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Assessing the Tropospheric Impacts on Positioning Accuracy Using

IGS02 Real-Time Service Data Versus Long-Convergence Static

PPP in Gwagwalada, Abuja, Nigeria

Atoki, Lucas Olu., Ono, Matthew N., Ibraheem, Sikiru Temitope

Department of Geoinformatics and Surveying, Nnamdi Azikiwe University, Akwa, Anambra State, Nigeria.

DOI: https://doi.org/10.51583/IJLTEMAS.2024.130817

Received: 01 September 2024; Accepted: 06 September 2024; Published: 17 September 2024

Abstract: The International Association of Geodesy (IAG) has established the International GNSS Service-Real Time Service

(IGS-RTS) as a service provider, offering real-time access to precise products like orbits, clock corrections, and code biases

regarding satellite navigation and positioning system. These products serve as an alternative to ultra-rapid products in real-time

applications. The performance of these products is assessed through daily statistics from Analysis Centres, which compare them

to IGS rapid products. However, the accuracy of GPS real-time corrections for satellites during eclipsing periods was slightly

reduced, attributed to the impact of environmental factors on the services. The speed of GNSS signals can be impacted by various

atmospheric factors, including troposphere, temperature, pressure, and humidity, resulting in positioning inaccuracies and even

giving rooms for signal jamming and hijacking. However, the unique weather conditions prevalent in the African continent are

often overlooked during the development of error mitigation parameters and algorithms, which can lead to reduced accuracy in

GNSS positioning in a region like Nigeria. The purpose of this study is to estimate the tropospheric impact on positioning with

IGS02 Real Time Service data compared to long convergence Static-PPP in Gwagwalada Area Council, Abuja, Nigeria. The

study adopts the determination of the GNSS Static observations (minimum of two hours per session) on the chosen stations as

standard, determination of the IGS-RTS data observations using RTKLIB software; observations were done with IGS-RTS data

stream of IGS02 and statistical tests were performed. The GNSS Static coordinates and IGS-RTS coordinates were validated from

error due to troposphere, temperature, pressure, etc., with the computation of their mean horizontal and vertical uncertainties

which have a similar level of accuracy but slightly differ at centimeter levels. The result shows the Root Mean Square (RMS)

Error discrepancy of IGS02 at the Wet and Dry season, as compared with the Static-PPP was within 0.065(m) and 0.046(m)

respectively.

Key Words: IGS-RTS Data, Static-PPP, Tropospheric impact.

I. Introduction

The International GNSS Service (IGS) was established in 1994 by the International Association of Geodesy (IAG) as a service

provider, and since then, researchers have persistently identified and addressed existing gaps. Originally named the International

GPS Service for Geodynamics, the organization underwent a name change in 1999 to International GPS Service, acknowledging

the growing scope of GPS applications and functions in the scientific field. The International GNSS Service (IGS) has rebranded

to reflect its broader mission, which now includes integrating multiple Global Navigation Satellite Systems (GNSS) beyond just

GPS. This expansion, formalized in 2005, recognizes the important contributions of GLONASS, GALILEO, BeiDou (developed

by China), and QZSS (monitored by Japan), as discussed by (Bahadur and Nohutcu 2020; Charles 2022). As scientists explored

the technology's potential for various applications, numerous organizations recognized the vast possibilities offered by its precise

positioning capabilities at a relatively low cost. Consequently, it became clear that no single entity could bear the significant

capital investment and ongoing operational expenses required to maintain a global system of this scope. In response to this

realization, major international organizations formed a collaborative partnership to foster global cooperation, establish unified

standards, and ensure the achievement of their shared objectives. This collective effort aimed to promote exceptional scientific

accomplishments and guarantee the success of their endeavors. For years, Global Navigation Satellite Systems (GNSS) have been

utilized for positioning and navigation, offering continuous, weather-resistant real-time information. Although many errors can be

