Keywords

1 Introduction

Integrated Water Vapour (IWV) plays a fundamental role in several weather processes that deeply influence human activities. Retrieving IWV content in the atmosphere can be performed in different ways using independent techniques: from the traditional ones like radiosondes and ground-based microwave radiometers, up to the recent ones based on satellite techniques. In particular, the GNSS-based tropospheric Zenith Total Delay (ZTD) estimates allow inferring IWV values with high accuracy equivalent to that expected from direct observational techniques, such as radiosondes and microwave radiometers (Bonafoni et al. 2013; Van Baelen et al. 2005; Calori et al. 2016). Several studies have been devoted to the use of GNSS stations for the estimation of IWV over South America. Bianchi et al. (2016) estimated mean IWV based on GNSS data (IWVGNSS) and its trends during 2007–2013 over more than a hundred GNSS tracking sites from SIRGAS-CON. Calori et al. (2016) analysed a period of 45 days where deep convective processes with hail precipitation took place over Mendoza province, in the Central-Western Argentina (CWA). For this assessment, the authors used IWVGNSS maps to draw insight into the accumulation and influence of humidity over the region. Even fewer studies have performed a validation of the IWVGNSS; for this, Fernández et al. (2010) used radiosonde data from four locations over Central-North Argentina in order to validate IWV estimates from Global Positioning System (GPS) stations during a 1-year period (2006–2007). The authors found an agreement between IWVGNSS and IWV estimated through radiosonde data (IWVRS), with differences as large as 3 kg/m2. Llamedo et al. (2017) used GPS-derived IWV to analyse moisture anomalies over South America during El Niño-Southern Oscillation phases, finding positive anomalies over northern Argentina during El Niño events.

Camisay et al. (2020) estimated IWVGNSS time series for a 4-year period (2015–2018), to assess the accuracy through a comparison in two GNSS Argentinean stations with radiosonde observations and explore the role of IWV in the development of regional precipitation events over the CWA. The obtained agreement between IWVGPS and IWVRS was close to 2 kg/m2 in terms of mean absolute error. In Latin-American region, in situ meteorological observations are scarce; therefore, GNSS atmospheric monitoring has significant relevance for the understanding of regional meteorological processes. This kind of information is extremely valuable, and it can be used to achieve a better knowledge of IWV variable in the study region.

The GNSS allows monitoring the IWV from a network that surpasses traditional techniques due to its significant temporal and spacial density. This is of interest to study the regional trends of the climatic variable for which it is necessary to have a long time series by site and region. On the other hand, the ZTD can be estimated in real-time and near real-time mode, in order to be assimilated in regional forecast models.

Fig. 1
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SIRGAS GNSS stations and radiosonde sites considered in this study

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SIRGAS (Sistema de Referencia Geocéntrico para las Américas) is the geocentric reference frame in Latin America and the Caribbean. It is at present given by a network of about 420 continuously operating GNSS stations (Cioce et al. 2018) (Fig. 1). These stations are routinely processed by the SIRGAS Analysis Centres (AC), following the guidelines and standards set up by the International Earth Rotation and Reference Systems Service (IERS) and International GNSS Service (IGS). Since 2014, the routine GNSS data processing includes the estimation of hourly ZTD values based on GPS and GLONASS observations (Camisay et al. 2020; Sánchez et al. 2015; Brunini et al. 2012).

Pacione et al. (2017) shows the great potential that a continental GNSS network offers in atmospheric studies. EUREF Permanent Network (EPN) (Bruyninx et al. 2019) had been used as a valuable database for the development of a climate data record of GNSS tropospheric products over Europe. It had been used as a reference in the regional numerical weather prediction reanalyses and climate model simulations and had been used for monitoring IWV trends and variability. Guerova et al. (2016) showed and discussed the advantages of the application of GNSS tropospheric products in operational weather prediction and in the climate monitoring.

