Tropospheric Delay Estimation using MET3A …ijarece.org/wp-content/uploads/2014/02/IJARECE-VOL-3-ISSUE-2-223... · based radio navigation system that provides accurate three dimensional

International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE)

Volume 3, Issue 2, February 2014

223

ISSN: 2278 – 909X All Rights Reserved © 2014 IJARECE

Abstract— Global Positioning System is the emerging

technology for Navigation especially in Civil Aviation and

Defense sector. GPS Navigation accuracy can be degraded by

several error sources and one such error source is atmosphere

in which the refraction of GPS signal causes an error of

1-50meters. This atmospheric error is due to refraction of the

GPS signal in ionosphere and troposphere. In this paper, an

attempt is made to estimate the error due to troposphere, which

causes the delay of GPS signal in atmosphere. This delay due to

tropospheric refraction leads to an error in ranging

measurements, which reduces the accuracy of Navigation

Solution. In this paper, in order to investigate the impact of

tropospheric delay on Global Positioning System (GPS),

tropospheric delay is estimated using the Meteorological data

provided by Scripps Orbit And Permanent Array Center

(SOPAC) for one of the International GNSS Service (IGS)

station i.e. Indian Institute Of Science (IISC: Lat/Long:

13°1'14"N 77°33'56"E) located in Bangalore, Karnataka, India

for one typical day i.e. August 1st 2012.

Index Terms— Global Positioning System, Total Electron

Content, Zenith Dry Delay, Zenith Wet Delay, Zenith

Troposphere Delay.

I. INTRODUCTION

The introduction of Global Positioning System (GPS) has

revolutionized the field of navigation particularly in the field

of Civil Aviation. The GPS is a reliable, all weather satellite

based radio navigation system that provides accurate three

dimensional (3D) position, velocity and timing information

anywhere on or above the earth‘s surface.

The performance of GPS is affected by several error sources

such as atmospheric layers, clock bias of satellites, multipath,

receiver noise and the receiver to satellite geometry. One of the predominant factors that significantly influence the GPS

navigation solution is the refraction of the GPS signal in

atmosphere, which causes an error of 10-30m [1]. Hence it is

necessary to model the error due to atmosphere layers. As

ionosphere and troposphere are the two layers which

contribute major error, in this paper an attempt is made to

estimate the delay due to troposphere.

The refraction of the GPS signal in troposphere is due to the

presence of water vapor, pressure, temperature and

combination of few gases like N2, O2 etc. in the troposphere.

Manuscript received Feb, 2014.

First Author name, Shaik Gowsuddin, ECE Department, GITAM University, Vijayawada, India, Mobile No 8885822201

This refraction of GPS signal causes delay of the signal

arrival at the receiver, which in turn induces an error in

ranging measurements of the signal. Hence the navigation

solution accuracy, which is determined by ranging

measurements, is degraded. Hence to obtain the precise

navigation solution, these atmospheric errors should be

modeled [2]. There are various methods to estimate the delay of the GPS signal due to tropospheric refraction, among

which the Hop-Field global prediction model and

saastamoinen models are used to estimate the tropospheric

delay using the data due to an IGS station. The

meteorological data of an IGS station i.e. IISC (Lat\Long:

13°1'14"N 77°33'56"E), Bangalore, is collected from the

web and is processed to estimate the tropospheric delay.

II. GPS OBSERVABLES

The GPS system provides two ranging measurements namely

code phase measurements and carrier phase measurements to provide the positioning information. Code phase

measurements provide range measurements from satellite to

receiver by considering the apparent transit time of the signal

between the satellite and receiver. Carrier phase

measurements are made by comparison of the received signal

phase with the reference signal phase generated by the

receiver.

As the code phase measurements are less prone to noise, they

are considered to carry out this work.

The Code phase observable equation of GPS is expressed as

Eq.1.

)1()( tro

dion

ddTdtcp

‗ p ‘ is measured pseudo range.

‗ ‘ is geometric or true range.

‗c ‘ represents speed of light.

dt and dT are offsets of satellite and receiver clocks.

'',''tro

dion

d are range delays due to ionosphere and

troposphere.

'' represent effects of multipath and receiver

measurement noise.