easily corrected using techniques like differencing or precise point positioning, atmospheric refraction remains a significant

challenge in GNSS positioning. As noted by Nzelibe, Tata, and Idowu (2023), GNSS signals traveling from satellites to receivers

near the Earth's surface are affected by tropospheric errors, causing signal slowing and refraction. This leads to substantial

positioning errors, ultimately reducing accuracy. The tropospheric delay is a complex error that poses significant challenges in

space geodetic techniques, particularly affecting the accuracy of height measurements. As a result, it is a pressing concern in

applications requiring high-precision positioning, such as monitoring sea levels, mitigating earthquake hazards, and studying plate

tectonic margin deformation. According to Faruna and Ono (2019), improving tropospheric delay modeling is essential to achieve

the necessary level of accuracy in these critical fields.

The Tropospheric delay is influenced by the receiver's elevation and altitude, and is dependent on various atmospheric conditions

including temperature, pressure, and humidity. The temperature gradient, which affects the delay, varies with height, season, and

geographical location. To compensate for this delay, several Global Tropospheric Models, such as the Saastamoinen, Hopfield,

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and Neil models, have been developed and implemented in GPS timing receivers, as demonstrated by Tsebeje and Dodo (2019).

In the realm of GNSS, the troposphere and temperature exert distinct influences on signal propagation. Temperature, in particular,

has a multifaceted impact on GPS signals. Notably, the precision of real-time corrections for GPS satellites during eclipsing

periods was somewhat compromised. Furthermore, the accuracy of corrections for eclipsing GLONASS satellites was

substantially lower compared to other satellites as assessed by (Jeffrey 2015; Byung, Kyung and Sang 2013; Cai and Gao 2013).

As a result, the decline in accuracy can be attributed to the impact of climate on these services. GNSS signals are susceptible to

atmospheric conditions such as temperature, pressure, and humidity, which can alter their speed and lead to positioning errors.

Notably, Africa is often overlooked in the design of error mitigation strategies, despite its unique weather patterns. Unlike other

continents with more temperate conditions, Africa primarily experiences hot and humid weather year-round, with only dry and

wet seasons.

Gwagwalada, Nigeria experiences a relatively consistent temperature range throughout the year, fluctuating between 63°F and

95°F, with rare instances below 57°F or above 102°F. This region is the second hottest in Nigeria, after Adamawa and Sokoto

States. The hot season, spanning from November to April, is characterized by average daily highs above 92°F. March stands out

as the hottest month, with average highs reaching 94°F and lows of 73°F, making Gwagwalada an ideal location for this study

due to its distinct temperature profile.

The accuracy and performance of the PPP-based positioning solution utilizing the real-time IGS-RTS service are currently being

assessed and analyzed by numerous researchers in both static and kinematic modes, as highlighted in studies by Elsobeiey and

Al-Harbi (2015) and El-Diasty and Elsobeiey (2015). While the International GNSS Service (IGS) suggests that the Real-Time

Service (RTS) provides orbit and clock parameters with an accuracy of 5cm and 0.5 nanoseconds (approximately 15cm), various

studies have found that this is not always the case. For example, research by Hadas and Bosy (2015) revealed that GPS orbit and

clock errors can reach up to 30cm and 20cm, respectively, in different regions worldwide. Furthermore, GLONASS orbit and

clock errors can be even higher, reaching up to 50cm and 75cm, respectively. In a study on the feasibility of using IGS-RTS for

maritime applications, El-Diasty and Elsobeiey (2015) reported mean and maximum errors of 0.07m and 0.22m, respectively.

Additionally, they achieved a 2-dimensional horizontal accuracy (RMS) of 0.08m at a 39% confidence level and 0.19m at a 95%

confidence level. These findings highlight the importance of surveyors and geodesists verifying the achievable positioning

accuracy in their specific location to determine the reliability of RTS data for their purposes. This research focuses on assessing

the accuracy of RTS-IGS02 in the context of Gwagwalada's climate. To achieve this, we conducted a study in the Gwagwalada

Area Council, Abuja, Nigeria, where we determined the positions of six ground control points (GCPs) using both IGS-RTS and

differential static GPS methods, and subsequently analyzed the results to evaluate the achievable accuracy.