In this contribution, we report on the estimation and validation of the ZTD and IWV values in Latin America GNSS stations, using as input data the ZTD values obtained in: (1) the operational processing of the SIRGAS regional reference frame and (2) applying the Precise Point Positioning (PPP) approach, with two softwares, BKG NTRIP Client (BNC) and Bernese v5.2. (BSW52). To assess the reliability of our results (ZTD and IWV values), they are compared with the operational IGS products (ZTDIGS), IWV values extracted from radiosonde profiles (IWVRS) and ZTD estimations inferred from integrate the correspondent radiosonde profile data (ZTDRS).

In Sect. 2, the methodology used in operational SIRGAS processing to estimate ZTD product is reviewed. ZTDSIR internal consistency is presented. ZTD products estimated by PPP in SIRGAS stations are reviewed. Section 3 summarises the ZTDSIR and IWVSIR products validation with respect to ZTDIGS products and IWV radiosonde data. Conclusions, outlook and future work are given in Sect. 4.

2 Methodology

2.1 Estimation of ZTD Values Based on the Operational SIRGAS Processing (ZTDSIR)

The ZTD estimations based on the operational SIRGAS GNSS processing (ZTDSIR) are routinely calculated for all the SIRGAS-CON stations (Fig. 1) by the SIRGAS Analysis Centres (AC) for a 5-year period (2014–2018). The eight official SIRGAS-AC (Table 1) used Bernese GNSS Software v5.2 (BSW52, Dach et al. 2015).

Table 1 SIRGAS Analysis Centres (AC) that estimated ZTD for the period 2014–2018
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Table 2 Models used for the ZTD estimation for the operational SIRGAS products and the testing PPP products
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Table 3 Rejected ZTD estimates (σZTD > 0.02 m)
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The SIRGAS operational ZTD products (ZTDSIR) are calculated with the final IGS products (orbits and earth rotation parameters, ERP). Table 2 summarizes the methodology implemented for the operational SIRGAS products and the testing PPP products.

Each SIRGAS-AC processes a different sub-network of SIRGAS GNSS stations. The distribution of the stations considers that each station parameter (ZTDi) is available in three different solutions, so it is possible to evaluate the internal consistency and generate the final combined ZTD products (ZTDSIR).

The ZTDi variance is used as a filter (σZTD > 0.02 m), prior to the combination. The 5% of the ZTDi values are rejected in the analysed period. Table 3 shows the number of rejected estimates (in %) for each AC.

2.2 ZTDSIR Internal Consistency

A weighted least-squares combination scheme using the inverse of the input data variances (σZTD) as a weighting factor is implemented to estimate ZTDSIR products. Figure2 shows a detail per year of the number of stations in which the ZTDi data (3 or more solutions available, with σZTD < 0.02 m) are combined (Nc) compared to the number of stations that had only one solution. For the years 2015–2018 it was possible to have a data redundancy in more than 75% of the stations.

Fig. 2
figure 2

Number of stations in which ZTDi data were combined vs stations with one solution

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The internal consistency of the ZTDSIR values is evaluated considering the residuals of each contributing ZTD solutions with respect to the combined ZTD value (ZTDi-ZTDSIR). After a weighted least squares combination process, rms of each ZTDSIR parameters are determined. A mean rms is calculated per station and per year (Table 4). The mean rms is less than 1 mm in more than the 84% of the estimated values in the period 2014–2018 (Fig. 3).

2.3 ZTDSIR Validation with IGS Tropospheric Products

For validation, the ZTD final products (ZTDSIR) are compared with the operational IGS (Byram et al. 2011; Byun and Bar-Sever 2009) products (ZTDIGS) at 15 GNSS stations. Figure 4 shows both ZTD time series in two selected stations, AREQ (16.46 °S; 71.49 °W; 2488.92 m.) and OHI2 (63.32 °S; 57.90 °W; 32.47 m.) in the study period (Jan 2014–Dec 2018).