Tropospheric Delay Estimation using MET3A

Meteorological Measurement System for GPS

Atmospheric Error Modeling

Shaik Gowsuddin



224


III. GPS ERROR BUDGET

Navigation solution accuracy of the GPS system is

dependent on the accuracy of the measured GPS ranging

signals. But, the GPS signals are degraded due to several

factors such as atmospheric refraction, multipath, receiver clock bias, satellite clock bias and satellite-receiver geometry

[3]. Table 1 represents the typical error value for each of the

error source.

Table (1) Typical GPS Error Budget

From Table (1) the part of error due to the tropospheric delay

is around 1-3 meters. This delay is due to the neutral

atmosphere which refers to the non-ionized portion of the

atmosphere made up of lower part of stratosphere and

troposphere.

IV. ESTIMATION OF TROPOSPHERIC DELAY

The transmitted signal from the satellite in space, which is

the heart of the GPS navigation system, passes through

different layers of the atmosphere such as ionosphere and

troposphere while reaching the receiver. This GPS signal

experiences a change in speed and direction as it passes through the atmosphere. Ionosphere and troposphere are the

two layers which effect the GPS signal propagation. The

effect of ionospheric refraction can be eliminated by dual

frequency measurements. The delay due to the neutral

atmosphere can be modeled using several algorithms. In this

paper, estimation of the tropospheric delay due to two

different global models called the Hopfield model and

Saastamoinen model is presented for the entire day of the 1st

August 2012.The atmospheric errors responsible for the

refraction of the GPS signal are shown in Fig (1) [4]

Fig (1) Atmospheric Delays

The delay of the GPS signal in troposphere is due to the dry

part of troposphere as well as the wet part of troposphere.

Among them 90% error contribution is by the dry part of

troposphere and the remaining 10% error contribution is by

wet part of troposphere

Hopfield developed an empirical Troposphere model in

1969 using world wide data. In this model, troposphere and

stratosphere are considered as wet part and dry part of the

atmosphere and the corresponding refractivity is a function

of height above the surface. The wet part extends from a

height of 11Km from the surface of the earth and the dry part

extends from the troposphere at a height of 40km as shown in

Figure (2)[4].

Saastamoinen model is also based on refractivity in which

the refractivity is derived using gas laws. In this model also,

total Tropospheric delay is the combination of Saastamoinen dry delay and Saastamoinen wet delay. In this model the wet

part extends from a height of 12Km from the surface of earth

and dry part extends up to 50Km from the wet region. The

Saastmoinen model is shown in Figure (3)[5].

Fig (2) Hopfield Tropospheric Model

.

Fig (3) Saastamoinen Tropospheric Model The Total Tropospheric Delay which is also known as Zenith

Tropospheric Delay (ZTD), is the sum of Zenith Dry Delay

(ZDD) and Zenith Wet Delay (ZWD) [3].To estimate the

ZDD and ZWD, the surface meteorological parameters such

as temperature, pressure, water vapor and station specific

parameters such as height, latitude of the Tracking Earth

Station can be collected for the typical day using MET3A

meteorological measurement system [6].

Error Type Error (meters) Segment

Ephemeris 3.0 Signal-In-Space

Clock 3.0 Signal-In-Space

Ionosphere 4.0 Atmosphere

Troposphere 1.0 Atmosphere

Multipath 1.4 Receiver

Receiver 0.8 Receiver

Atmospheric Delay

Ionosphere

Delay

Troposphere

Delay

Dry

Delay

(Gases)

Wet Delay

(Water

Vapor)

TEC



225


V. WATER VAPOR ESTIMATION

The surface meteorological parameters such as temperature

in Kelvin, pressure in millibars and relative humidity in

percentage of MET3A meteorological measurement system

are collected from the SOPAC IGS station data available on web.

The Water vapor content in millibars is calculated from the

prior environmental information such as relative humidity

and temperature at the tracking earth station using

international earth rotation service (IERS) convention shown

below.

)2(152733237

1527357

1006110 .T.

).(T.

RH.W

‗ RH ‘is the Relative Humidity in (%)

‗T ‘ is the Temperature in degrees Kelvin (K)

‗W ‘ is the Water Vapor Pressure in millibars (mb)

VI. TOTAL TROPOSPHERIC REFRACTIVITY

The Tropospheric Refractivity is the sum of hydrostatic (Ndry)

(or) ‗dry‘ refractivity and non- hydrostatic (Nwet) (or)‘wet‘

refractivity due to the effect of dry gasses and water vapor

respectively and are expressed as below )3(0,0, N wetN dryNtrop

‗ Ntrop ‘ is the Total Refractivity of Troposphere in meters

‗ N dry,0 ‘ is the Dry Refractivity of Troposphere in meters.

