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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), Olivier Bock (IGN)

Issued by: CNR-IMAA / Fabio Madonna

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Acronyms

Introduction

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.

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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 observations and products 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 formal IGS ZTD product, used for retrieval of IGS IPW Daily, is produced by 12 licensed Analysis Centres (ACcomputed by a single AC (USNO).

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).

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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.

Data and metadata sources

 IGS-daily

The daily (NRT) tropospheric products for IGS sites (Fig. 1) are publicly available through the CDDIS data portalhttps://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 after 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].

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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 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 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).

IGS-repro3

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. From 2019 to 2020, the IGS coordinated its third reanalysis of the complete history of GNSS data collected by the IGS global network since 1994. Ten ACs participated in this so-called “Repro3” effort, using the latest models and methodologies (https://igs.org/news/repro3-solutions-now-available/). Several of them provided estimates of ZTD, which can be accessed at https://cddis.nasa.gov/archive/gnss/products/repro3. An intercomparison of four ZTD data sets was conducted by [5]. The repro3 product chosen for CDS is a precise point positioning (PPP) solution provided by the Center for Orbit Determination in Europe (CODE) [2]. It covers the period 2000-2020 and includes 493 stations. The combination was done for station positions for the realization of ITRF2020.

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, 56]. Data reprocessing removes all estimated avoids inhomogeneities due to changes in the processing methodology. However, it does not always remove shifts (biases) from GNSS antenna replacements [5due to GNSS equipment changes (e.g. antenna replacements). This can only be achieved in a post-processing step, so-called homogenization [7, 8]. Therefore, this data record can be used as a an interim 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 . Monitoring trends and the variability in atmospheric water vapour are accessible after complete homogenization that will be made available in a future release.

EPN-repro2

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/. 

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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 [54]. 

Metadata

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 "metadata" needed here are the station positions. They are used to extract the meteorological data (ground surface pressure, p₀, and the water-vapour-weighted mean temperature of the atmosphere, T_m) at the location of the GNSS stations, for the ZTD to IWV conversion. The station positions can be found in the so-called GNSS "logfiles" and also in the SINEX TRO files (described in M311a_Lot3.3.2.3_2020, Section 4.1 and athttps://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 Ancillary meteorological data are needed to estimate GNSS IPW based on ZTD , ancillary meteorological data are needed[5]. 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, T_m, 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 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).

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• 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 [69] (step 3). Due to the fact that the site latitude and longitude are given with height from the WGS84 ellipsoid, the AMSL ( height above the mean sea level (AMSL) 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 (T_m) is calculated and T_m 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 T_m and ground surface pressure (p₀) in time. On the other hand, this is done in the case of EPN-repro2 since its data are provided at half-hours. Depending on dry gases between the receiver and satellite, the ZHD is calculated using p₀, latitude, and height of the site above the mean sea level AMSL (step 6). The ZWD, on the other hand, depends on the 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 T_m (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 [710], and accounts accounting 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 [811]. In case of uncertainties for p₀ and T_m, these values, statistically determined using IGRA and GRUAN radiosonde measurements, are in good agreement agree with the results presented in several papers specifically focusing on ERA5 [912, 10 13]. After the values for IPW and its uncertainty are calculated, the results are ingested into the database (step 10).

Data validation process

 IGS

IGS daily

The IGS ACs guarantee 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 [710]) the data processing chain for CDS comprises additional steps as follows:

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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)This is the case with σ_ZTD < 2.5 · median; the median is computed for each station.

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.

IGS-repro3

For IGS-repro3, although a reprocessed product, the same QC algorithm is applied for ZTD and σZTD values as in the 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

EPN-repro2 time series do not need any additional quality checks. All necessary quality checks are performed already during reprocessingcombination [4]. 

Unlike many other measurements in environmental physics (atmospheric temperature, humidity, etc.), the GNSS IPW is retrieved from GNSS ZTDs characterised with characterized by uncertainties expressed in terms of formal (not instrumental) errors.

The characteristic values depend on GNSS data processing software and the . The IGS specifies σ_ZTD limits to 4 mm [1114] for observations in mostly ideal conditions with instrumental installations following strict IGS technical requirements. The sources of uncertainties contributing to the final σ_IPW are extensively analysed analyzed in Ning et al., 2016 [710] and in GAIA-CLIM D2.8, Annex IX, Product Traceability and Uncertainty for the GNSS IPW Product [1215].

Comparing The user may notice remarkable differences when comparing σ_ZTD 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 σ_ZTD 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 mmkg/m². 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 uses differ (a dedicated reader may refer to the software manuals). The Bernese and GIPSY show σ_ZTD values usually around 1.5–2 mm while σ_ZTD provided by the GAMIT is around 4–5 mm. Eventually, the higher σ_ZTD values from GAMIT lead to relatively higher values of σ_ZTD in EPN-repro2.

The differences between σ_ZTD values derived from different software is a reason why, in addition to using σ_ZTD as given by data providers, the uncertainty of GNSS IPW product is calculated with fixed σ_ZTD = 4.0 mm (as suggested by IGS [1114]). While also σ_ZTD values 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 [16] (for example, GNSS versus VLBI, MWR or radiosonde).

An extensive analysis by comparing the differences from between diverse GNSS data processing software and data processing strategies on GNSS IPW values itself can be found in [1317, 14 18], where mean values of IPW estimated from GNSS observations agree within 0.5 mm.

Examples

Impact of reprocessing

The IGS NRT ( daily ) dataset provides global coverage with relatively short latency (about 2-3 weeks) and, therefore dataset is an operational product which can show inhomogeneities due to changes in the processing methodology and unreported or recent reporter changes in station equipment. 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).

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Figure 3. Differences between IGS Near Real-Time (NRT) daily 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 daily 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. 

Comparison with third-party data

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. [1519].

The following section provides a concise summary of summarises 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):

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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 a 2 km radius (in red) and (2) within a 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 kg/m² (MWR–IGS daily), while its average RMSD ranges from 0.91 mm (GRUAN RS–IGS daily) to 1.64 mm kg/m² (IGRA–IGS daily).

Some researchers have reported a seasonal behavior behaviour of the GNSS–RS IPW bias [1620, 17 21], ; others have pointed out only a dependency between its RMSD and latitude [1822, 19 23] without seeing a clear an apparent effect on biases. Based on the presented examples, there is no dependency between the IPW difference and latitude. On the contrary, a A strong correlation between IPW difference, RMSERMSD, 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 primarily 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 σ_ZTD = 4 mm as claimed by IGS and (2) using σ_ZTD values provided by CDDIS. The annual average uncertainties of GNSS-estimated IPW when using σ_ZTD = 4 mm in its calculations are 0.66 and 0.72 mm kg/m² at NYA1 and TSKB. If using σ_ZTD values provided by CDDIS, the uncertainty decreases to 0.43 and 0.49 mm kg/m², respectively. At the same time, GRUAN RS-estimated average IPW uncertainty at these stations was 0.31 and 1.07 mm kg/m². 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.