Table 4 Summary of combination process statistics
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Fig. 3
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Distribution of the ZTDSIR mean rms per year (2014–2018)

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Fig. 4
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Time series of ZTDSIR (grey) and ZTDIGS (black) values at two selected SIRGAS stations, AREQ (Arequipa, Peru) and OHI2 (O’Higgins, Antartica), period: Jan 2014–Dec 2018

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2.4 ZTDSIR Validation with Radiosonde Data

ZTDSIR also, are compared with ZTD values calculated from data of 10 radiosonde stations (ZTDRS). Table 5 details characteristics of the RS used.

The ZTDRS are calculated from the precipitable water for entire sounding (IWVRS), data extracted from radiosonde profiles available at Wyoming Weather Web-University of Wyoming (http://weather.uwyo.edu/upperair/sounding.html). First, ZWDRS values were calculated by Askne and Nordius (1987) with the physical constants for atmospheric refractivity from Rüeger (2002) (Eq. 1). The mean temperature of the atmosphere (Tm) used in (1) is calculated integrating the radiosonde profiles data (temperature and dew-point) in each level profiles up to GNSS station height (Eq. 2). The zenith hydrostatic delay values at the RS sites (ZHDRS) are obtained according to Davis et al. (1985) (Eq. 3), where pressure is calculated to the GNSS height (PGNSS) from pressure radiosonde data. An adaptation to the standard pressure model of Berg (1948) to correct for the height differences is applied (Eq. 4). Finally, ZTDRS values are calculated by adding ZHDRS to ZWDRS

$$ \vspace*{-2pt}ZWD=\frac{\left(22,9744+\frac{375463}{Tm}\right)0,4614991785}{10^5} IWV \vspace*{-18pt} $$
(1)
$$ Tm=\frac{\int_H^{\infty }e/T\ dz}{\int_H^{\infty }e/{T}^2\ dz} \vspace*{-20pt} $$
(2)
$$ \fontsize{9}{11}\selectfont{\begin{array}{lll}ZHD=0,002276738.\dfrac{P_{\it GNSS}}{1-0,00266.\cos \left(2\varphi \right){-}0,28.10^{-6}.{h}_{\it GNSS}}\end{array}} \vspace*{-18pt} $$
(3)
$$ {\mathrm{P}}_{GNSS}={P}_{RS}{\left(1-0.0000226\ \left({h}_{GNSS}-{h}_{RS}\right)\right)}^{5.225} $$
(4)

2.5 ZTD Estimation Applying PPP

In order to have a product in near real time to be used in numerical weather prediction model, we tested the Precise Point Positioning processing technique (ZTDPPP). Two case of study are analysed

  • Case 1: Feb 21 to Mar 27 (36 days), 2016; ten GNSS stations (located in the central-western region of South America).

  • Case 2: Jan 1 to Dec. 31, 2019 (365 days); thirty GNSS stations (located in Argentina).

The ZTDPPP values estimated are compared with the corresponding ZTDSIR values.

This estimation approached with two softwares, BNC (Weber et al. 2016) and BSW52, in the first case study. PPP with BSW52 showed better results (not shown). In the second period (year 2019) we decided to estimate ZTDPPP only by BSW52. In both cases of study, with BSW52 PPP, rapid IGS products (orbits, ERP and satellite clock corrections) were used so the ZTDPPP were estimated with 24 h delay. Table 2 summarizes the input data, models and main configuration used for each software.

Table 5 Location of ten RS stations used (bold used in ZTDPPP validation), distance to GNSS sites, and heights (hGNSS and hRS)
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2.6 Determination of IWV Values from GNSS-Based ZTD Estimates

The GNSS-based ZTD values are used to calculate the IWV applying the ratio of Askne and Nordius (1987) to the wet component of the delay (ZWD), (Eq. 1). In this work, the ZTDSIR and the one from applying PPP (from BSW52) were used. ZWD values were obtained by removing the ZHD, which was calculated according to Davis et al. (1985) (Eq. 3). Sea level pressure values (Pref) were extracted from the ERA-Interim products and were reduced to the height of the GNSS stations (PGNSS) following Berg (1948) (Eq. 5).