‗ Nwet,0 ‘ is the Wet Refractivity of Troposphere in meters.

(a) Dry Refractivity

The dry refractivity at the tracking earth station at a height of

0Km is measured using Eq. 4

)4(64770

T

P).(

dry,N

‗ P ‘ is the Atmospheric Pressure in millibars (mb).

The dry refractivity of the troposphere above the earth

surface at tracking earth station at an altitude of ‗h‘ is

measured using Eq. 5

)5(

4

0,,

dh

hd

h

dryN

hdryN

)16.273(72.14840136 Td

h

dh is the height of dry part of troposphere in meters.

‗ h ‘ is the altitude at which we want to measure refractivity.

(b) Wet Refractivity

The wet refractivity at the tracking earth station at a height of

0Km can be can be measured using Eq. 6

)6(2

510718396120

T

W).(

T

W).(

wet,N

The wet refractivity of the troposphere above the earth

surface at tracking earth station at an altitude of ‗h‘ is measured using Eq. 7

)7(

4

0,,

wh

hw

h

wetN

hwetN

wh is the height of wet part of troposphere in meters.

Where ‗w

h ‘ is any value between 11000 to 12000m.

The wet component is much more difficult to model because

of the strong variations in the distribution of atmospheric

water vapor in space and time. Hence, due to a lack of an

appropriate alternative, the Hopfield model assumes the

same functional model for the wet component as that of the

dry component.

(c) Estimation of Zenith Tropospheric Delay

Tropospheric delay can be obtained directly by integrating

the refractivity along the GPS signal path dl through the

neutral atmosphere to obtain slant delay or by integrating

vertically to obtain zenith delay. In this paper, zenith tropospheric delay (ZTD) is estimated, which is a

combination of zenith wet delay and zenith dry delay.

)8(10 6 dlNdlNZTD wetdry

In this paper, the total zenith delay is estimated using two

popular global models namely Hopfield model and

Saastamoinen model. A comparison of these two models is

presented.

VII. HOPFIELD MODEL

(a) Zenith Wet Delay of Troposphere

The delay of the GPS signal in troposphere due to water

vapor which extends up to 11Km from the surface of the earth is called Zenith wet delay and is expressed as

)9(0,5

610

wh

wetNZWD

ZWD is the Zenith wet Delay in meters.

(b) Zenith Dry Delay of Troposphere

The GPS signal delayed in the troposphere due to the effects of dry gasses in atmosphere which extends from 40Kms

above the wet atmosphere is called dry delay or hydrostatic

delay and is expressed as

)10(0,5

610

dh

dryNZDD

ZDD is the Zenith Dry Delay in meters.

Due to the total delay in troposphere, the GPS signal travels

longer distance than the actual distance between satellite and

receiver. Total zenith tropospheric delay is expressed as

)11(ZWDZDDZTD



226


VIII. SAASTAMOINEN MODEL

(a) Saastamoinen Wet Delay of Troposphere

The delay of the GPS signal in troposphere due to water

vapor which extends up to 12Km from the surface of the earth is called Saastamoinen wet delay and is expressed as

Eq.12

)12(0501255

0022770

.

T*WV.SWD

SWD - Saastamoinen Zenith Wet Delay in meters.

WV -Water Vapor in millibars.

T -Temperature in Kelvin

(b) Saastamoinen Dry Delay of Troposphere

The GPS signal delayed in the troposphere due to the effects

of dry gasses in atmosphere which extends from 50Kms

above the wet atmosphere is called Saastamoinen dry delay

or hydrostatic delay and is expressed as Eq.13

)13(0022770 *P.SDD

SDD - Saastamoinen Zenith Dry Delay in meters.

P -Pressure

Total zenith tropospheric delay is expressed as

)14(SWDSDDSTD

STD Total zenith tropospheric delay due to Saastamoinen

mode.

IX. RESULTS

The Results are based on the Meteorological data and GPS

observation data. Those are collected from the

Meteorological Measurement System MET 3A and GPS

Receiver present in IISC located at Bangalore (IISC:

Long/Lat: 13°1'14"N 77°33'56"E). The data of the typical

day i.e. August 1st 2012 is collected and sampled at an

interval of 1 minute, in order to carry out this work [8].