Study Area

This study was conducted in Gwagwalada Area Council, located in the Federal Capital Territory (FCT) of Abuja, Nigeria.

Gwagwalada is one of the six administrative Area Councils in the FCT. Geographically, it is situated in the north-central region

of Nigeria, bounded by latitudes 8.05515211N to 9.0113411N and longitudes 6.05113611E to 7.01113511E (as shown in Figure

1.1). The area spans approximately 1,043 square kilometers.

Fig. 1.1: Study area in Gwagwalada, Nigeria.

IGS-Real Time Service Data

The Real Time Service Products provide corrections to the broadcast ephemeris, including GNSS satellite orbit and clock

adjustments. These corrections are formatted according to the RTCM State Space Representation (SSR) standard and transmitted

via the NTRIP protocol. As noted by (Kazmierski, Sośnica, and Hadas 2017; Wenju, Jin, Lei, and Ruizhi 2022), the corrected

orbits are referenced to the International Terrestrial Reference Frame 2008 (ITRF08), ensuring a precise and standardized

framework for real-time positioning. The combined solution provided by processing the individual Real-Time solutions from

Real-time Analysis Centers (RTAC), are the product streams readily available in the RTS. (www.igs.org/rts/products). The three

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official products currently include corrections to the GPS satellite orbits and clocks, such as IGS01, IGS02 and IGS03, Bingbing,

Urs, Junping, Inga, and Jiexian (2019).

Tropospheric Delay

The studies by Dodo, Ekeanyanwu, and Ono (2019) and Lu et al. (2017) reveal that the troposphere's impact on GNSS signals

manifests as an extra delay in signal propagation from the satellite to the receiver. This delay is attributed to changes in

tropospheric conditions, including humidity, temperature, and atmospheric pressure, as well as the geographical locations of the

transmitter and receiver antennas, as highlighted by Olayemi et al. (2015). The ability to account for Tropospheric delay enables

differential GNSS and RTK systems to correct for this error. Additionally, GNSS receivers can utilize Tropospheric models to

predict the magnitude of error caused by Tropospheric delay. According to Osah, Acheampong, Fosu, and Dadzie (2021), the

primary sources of errors in GNSS positioning are satellite clock bias, receiver clock bias, satellite orbit errors, multipath effects,

and atmospheric interference, including ionospheric and tropospheric delays. Tropospheric delay is evaluated in the zenith

direction over the GPS station, hence the term Zenith Tropospheric Delay (ZTD), which is the combination of the Zenith Dry

Delay (ZDD) and Zenith Wet Delay (ZWD). The tropospheric delay is expressed as the sum of two components. The Hydrostatic

component which is also known as Dry part, and the other one is Nonhydrostatic component also known as Wet part, i.e. (ZTD) =

(ZDD) + (ZWD), as detailed by Michal and Andrzej (2013); Mohd and Kamarudin (2007).

The Tropospheric delay is affected by the receiver's height and atmospheric conditions like temperature, pressure, and humidity.

Unlike the ionospheric delay, which can be reduced by combining L1 and L2 signals because it varies by frequency, the

Tropospheric delay remains constant across frequencies and cannot be removed by combining observations, making it a more

persistent source of error. Research by Dodo et al. (2019, 2015) highlights the use of various Tropospheric models, such as

Saastamoinen, Hopfield, and Niell, in GPS timing receivers to correct for Tropospheric delay. However, as Pan and Guo (2018)

point out, these global models can be flawed due to daily variations in temperature, pressure, and humidity, leading to errors in

calculated Tropospheric delays. Moreover, Nigeria's location near the equator and in the tropics makes it particularly susceptible

to significant Tropospheric effects, which can degrade GPS signal quality and impact precise point positioning, as noted by Ana

(2011).