$$ {P}_{GNSS}={P}_{ref}.{\left(1-0,0000226.\left({h}_{GNSS}-{h}_{ref}\right)\right)}^{\mathrm{5,225}} $$
(5)

In this case, the weighted mean temperature of the atmosphere (Tm) was calculated in accordance with Mendes (1999) using the surface temperature (Ts) also provided by ERA-Interim. The values for the refractivity constants were taken from Rüeger (2002). Following this strategy, IWVSIR series from a 5 years (2014–2018) period were estimated in each SIRGAS station. We generated four daily IWV maps by Hunter (2007) (at 00:00, 06:00, 12:00 and 18:00 UTC) for the entire SIRGAS region, see some examples in Fig. 5 (24-6-2018).

Fig. 5
figure 5

Maps of IWV inferred from the ZTD estimates produced within the operational SIRGAS processing (24-6-2018; 00,06,12 and 18 hs UTC)

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The IWVSIR values were tested in the 10 radiosonde stations selected (Table 5). The Figs. 6 and 7 show the comparison of IWVSIR (inferred from ZTDSIR) values with values obtained from radiosonde profiles (IWVRS) at two SIRGAS stations: MZAC (located in an arid region) and IGM1 (located in a humid region), respectively.

Fig. 6
figure 6

IWVSIR (MZAC GNSS station) and IWVRS (RS: SAME)

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Fig. 7
figure 7

IWVSIR (IGM1 GNSS station) and IWVRS (RS: SAEZ)

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3 Results

3.1 ZTDSIR Validation

Our results presented a quite good agreement with the IGS products (see Fig. 4). Discrepancies between ZTDSIR and ZTDIGS values are compared at 15 IGS (SIRGAS) stations (Fig. 8). The results present a mean root mean square (rms) value of 6.8 mm (0.29% of the mean ZTD) with a negative mean bias of 1.5 mm (0.07% of the mean ZTD).

Fig. 8
figure 8

Comparison of ZTDSIR and ZTDIGS values at 15 selected SIRGAS stations (Jan 2014–Dec 2018)

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The comparison of ZTDSIR w.r.t. ZTDRS is also very promising: discrepancies computed at 10 radiosonde stations (see Fig. 1 and Table 5) have a mean rms of 7.5 mm (0.32% of the mean ZTD) and a negative mean bias of 2 mm (0.09% of the mean ZTD). An analysis of the radiosonde types has been started at each analysed site, which it could be the cause for the negative bias in line with the results of Wang et al. (2007) and Pacione et al. (2017).

3.2 ZTDPPP Products Validation

Analysing the ZTDPPP products, the BSW52-based ZTDPPP estimates showed a better agreement than the BNC-based ZTDPPP estimates with respect to the corresponding ZTDSIR values. The rms and bias are the two indexes for the evaluation of the two estimations. Results of these two-test data set are shown in Table 6. BNC-based ZTDPPP estimates were less accurate as expected because real time IGS product were used. It may also be a consequence of the fact that ZTDSIR and the BSW52-based ZTDPPP use the same models to determine the tropospheric parameters. In the case 2 a bias-reduction scheme was implemented on a monthly basis as applied in Douša and Vaclavovic (2014).

The comparison of the BSW52-based ZTDPPP estimates and ZTDSIR values at two selected SIRGAS station, EBYP (in a subtropical region) and MGUE (in an arid region), with the data in the case 1, are shown in Fig. 9.

The discrepancies between the ZTDPPP values estimated in the second case of study (Year 2019, 30 stations) with the respectively ZTDSIR values were also very promising (Fig. 10). The mean rms and mean bias per station is shown in the Fig. 10. The 84% of the stations had a mean rms < 28 mm and the rest 16% had a mean rms < 31 mm.