Visibility of the satellites for complete 24 hours of the

typical day is shown in Fig.4

Fig (4) Visibility of the satellites for the entire day of 1st

August, 2012 at IISC, Bangalore

The meteorological parameters of the IGS Station IISC

Bangalore for the entire day i.e. Aug 1st 2012 at are shown in

Table 2

GPS

Time in

(Hours)

Pressure In

Millibars

Temperature

In Celsius

Relative

Humidity

In %

1 907.6 19.9 89.1

2 907.8 20.3 88

3 908.3 21.3 84.6

4 908.7 23.4 76.2

5 908.5 25 67.8

6 908.4 26.6 61.4

7 907.5 28.1 55.9

8 906.7 29.3 50.7

9 906.2 29.8 47.7

10 905.6 29.4 46.7

11 905.5 30.4 43.1

12 906 29.7 44.1

13 906.6 27.8 51.5

14 907.2 26.3 55

15 908.1 25.1 62.5

16 908.7 24.2 67

17 909.4 23.3 72.1

18 909.1 22.4 77.2

19 908.3 21.7 81.1

20 908 21.1 84.3

21 907.7 20.6 87.1

22 907.9 20.7 86.8

23 907.5 20.2 88.4

Table (2) Meteorological parameters (Pressure,

Temperature, and Relative Humidity) on typical day of

1st August, 2012 at IISC in Bangalore

The meteorological parameters, temperature, pressure,

relative humidity, water vapor of the IISC, Bangalore for a

typical day 1st August, 2012 are shown in Fig.5

Fig (5) Meteorological parameters (Temperature,

pressure, relative humidity and water vapor) of 1st

August, 2012 at IISC, Bangalore

The Pressure measurements at IISC Bangalore are using the

Paroscientific Digiquartz Barometer sensor model MET3A



227


meteorological measurement System and the data considered

is at a sampling rate of 60 seconds for the entire day. The

maximum pressure i.e. 909.4 millibars is observed at

17:00Hrs (GPS Time) and the minimum pressure i.e. 905.5

millibars is observed at 11:00Hrs (GPS Time) of the typical

day i.e. August 1st 2012. At 11:00Hrs (GPS Time) of the typical day maximum temperature i.e. 30.40C is observed

and at 1:00Hrs (GPS Time) minimum temperature of 19.90C

is observed. The maximum of relative humidity i.e.89.1% is

observed at 1:00Hrs(GPS Time) and a minimum of 43.1% is

observed at 11:00 Hours(GPS Time) on August 1st

2012.Water vapor content is Relative Humidity is used to It

is necessary to convert the Relative Humidity in to Water

Vapor [6] for using in Hopfield model. As per the plot we can

say that the Maximum Water Vapor 22.37 milli bars is

observed at 3:36 Hour (GPS Time) and the Minimum Water

Vapor 18.13 milli bars is observed at 12:00 Hours (GPS

Time) of a complete day i.e. Aug 1st 2012.

(a) REFRACTIVITY

Refractivity is a function of pressure, temperature, and water

vapor pressure (moisture). Refractivity is symbolized by

"N"[7]. Atmospheric Refractivity near the earth‘s surface

normally varies between 250 and 400 N. smaller the N-value,

faster the propagation of GPS signal, larger the N-value, the

slower the propagation of GPS signal.

GPS

Time in

(Hours)

Dry

Refractivity

(N)

Wet

Refractivity

(N)

Total

Refractivity

(N)

1 240.41 88.99 329.4122

2 240.14 89.85 329.993

3 239.45 91.23 330.6905

4 237.86 92.03 329.901

5 236.53 89.15 325.695

6 235.25 87.80 323.0598

7 233.84 86.40 320.2502

8 232.71 83.33 316.0491

9 232.20 80.42 312.6264

10 232.35 77.15 309.5075

11 231.56 74.91 306.4755

12 232.22 73.97 306.2052

13 233.84 78.38 312.2287

14 235.17 77.43 312.6099

15 236.35 82.62 318.9771

16 237.22 84.44 321.676

17 238.13 86.61 324.7478

18 238.77 88.36 327.1422

19 239.13 89.37 328.5131

20 239.54 89.92 329.4655

21 239.87 90.40 330.277

22 239.84 90.58 330.4312

23 240.14 89.76 329.9097

Table (3) Dry and Wet refractivity estimated for a typical

day of 1st August, 2012 at IISC, Bangalore

The Total refractivity is the sum of dry and wet refractivity.