To estimate the accuracy of positioning using IGS-RTS data, it's crucial to examine how the troposphere affects the network

system using global Tropospheric models, as emphasized by Zhao, Cui, and Song (2023). This research utilizes the Refined

Saastamoinen model, a global Tropospheric delay model, to investigate this impact and improve positioning precision.

Mathematical Analysis of the IGS_RTS Corrections

A broadcast orbit using the RTS satellite position (







) correction can be corrected as given by Kim and Kim (2015);





Orbit





broadcast

−





(2.1)

Where







is the RTS satellite position correction expressed in earth-centered earth-fixed (ECEF) coordinates, 



orbit

is the satellite

position vector corrected by the RTS correction, and 



broadcast

is the satellite position vector computed from GNSS broadcast

ephemeris. The raw RTS correction data is expressed in radial, along-track, and cross-track (RAC) coordinates, also the broadcast

orbit is expressed in ECEF coordinates. These differences demand a transformation of the correction from RAC to ECEF

coordinate. Unit vectors  representing the RAC components can be computed from the broadcast position and velocity vectors 

󰇗





Along



󰇗

󰇟

󰇍



󰇠 





cross



󰇗

󰇟

󰇍





󰇗

󰇠





radial





along





cross

(2.2)







() = [





radial





along





cross

] 



󰇍



(), (2.2a)

where 

radial

, 

along

, and 

cross

are the unit vectors for radial, along-track, and cross-track coordinates, respectively





󰇍



()

is the orbit

correction represented in RAC coordinates. All the correction components consist of transmitted orbit correction, 



, and its rate

of change, 

󰇗

, as





() =  (

) +



󰇗

( − 

) (2.3)

Where



= radial, along-track, and cross-track,

also



is the current time to compute the correction, and 

is the time of

applicability that is included in the RTS message, Hadas and Bosy, (2015); El-Mowafy, Deo and Kubo (2019).

The RTS clock correction,  (t), is given as a correction to the broadcast clock offset. And for the orbit correction, the clock

correction consists of the transmitted correction and its rate of change:

 () = 

+ 

( − 

) + 

( − 

)

(2.4)

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here 0, 

, and 

represent the transmitted clock corrections. (t) is expressed as a correction-equivalent range unit, and where

 ()

is expressed as the clock offset, which can be obtained by dividing it by the speed of light c:

 () =

󰇛



󰇛



󰇜



󰇜



(2.5)

II. Methodology

A work flow-diagram for the research methodology is shown in Fig. 3.1.

Figure 3.1: A flowchart of the design.

III. GNSS Static Positioning Method

A Hi-Target 90 GNSS dual-frequency receiver was employed for static observations at each ground control point (GCP), with

technical details listed in Table 3.1. After ensuring the receiver's functionality, observations were conducted for a minimum of

two hours at each GCP between July 18-19, 2023 (DOY 199-200). The receiver was set to collect data at 15-second intervals with

a 15° mask angle. The data was then converted to RINEX format and submitted for online processing on August 13, 2023 (DOY

225), using AUSPOS 2.4, which utilizes IGS products to compute precise coordinates in the International Terrestrial Reference

Frame (ITRF). AUSPOS leverages the Bernese GNSS Software Version 5.2 for data processing. All data was optimally

processed, yielding positions in the ITRF14 reference frame.

