb.igs.org/hc/en-us/articles/201096516-IGS-Formats). However, the technical implementation of the IPW software package uses SEMISYS (the service of GFZ, referred to in the product availability and licenses section).
For estimating GNSS IPW based on ZTD, ancillary meteorological data are needed. These are obtained from the ERA5 reanalysis dataset available via the CDS. Geopotential, specific humidity and temperature values are downloaded for 37 pressure levels to calculate the water-vapor-weighted mean temperature of the atmosphere, Tm, which is crucial for converting ZTD to 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 products is described in more detail in the ATBD 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. 2).
Figure 2: Retrieval of IPW for IGS and EPN-repro2
The integrity of the data in SINEX TRO files is guaranteed by the IGS ACs. However, to ensure the data quality for retrieval of GNSS IPW and its uncertainty (methodology described in Ning et al., 2016 [7]) the data processing chain for CDS comprises additional steps as follows:
If any of these checks fail, the ZTD or σZTD will be assigned a “null” value (indicating a missing or unacceptable value).
These checks are similar to those used by a screening of ZTD time series for IGS repro-products [3, 5]. However, it must be noted, that a range check on formal errors should be station-specific [3] and the time series have not passed all the procedures applied on reanalysis (e.g., EPN-repro2).
IGS sites’ metadata (site coordinates) are checked online by SEMISYS and corrected by proofed values from SEMISYS if the horizontal displacement exceeds 90 meters (equivalent to 3 arc seconds). Coordinate check avoids using erroneous AMSL values from a geoid model.
For IGS-repro3, although a reprocessed product, the same QC algorithm is applied for ZTD and σZTD values as in case of IGS daily. The epochs with data not passed QC are unexposed. The same site metadata and ancillary meteorological data from ERA5 are used for both IGS daily and IGS-repro3.
EPN-repro2 time series do not need any additional quality checks. All necessary quality checks are performed already during reprocessing.
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 [11] 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 [7] and in GAIA-CLIM D2.8, Annex IX, Product Traceability and Uncertainty for the GNSS IPW Product [12].
Comparing σZTD values between IGS and EPN-repro2, the user may notice remarkable differences. In the case of IGS, these values stay around 2 mm. At the same time, EPN-repro2 σZTD values can reach 5–6 mm. As a direct consequence of 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 [4] of the results provided by different ACs that use different software — Bernese, GIPSY, and GAMIT. While the ZTD values estimated by this software agree within around 3 mm, the σZTD values may differ up to three times. This is because the initial constraints for the models used by this software 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.
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 is calculated with fixed σZTD = 4.0 mm (as suggested by IGS [11]). 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).
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 in [13, 14], where mean values of IPW estimated from GNSS observations agree within 0.5 mm.
The IGS NRT (daily) dataset provides global coverage with relatively short latency (about 2-3 weeks) and, therefore it is not the ideal choice for climate studies. Reprocessed GNSS datasets, which account for instrument and data processing changes, are preferred for such research. Figure 3 illustrates the effect of correcting instrument-related biases on IPW for station BAMF (Bamfield, Canada, 48.84°N, -125.14°E).
Figure 3. Differences between IGS Near Real-Time (NRT) and ERA5 data, as well as IGS Repro3 and ERA5 data, both on an hourly and monthly basis, at the BAMF station. The most substantial discrepancies in the IGS NRT data become evident from March 2018 to February 2020. This noticeable change aligns with the installation of the new receiver, as indicated by the accompanying metadata on the right. Notably, such a significant shift is not observed in the reprocessed IGS time series.
For a thorough examination of comparison results, which encompasses IGS repro3, EPN repro2, IGS daily, RHARM, IGRA, GRUAN, and ERA5 datasets for the period 2000–2021, we recommend reading a paper authored by Rannat et al. [15].
The following section provides a concise summary of the comparison results obtained from IGS daily, EPN repro2, radiosonde data, and ERA5. The IPW estimation from 18 globally representative IGS stations was compared for the period 2014–2019 with the IPW values calculated from four independent co-located data sets (Fig. 4):
The IPW datasets from EPN repro2, GRUAN radiosonde and ERA5 were compared based on data collected in 2014 at Ny-Ålesund.
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 daily, GRUAN RS−IGS daily and IGRA−IGS daily 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 overall comparison techniques included ranges from 0.15 mm (ERA5–IGS daily) to 0.64 (MWR–IGS daily), while its average RMSD ranges from 0.91 mm (GRUAN RS–IGS daily) to 1.64 mm (IGRA–IGS daily).