So, the refractivity of the Troposphere due to the data due to

IISC Bangalore region is estimated using Hopfield model

and shown in Table 3

Fig (6) Zenith Dry Refractivity vs GPS Time for the

entire day of 1st August, 2012 at IISC, Bangalore

Fig (6) shows the dry refractivity, wet refractivity and total

refractivity of the entire day of 1st August, 2012 at IISC,

Bangalore. From Fig (6) it is observed that the maximum Dry refractivity i.e. 240.41 N is observed at 1:00Hrs of GPS Time

and minimum Dry Refractivity i.e. 231.56 N is observed at

11:00Hrs of GPS Time.

It is also observed that the maximum Wet refractivity i.e

92.03 N is at 4:00Hrs of GPS Time and minimum Wet

refractivity i.e 73.97 N at 12:00Hrs.

Total refractivity of the troposphere is a combination of wet

refractivity and dry refractivity and is also shown in Fig (6).It

is observed that maximum total refractivity i.e. 330.6905 N

at 3:00Hrs of GPS Time and minimum total refractivity i.e. 306.2052 N is observed at 12:00Hrs of GPS Time.

(b) ZENITH DELAY DUE TO HOPFIELD MODEL

As the delay of the GPS signal is due to the refractivity of the

signal in the atmosphere, using the refractivity, zenith dry

delay and zenith wet delay are calculated for the entire day

using hop filed model and saastamonein model. Table 4

shows the delay estimated at each hour of the typical day

using the Hopfield model.

The delay estimation is based on the data due to IISC

Bangalore at sampling rate of 60 second using the

meteorological parameters temperature pressure, water vapor

and relative humidity.



228


Table (4) Tropospheric delay estimated using Hopfield

model for a typical day of 1st August, 2012 at IISC,

Bangalore. The total Zenith Delay which is a combination of wet delay

and dry delay is calculated at a data sampling rate of 60

seconds for the entire day based on the meteorological data

calculated i.e. temperature, water vapor by using the MET3A

sensor model present in the IISC Bangalore. The variations

of dry delay, wet delay and the total zenith delay on the entire

day of August 1st 2012 at IISC Bangalore is shown in Fig (7).

From Fig (7) it is observed that the dry part of atmosphere

contributes more error in the ranging measurements. The

maximum Zenith Dry Delay i.e. 2.0768 meters is estimated at 17:00Hrs (GPS Time) and minimum Zenith Dry Delay

i.e. 2.0684 meters is estimated at 10:40Hrs (GPS Time) of the

typical day i.e. Aug 1st 2012. The maximum Zenith Wet

Delay i.e. 0.211677 meters is estimated at 3:28Hrs (GPS

Time) and the minimum Zenith Wet Delay i.e. 0.170148

meters is observed at 11:45Hrs (GPS Time) of the complete

day i.e. Aug 1st 2012.

The Zenith Tropospheric Delay (ZTD) is the sum of both

Zenith dry delay and zenith wet delay is also estimated and is

shown in Fig (7).

The maximum Zenith Tropospheric Delay i.e. 2.286927 meters is estimated at 3:28Hrs (GPS Time) and the minimum

Zenith Tropospheric Delay i.e. 2.239714 meters is estimated

at 11:45Hrs (GPS Time) of the complete day i.e. Aug 1st

2012.

Fig (7) Total Zenith Delay (Hopfield) for the entire day of

1st August, 2012 at IISC, Bangalore

(c) ZENITH DELAY DUE TO SAASTAMOINEN

MODEL

Total Zenith delay is also estimated using the saastamoinen

model by estimating the dry delay as well as wet delay. Fig

(8) illustrates the variations of the dry delay, wet delay and

total zenith delay respectively on the typical day at IISC

Bangalore.

Fig (8) Total Zenith Delay (saastamoinen) model for the

entire day of 1st August, 2012 at IISC, Bangalore

The maximum Zenith Dry Delay i.e. 2.0714 meters is

estimated at 17:00Hrs (GPS Time) and minimum Zenith Dry

Delay i.e. 2.061 meters is estimated at 10:40Hrs (GPS Time)

of the complete day i.e. Aug 1st 2012. The maximum Zenith

Wet Delay i.e. 0.211677 meters is estimated at 4:00 Hrs

(GPS Time) and the minimum Zenith Wet Delay i.e.