Table 3.1: Technical Specifications of GPS Receivers

ITEM

HI-TARGET V90+ GPS RECEIVER

Type

Dual frequency

Channels

220 Channels (GPS, GLONASS, SBAS, GALILEO, BDS, QZSS)

Ports

1 mini USB, 1 5-pin serial for NMEA output, external devices, power, etc

Bluetooth

Dual mode BT4.0

Kinematic

Accuracies

Horizontal: 10mm + 1ppm RMS

Vertical: 2.5mm + 1ppm RMS

RTK: Hor.: 8mm+1ppm; Vert.: 15mm+1ppm

Static Accuracies

Horizontal: 2.5mm + 1ppm RMS

Vertical: 5mm + 1ppm RMS

Transmission/ Reception

Formats

CMR, CMR+, sCMRx

RTCM: 2.1, 2.3, 3.0, 3.1, 3.2

DGPS

NMEA 0183GSV, AVR, RMC, HDT,VGK, VHD, ROT, GGK, GGA, GSA, ZDA, VTG,

GST, PJT, PJK, etc

Communication (Data

Links)

Radio modem, Internal 3G, compatible with GPRS, GSM, and Network RTK

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IGS-RTS Positioning Method

The Hi-Target V90+ dual-frequency GPS receiver was selected for the IGS-RTS PPP method due to its compatibility with the

required accessories. The receiver's technical specifications are outlined in Table 3.1. To facilitate real-time processing, the

RTKLIB/RTKNAVI software was installed on a laptop PC, and the Hi-Target V90+ receiver was connected to the PC via a serial

port. The RTKNAVI real-time navigation program was then launched, and the receiver was configured to receive corrections

from IGS servers, as illustrated in Figure 3.2.

Fig 3.2: Configuration of RTKNAVI

IV. Results and Discussions

Results for Differential GNSS Static Positioning

Table 4.1 presents the results of differential GNSS static positioning using the Hi-Target V90 GNSS dual-frequency receiver,

processed online by AUSPOS with Bernese software v5.2. The results include the Cartesian (X, Y, Z) and geodetic (latitude,

longitude, and ellipsoidal height) coordinates of six ground control points (ZIK1-ZIK6) in the ITRF 2014 datum. Among the IGS

reference stations used for processing, NKLG is the closest to the study area, with a baseline length of approximately 990km.

Consequently, AUSPOS utilized NKLG as the reference station to form baselines with the network stations.

Table4.1: ITRF2014 Coordinates from GNSS Static method processed by AUSPOS

Station

ITRF 2014 COORDINATES

Ambiguity

Resolution

(%)

CARTESIAN (m)

GEODETIC (2)

X (m)

Y (m)

Z (m)

 (DMSm)



(DMSm)

h (m)

ZIK1

6252855.930

778709.086

986131.768

8 57 13.035

 0.022

7 05 55.930

0.008

233.059 0.036

64.5

ZIK2

6252867.417

778887.666

985906.152

8 57 05.612

 0.028

7 06 01.685

 0.016

231.012 0.078

59.6

ZIK3

6252883.279

778999.713

985709.505

8 56 59.139

 0.058

7 06 05.260

 0.013

229.648 0.061

58.7

ZIK4

6252830.968

779255.827

985836.155

8 57 03.314

 0.027

7 06 13.791

 0.016

229.356 0.089

46.6

ZIK5

6252749.708

779693.680

985930.578

8 57 06.484

 0.022

7 06 28.343

 0.010

217.899 0.050

59.0

ZIK6

6252939.956

778711.973

985560.103

8 56 54.231

 0.030

7 05 55.684

 0.012

226.8320.057

61.5

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The ambiguity resolution (A.M.) percentage indicates the processing success rate, with 50% or higher considered reliable

(AUSPOS Report, 2023). All GCPs achieved success rates above 55%, except ZIK4 (46.6%), making its static method

coordinates unreliable (Table 4.1). Geodetic positional uncertainties for the GCPs were determined at a 95% confidence limit

(AUSPOS processing report, 2023). The mean horizontal and vertical errors were calculated as follows;

 





󰇛





󰇜









(4.1)

   





󰇛









󰇜







(4.2)

Where ∆E is the change in easting coordinates, ∆N is the change in northing coordinates and n is the total number of the

observation’s points

The mean uncertainties for horizontal and vertical positions were therefore calculated using the above equations 4.1 and 4.2 as

0.036m and 0.064m respectively; while the maximum are 0.058m and 0.089m respectively.