Some researchers have reported a seasonal behavior of the GNSS–RS IPW bias [16, 17], others have pointed out only a dependency between its RMSD and latitude [18, 19] 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 to be similar to previous studies, with the 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 the standard deviation of differences occur in the summertime when IPW reaches its maximum. Based on GNSS and GRUAN RS data collected at NYA1 and TSKB, this kind of seasonal pattern in the standard deviation of IPW differences is mostly due to 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 daily | 18 | 46 894 | 0.15 | -0.9 to 0.93 | 1.46 | 0.93 to 2.6 |
GRUAN RS–IGS daily | 2 | 704 | 0.23 | 0.11 to 0.36 | 0.91 | 0.6 to 1.23 |
IGRA–IGS daily | 15 | 2 054 | 0.43 | -0.9 to 1.84 | 1.64 | 0.73 to 2.27 |
MWR–IGS daily | 1 | 3 120 | 0.64 | – | 1.21 | – |
Figure 5: IPW differences in IGRA−IGS daily, ERA5−IGS daily, MWR−IGS daily, and GRUAN RS−IGS daily 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 scopes of application, the results obtained using EPN-repro2 are comparable to the outcome derived from IGS tropospheric product. Considering only the year 2014, the average ERA5–IGS daily and GRUAN RS–IGS daily 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 daily. This is in line with expectations because IGS provides ZTD weekly with a delay of 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.
The CDS web interface provides data in two formats:
All CDS in-situ observations share a common data model (CDM). This format is described in the CDS documentation.
The GNSS product for IGS and EPN-repro2 and ancillary data used to retrieve GNSS IPW is stored in 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 also access the meteorological conditions at the same time as 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 are calculated by EGM2008 geoid model.
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 |
| latitude | Latitude deg. North |
| longitude | Longitude deg. East |
| height_of_station_above_sea_level | Altitude above mean sea level |
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. The numerical value of the 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. |
| uncertainty_value1 | 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 |
| uncertainty_value5 | Total 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 |
The data from both IGS and EPN networks is public and free for use. However, there exist some strong recommendations on the usage and citing of the products.
The terms of use of IGS products can be found in the 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.
Jupyter notebook(s) at the following location provide examples on how to read, plot, and reorganize the data:
In-situ observations GNSS: download-and-explore
[1] Thorne, P. W., Madonna, F., Schulz, J., Oakley, T., Ingleby, B., Rosoldi, M., Tramutola, E., Arola, A., Buschmann, M., Mikalsen, A. C., Davy, R., Voces, C., Kreher, K., De Maziere, M., and Pappalardo, G.: Making better sense of the mosaic of environmental measurement networks: a system-of-systems approach and quantitative assessment, Geosci. Instrum. Method. Data Syst., 6, 453–472, 2017, https://doi.org/10.5194/gi-6-453-2017.
[2] Selmke, I., Dach, R., Arnold, D., Prange, L., Schaer, S., Sidorov, D., Stebler, P., Villiger, A., Jäggi, A., Hugentobler, U. CODE repro3 product series for the IGS. Published by Astronomical Institute, University of Bern, 2020. URL: http://www.aiub.unibe.ch/download/REPRO_2020; DOI 10.7892/boris.135946.
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[5] 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.
[6] Bradke, Markus, SEMISYS - Sensor Meta Information System. GFZ Data Services, 2020, https://doi.org/10.5880/GFZ.1.1.2020.005.
[7] 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.
[8] 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.
[9] 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.
[10] Mateus, P., Catalão, J., Mendes, V.B., Nico, G., An ERA5-Based Hourly Global Pressure and Temperature (HGPT) Model, Remote Sens. 12, 1098, 2020.
[11] IGS, Atmospheric products: accuracy of ZTD, https://www.igs.org/products/#about.
[12] 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.
[13] 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.
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[15] Rannat, K.; Keernik, H.; Madonna, F. The Novel Copernicus Global Dataset of Atmospheric Total Water Vapour Content with Related Uncertainties from GNSS Observations. Remote Sens. 2023, 15, 5150. https://doi.org/10.3390/rs15215150.
[16] 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.
[17] 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.
[18] 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.
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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
This document has been produced in the context of the Copernicus Climate Change Service (C3S). The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of C3S on behalf of the European Union (Delegation Agreement signed on 11/11/2014 and Contribution Agreement signed on 22/07/2021). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose. The users thereof use the information at their sole risk and liability. For the avoidance of all doubt , the European Commission and the European Centre for Medium - Range Weather Forecasts have no liability in respect of this document, which is merely representing the author's view. |
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