0.1752meters is observed at 11:45Hrs (GPS Time) of the complete day i.e. Aug 1st 2012. It is observed that the

maximum Zenith Tropospheric Delay i.e. 2.28513 meters is

estimated at 3:28Hrs (GPS Time) and the minimum Zenith

Tropospheric Delay i.e. 2.23214 meters is estimated at

11:40Hrs (GPS Time) of the complete day i.e. Aug 1st 2012.

Table 5 presents the details of the tropospheric delay

estimated using saastamoinen model.

GPS

Time in

(Hours)

Zenith

Dry

Delay(ZDD)

in Meters

Zenith

Wet

Delay(ZWD)

in Meters

Total Delay

(ZTD) in

Meters

1 2.072461 0.20469 2.277151

2 2.07295 0.206658 2.279608

3 2.074171 0.209834 2.284005

4 2.07525 0.211677 2.286927

5 2.074918 0.205059 2.279977

6 2.074813 0.201961 2.276774

7 2.072872 0.198726 2.271597

8 2.071135 0.191669 2.262804

9 2.07003 0.184975 2.255005

10 2.06863 0.177449 2.246079

11 2.068476 0.172295 2.240771

12 2.069566 0.170148 2.239714

13 2.070793 0.180274 2.251067

14 2.072049 0.1781 2.250149

15 2.074012 0.190029 2.264042

16 2.075313 0.194231 2.269544

17 2.076841 0.199219 2.27606

18 2.076085 0.20324 2.279325

19 2.074203 0.205573 2.279776

20 2.07347 0.206825 2.280295

21 2.072745 0.207936 2.280681

22 2.07321 0.208357 2.281567

23 2.072257 0.206461 2.278717



229


Table (5) Tropospheric delay estimated using saastamoinen

model for a typical day of 1st August, 2012 at IISC, Bangalore

COMPARISON

Tropospheric delay estimated using Hopfield model and

saastamoinen model are compared for the entire day of 1st August

2012. Fig (9) shows the comparison of the both models for dry

delay, wet delay and total zenith delay respectively. Table 6

presents the details of the comparison by considering the

difference between Hopfield model and saastamoinen model.

GPS Time

in

(Hours)

Zenith

Dry Delay

(ZDD)

difference

in Meters

Zenith Wet

Delay

(ZWD)

difference

in Meters

Total Zenith

Delay

(ZTD)

difference

in Meters

1 0.0059 -0.0002 0.0056

2 0.0059 -0.0005 0.0054

3 0.006 -0.0013 0.0047

4 0.0061 -0.0028 0.0033

5 0.0063 -0.0039 0.0024

6 0.0064 -0.005 0.0014

7 0.0065 -0.0059 0.0006

8 0.0066 -0.0065 0.0001

9 0.0066 -0.0066 0

10 0.0066 -0.0061 0.0005

11 0.0067 -0.0065 0.0001

12 0.0066 -0.006 0.0006

13 0.0065 -0.0052 0.0013

14 0.0064 -0.0042 0.0022

15 0.0063 -0.0037 0.0026

16 0.0062 -0.0031 0.0031

17 0.0061 -0.0026 0.0035

18 0.0061 -0.002 0.0041

19 0.006 -0.0015 0.0045

20 0.006 -0.0011 0.0049

21 0.0059 -0.0007 0.0052

22 0.0059 -0.0008 0.0051

23 0.0059 -0.0004 0.0054

Table (6) Comparison of tropospheric delay components

of Hopfield model and sastamoinen model

Fig (9) Comparison of the total zenith delay due to

Hopfield and saastamoinen models for the entire day of

1st August 2012

From Table (6), it can be observed that difference of

tropospheric delay estimated using Hopfield model and saastamoinen model is in the order of millimeters. It can also

be observed that around 9:00Hrs of the day there is no

difference in the tropospheric delay estimated using both the

models

The difference in tropospheric delay between the two models

is due to the altitude consideration of the dry and wet

components of the troposphere above the surface of the earth.