Results for IGS-RTS Positioning

Real-time service data was transmitted via NTRIP caster version 2.0.21/2.0, with the host server being (rt.igs.org) The

coordinates were referenced to the World Geodetic System 1984 (WGS84) framework, as the operation utilized RTKNAVI

software version 2.4.3_b3. The data streaming employed IGS02 format, using message codes 1057(60), 1059(5), and 1060(5).

Geodetic positional uncertainties for the GCPs were calculated, and tropospheric effects were estimated using the Saastamoinen

model. The data collection consisted of two sessions: Wet observations on August 30, 2023, and Dry observations on February

16, 2024.

Table4.2: The Coordinates of points streamed by IGS-RTS with IGS02 at the Wet season

Station

WGS84 COORDINATES

CARTESIAN (m)

GEODETIC (2)

X (m)

Y (m)

Z (m)

 (DMSm)

 (DMSm)

h (m)

ZIK1

6252856.023

778709.188

986131.761

8 57 13.035

0.098

7 05 55.933

0.157

233.1610.144

ZIK2

6252867.662

778887.667

985906.211

8 57 05.613 

0.080

7 06 01.684 

0.041

231.2620.347

ZIK3

6252883.192

778999.718

985709.468

8 56 59.139 

0.055

7 06 05.260 

0.159

229.5580.184

ZIK4

6252830.985

779255.831

985836.137

8 57 03.313

0.036

7 06 13.791 

0.027

229.3710.104

ZIK5

6252749.758

779693.753

985930.618

8 57 06.485 

0.044

7 06 28.345 

0.119

217.9630.151

ZIK6

6252939.952

778711.926

985560.082

8 56 54.231 

0.032

7 05 55.682 

0.073

226.8200.117

From the Table 4.2, the mean uncertainties for horizontal and vertical positions at the Wet season were computed as 0.126m

and 0.192m respectively; while the maximum were 0.159m and 0.347m respectively.

Table4.3: The Coordinates of points streamed by IGS-RTS with IGS02 at the Dry season

Station

WGS84 COORDINATES

CARTESIAN (m)

GEODETIC (2)

X (m)

Y (m)

Z (m)

 (DMSm)

 (DMSm)

h (m)

ZIK1

6252855.850

778709.091

986131.750

8 57 13.035

0.020

7 05 55.930

0.015

230.9140.050

ZIK2

6252867.311

778887.676

985906.182

8 57 05.613 

0.045

7 06 01.685 

0.019

231.0270.116

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ZIK3

6252883.235

778999.736

985709.494

8 56 59.139 

0.038

7 06 05.261 

0.011

229.6060.058

ZIK4

6252831.100

779255.921

985836.179

8 57 03.314 

0.021

7 06 13.794 

0.011

229.5020.059

ZIK5

6252749.657

779693.694

985930.587

8 57 06.484 

0.014

7 06 28.343 

0.010

217.8510.050

ZIK6

6252939.986

778711.940

985560.102

8 56 54.231 

0.029

7 05 55.683 

0.014

226.8570.048

Also, from the Table 4.3, the mean uncertainties for horizontal and vertical positions at Dry season were computed as 0.033m

and 0.068m respectively; while the maximum are 0.045m and 0.116m respectively.

Comparison of IGS-RTS and GNSS Static Results

Tables 4.1, 4.2, and 4.3 present the positions obtained from GNSS Static and IGS02 data in both ITRF 2014 and WGS84

reference frames. To facilitate a precise comparison between the two frames, note that the WGS84 realizations are consistent with

ITRF at a level of approximately 10 centimeters. As a result, no official transformation parameters were established, implying

that ITRF coordinates can be considered equivalent to WGS84 coordinates at a 10-centimeter level. According to Dave (2022),

ITRF2014 and WGS84 are expected to align at the centimeter level, effectively rendering transformation parameters unnecessary.