In five GNSS stations, the BSW52-based ZTDPPP estimates were validated with respect to ZTDRS (detailed in bold in Table 5). Figure 11 shows this comparison in the IGS (SIRGAS) station CORD in the centre of Argentina, as an example.

3.3 IWVSIR Validation

The IWVSIR validation with IWVRS also showed agreement. The results for a period of 5 years, in 10 RS – GNSS locations yielded a mean bias 0.41 kg/m2 and a mean rms 3.5 kg/m2. The correlation coefficient of the two series (IWVSIR and IWVRS) presented in Fig. 12 is 0.94, which indicates a very good agreement between both estimations.

Table 6 Comparison of ZTDPPP values with the operational SIRGAS processing (ZTDSIR)
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Fig. 9
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Comparison of ZTDSIR and BSW52-based ZTDPPP values at two selected SIRGAS stations

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Fig. 10
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Comparison of BWS52-based ZTDPPP with ZTDSIR at 30 GNSS station (365 days)

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Fig. 11
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Time series of ZTDSIR, BWS52-based ZTDPPP and ZTDRS values at a GNSS station located in Cordoba (Argentina)

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Fig. 12
figure 12

Scatter plot comparing IWV values inferred from GNSS-based ZTD estimates (IWVSIR) and radiosonde profile data (IWVRS) at two selected SIRGAS stations (Jan 2014 to Dec 2018)

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In the other hand, the comparison of IWVPPP (calculated from the BWS52-based ZTDPPP values) with IWVRS, produces discrepancies with a mean rms of 1 kg/m2, a standard deviation of 0.73 kg/m2 and a bias of 2.37 kg/m2 (not shown).

4 Conclusions

Latin America has SIRGAS network, an infrastructure of GNSS stations that generates ZTD (per hour), offering regional and continental coverage that can be used in atmospheric studies.

The internal consistency of the ZTDSIR values, calculated by SIRGAS ACs, have been evaluated for a period of 5 years (2014–2018). An average rms less than 1 mm, in more than the 84% of the values, indicate the rigorous weighted least squares combination process implemented to get the SIRGAS reference products.

The ZTDSIR series for a 5-year period have been validated with two different time series. They agree with the corresponding values of the ZTD series obtained by the IGS (mean rms 6.8 mm; mean bias −1.5 mm) as well as those from the radiosonde technique (mean rms 7.5 mm; mean bias −2 mm).

The ZTD obtained by PPP with BSW52, using the RAPID CODE products (ephemeris and clock corrections) are validated with respect to the post-processing products ZTDSIR. The mean rms of the differences is 22 mm (84% of the stations had a mean rms < 28 mm) for an annual case of study (2019, 30 stations). It remains to continue improving the methodology to increase accuracy and decrease the positive bias that on average resulted in 2 mm (0.07% of the ZTD mean value in the stations evaluated). Anyway, these accuracy of ZTDPPP complies with the threshold requirements for the operational NWP nowcasting – the relative accuracy of 5 kg/m2 in integrated water vapor (IWV) and 30 mm in ZTD when approximating the conversion factor defined by Bevis et al. (1994) and Douša and Vaclavovic (2014). However, we must work to obtain a product in near real time (with 90 min of latency), applying ultra-rapid orbits and clocks, or even better using real-time corrections (Guerova et al. 2016).

The publication of this new product from SIRGAS opens the opportunity for new research topics that can be carried out both continentally and regionally in Latin America. As an example, it has been shown that SIRGAS ZTD products can be used to calculate the IWV over SIRGAS stations, thus providing IWV with a spatial and temporal density not existing in Latin America by conventional techniques. This variable has also been validated with radiosonde data (mean correlation coefficient 0.89, in 10 compared sites). SIRGAS ZTD products can be used as a reference for different scientific applications (e.g. validation of regional numerical weather prediction reanalyses) and they could be used for monitoring trends and variability in atmospheric water vapour in Latin America region, similar than EUREF Permanent network (Pacione et al. 2017).