CONCLUSIONS

In this paper, the meteorological data of the MET3A

meteorological measurement system and the observation

data

of the GPS receiver present at an IGS station IISC, Bangalore

(IISC: Lat/Long: 13°1'14"N 77°33'56"E) are used to estimate

the tropospheric delay components for the entire day of 1st

August 2012 Estimation of the zenith tropospheric delay is

important because it causes a delay in ranging measurements

which in turn cause an error in navigation solution.

Tropospheric zenith delay is estimated using two models i.e.

Hopfiled model and Saastamoinen model and a comparison

is done for the entire day of 1st August 2012. With both maximum delay (Hopfield: 2.286927m, saastmoinen:

2.28513m) is observed at 3:28Hrs and minimum delay

(Hopfield: 2.239714m, saastmoinen: 2.23214m) is observed

at 11:45Hrs of the typical day. It is also observed that the in

total Zenith delay, contribution of wet tropospheric delay is

much less than the dry delay.

REFERENCES

[1] Hall, M. P. 1979.‘ Effects of the troposphere on radio

communication‘,.Peter Peregrinus LTD on behalf of the Institution of

Electrical Engineers.

GPS

Time in

(Hours)

Zenith Dry

Delay

(ZDD) in

Meters

Zenith Wet

Delay

(ZWD) in

Meters

Total Zenith

Delay

(ZTD)in

Meters

1 2.0784 0.20449 2.271551

2 2.0789 0.206158 2.274208

3 2.0802 0.208534 2.279305

4 2.0814 0.208877 2.283627

5 2.0812 0.201159 2.277577

6 2.0812 0.196961 2.275374

7 2.0794 0.192826 2.270997

8 2.0777 0.185169 2.262704

9 2.0766 0.178375 2.255005

10 2.0752 0.171349 2.245579

11 2.0752 0.165795 2.240671

12 2.0762 0.164148 2.239114

13 2.0773 0.175074 2.249767

14 2.0784 0.1739 2.247949

15 2.0803 0.186329 2.261442

16 2.0815 0.191131 2.266444

17 2.0829 0.196619 2.27256

18 2.0822 0.20124 2.275225

19 2.0802 0.204073 2.275276

20 2.0795 0.205725 2.275395

21 2.0786 0.207236 2.275481

22 2.0791 0.207557 2.276467

23 2.0782 0.206061 2.273317



230


[2] Dejong, C. D. 1991, ‗GPS: Satellite orbits and atmospheric effects‘.

In NASA STI/Recon Technical Report N, 92:14097-.

[3] Shaik Gowsuddin, ‘ Ionospheric parameters estimation for

accurate GPS Navigation Solution‘, International Journal of

Engineering and Advanced Technology, ISSN No 2249-8958,

December 2012,Vol. 2, Issue 2.pp 302-305.

[4] Hopfield, H. S. 1969.‘Approximation to the tropospheric range

correction‘, In Applied Physics Laboratory, pages 572 – 569. Silver

Spring, Md., 12. November, S1A-572-69, 4 pp.

[5] Saastamoinen J. (1973). ―Contributions to the Theory of

Atmospheric Refraction.‖Bulletin Geodesique, 105, pp.279-298, 106,

pp. 383-397, 107, pp. 13-34. Printed in three

parts.

[6] ParoScientific, Inc. (2004). Met3A Meteorological System.

Retrieved 15 January 2004 from World Wide Web

(http://www.paroscientific.com/met3a.htm)

[7] Elgered, G., Davis, J. L., Herring, T. A., and Shapiro, I. I. 1991.

‗Geodesy by Radio Interferometry: Water Vapor Radiometry for

Estimation of Wet Delay‘.In Journal of Geophysical

Research, 96(B4):6541–6555.

[8]http://igscb.jpl.nasa.gov/igscb/station/log/iisc_20120124.log

this link is related to IGS, IISc Bangalore station information

Shaik Gowsuddin is completed his

M.Tech in GITAM University as

specialization Radio Frequency and

Microwave Engineering in E.C.E and He

completed his B.Tech in Nimra College

of Engineering and Technology under

(JNTUK).

http://igscb.jpl.nasa.gov/igscb/station/log/iisc_20120124.log

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Tropospheric Delay Estimation using MET3A …ijarece.org/wp-content/uploads/2014/02/IJARECE-VOL-3-ISSUE-2-223... · based radio navigation system that provides accurate three dimensional