Table 4.4: The difference in coordinates of GNSS Static and IGS02

Station

IGS02 REFERENCE FRAME

WET (m)

DRY (m)

∆X

∆Y

∆Z

3-D Error

∆X

∆Y

∆Z

3-D Error

ZIK1

-0.093

-0.102

0.007

0.138

0.080

-0.005

0.018

0.082

ZIK2

-0.245

-0.001

-0.059

0.252

0.106

-0.010

-0.030

0.111

ZIK3

0.087

-0.005

0.037

0.095

0.044

-0.023

0.011

0.051

ZIK4

-0.017

-0.004

0.018

0.025

-0.132

-0.094

-0.024

0.164

ZIK5

-0.050

-0.073

-0.040

0.097

0.051

-0.014

-0.009

0.054

ZIK6

0.004

0.047

0.021

0.052

-0.030

0.033

0.001

0.045

RMS

Discrepancy = 0.065

RMS

Discrepancy = 0.046

 



 󰇛



󰇜









(4.3)

Fig. 4.1: Discrepancies between positions from RTS and GNSS Static methods (wet season)

-0.3

-0.2

-0.1

0.1

0.2

0.3

Z I K 1 Z I K 2 Z I K 3 Z I K 4 Z I K 5 Z I K 6

DISCREPANCIES (M)

GROUND CONTROL POINTS

DISCRE PANCY BET W E E N POS I T IO N S BY I G S02 - RT S[ WE T

SE A S O N] AND G NSS S T AT I C M E T HO D

X-Error

Y-Error

Z-Error

3-D Error

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIII, Issue VIII, August 2024

www.ijltemas.in Page 149

Fig. 4.2: Discrepancies between positions from RTS and GNSS Static methods (Dry season)

The Root Mean Square Error (RMSE) values indicate that the dry season observations (IGS02: 0.046m) achieved higher accuracy

compared to the wet season observations (IGS02: 0.065m). This discrepancy is attributed to the lower atmospheric pressure

during the wet season, which affects the tropospheric delay in the Saastamoinen model used (Dodo, 2019). Figures 4.1 and 4.2

illustrate the differences between IGS-RTS and GNSS Static-PPP positions in both seasons, showing a consistent relationship

between IGS-RTS observations in both wet and dry seasons across all stations, unlike GNSS Static-PPP. The figures also reveal

that the maximum and minimum 3-D errors occurred at stations ZIK2 and ZIK4 (wet season) and ZIK4 and ZIK6 (dry season),

respectively. Despite the slight differences in RMSE values (0.065m and 0.046m), the study concludes that there is no significant

difference between IGS-RTS observations made in dry and wet seasons compared to GNSS Static-PPP observations.

V. Conclusion and Recommendations

The results reveal that the Root Mean Square Error (RMSE) of IGS02 in the Wet and Dry seasons, compared to GNSS Static

(AUSPOS) services, is approximately 7cm (0.065m) and 5cm (0.046m), respectively. The IGS-RTS products performed

optimally during the dry season, indicating that it is the best time to minimize the impact of climate on GNSS observations. The

research suggests that IGS-RTS data performs effectively in Nigeria's climate, as the study found no substantial differences in

IGS-RTS observations between dry and wet seasons when compared to GNSS Static-PPP observations, indicating its reliability

and adaptability to the region's varying weather conditions.

It is essential to reject results from stations with poorly resolved positions (ambiguity resolution below 50%) and repeat the

observations to ensure more accurate data. This approach will help maintain high-quality results and mitigate potential errors.

Acknowledgement

We extend our gratitude to the International GNSS Service (IGS) for granting us access to the crucial IGS-RTS data (IGS02),

which played a vital role in this research. We also appreciate the assistance provided by AUSPOS, whose online processing

service enabled us to process our data at no cost using the Bernese scientific software version 5.2, thereby supporting our study.

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