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Sensing of the atmospheric variation using Low Cost GNSS Receiver

As the GNSS signals transmitted through the atmosphere, they are delayed by interference of TEC (Total Electron Content) in the ionosphere and water vapor in the troposphere. By using inverse-problem, name GNSS Meteorology, those parameters can be obtained precisely and several researches has approved and supported that method. However, the geodetic GNSS receivers are relatively high cost ($30,000 to $70,000 each) to be established on a regular and uniform network. This research aims to investigate the potential use of low cost GNSS receiver (less than $2,000) to observe the atmospheric dynamic both in ionosphere and troposphere. Results indicated that low cost GNSS receiver is a promising tools to sensing the atmospheric dynamic, however, further processing is needed to enhance the data quality. It is found that both of ionosphere and troposphere dynamic has diurnal periodic component.


Introduction
Atmosphere consists at least two layers, like ionosphere and troposphere. The ionosphere can be described as the upper layer (20 to 2,000 km) of atmosphere where the thickness of free elections and ions is sufficiently high to interfere the electromagnetic waves propagation [1]. The troposphere can be defined as the lower layer (up to 20 km) of the atmosphere where consists of water vapor and aerosol [2]. Nowadays, atmospheric study is important for the communication and the climate change issues.
Atmospheric can be monitor by using ionosonde (for ionosphere) and Automatic Weather Station (for troposphere), however, in accordance with the emergence and the development of GNSS (Global Navigation Satellite System) technology and methods, it leads to diverse application of GNSS, such as ionosphere and troposphere monitoring. As the GNSS signals transmitted through the atmosphere, the GNSS signals are interfered by the TEC (Total Electron Content) in the ionosphere, while water vapor in the troposphere.
In ionosphere and troposphere monitoring, the geodetic GNSS receiver (high cost $30.000 to $70.000 each) leads to a problem, especially in Indonesia. As seen on Figure 1, at about 1.9 million square kilometers, Indonesia needs a lot of atmosphere monitoring stations. Assumed that there will be at least one monitoring station every 20 kilometers, Indonesia demand at least 4,000 monitoring stations. Recently several researches on low cost GNSS receiver (less than $2,000) had been done by many researchers, such as precise point positioning [3], landslide monitoring [4][5] and UAV applications [6]. Those researches indicate the accuracy and the potential use of the low cost GNSS receiver for a wider applications. Furthermore, as mentioned before, by replacing those geodetic GNSS receivers with low cost GNSS receiver, it lead to a cost efficiency for the site monitoring establishment itself. Therefore, this research aims to investigate the potential use of low cost GNSS receiver to observe the atmospheric 2 1234567890 ''"" dynamic both in ionosphere and troposphere. The data and method are presented in detail in Section 2.
In section 3, the ionosphere and troposphere parameter derived from low-cost GNSS will be evaluated.

Data and Method
An experimental observation points was established in Bandung, by using Astech ProFlex 500 as the reference geodetic GNSS receiver and Tersus BX305 as the low cost GNSS receiver ( Figure 2). Figure  3 shows the basic flowchart used in this research. This research includes several steps, such as data observation and satellite navigation collection, data quality assessment, ionospheric parameter determination and tropospheric parameter determination.

Data Quality Assessment Method
GNSS data quality can be determined by evaluating the Signal to Noise (SNR) ratio an the multipath error of GNSS signal propagation. SNR indicates the degree of distorted GNSS signal propagation which is affected by surrounding environment. The high value of SNR (in dbHZ) indicates that noise is significantly low compared with the signal strength and vice versa. In simplified, SNR can be defined as follows: The multipath of GNSS signal can be investigate by applied the Multipath Combination (MPC) algorithm. The MPC can be expressed as [7]: where P and L are the pseudorange and carrie phase range respectively and f is the frequency of the carrier phase. By using these combination, the first order of ionospheric delay and geometric range from satellite and receiver are eliminated, while noise are assumed to be negligible due to the accuracy of the pseudorange observation.

GNSS Signal Propagation and Error
GNSS observation data is consist of carrier phase and pseudorange code data observation. Error and other noises are also contained within the GNSS observation data. Mathematically, GNSS data observation, as well as the error and noise can be defined as follows [8]: where Pi and Li are pseudorange and carrier phase range on selected frequency (i =1,2), ρ is related to geometrical range from receiver to satellite, dρ is the orbital error, dtrop and dion are troposphere and ionosphere biases, c is the speed of light (299,729,458 m/s), dt and dT are time error of receiver and satellites, MPi and MLi are related to multipath error of pseudorange and carrier phase range, λi and Ni are related to wavelength and ambiguity number, while ϑPi and ϑLi are related to noise error Table 1. Error in GNSS signal propagation [9].

Error Source Magnitude
Satellite orbital error ~ 100 m Satellite clock error ~ 500 m Ionospheric bias ~ 50 m Tropospherc bias ~ 5 m By understanding the GNSS signal propagation, TEC and water vapor can be estimated. Geometrical linear combination of GNSS signal is used to determine the TEC, while PPP (Precise Point Positioning) method is used to determine the tropospheric delay which lead to water vapor estimation.

GNSS Ionospheric Algorithm
The Slant TEC is defined as the line integral of the electron density from all GNSS-Satellites visible from each of these receiver above a user-specified elevation cut-off angle (usually 15 0 ). Mathematically, slant TEC can be defined as follows [10]: where Cx, is the constant 80.62 (m 3 /s 2 ), RCB and SCB are receiver code bias/receiver clock bias and satellite code bias/satellite clock bias respectively.

GNSS Tropospheric Algorithm
Tropospheric parameter can be determined by Precise Point Positioning (PPP) algorithm. PPP uses both of pseudorange and carrier phase GNSS observation data from a single dual-frequency receiver. PPP where is refer to tropospheric mapping function and is refer to zenith troposphere delay. The PPP estimates four types of parameters, the receiver position ( , , ), the receiver clock ( ), the troposphere delay ( ) and the ambiguity ( 1 and 2 ). Figure 4 shows the SNR and multipath for both Astech Proflex 500 (geodetic GNSS) receiver and Tersus BX305 (low-cost GNSS) receiver. Both observed the same satellites in the same observation time. It could be seen that SNR for low-cost GNSS receiver is almost the same as the reference geodetic GNSS receiver which vary from 30 dbHZ to 50 dbHZ. On the other hand, Low-cost GNSS receiver gives worse performance in code multipath analysis. Low-cost GNSS receiver code's multipath is vary from -3 meter to 3 meter, while the multipath for geodetic GNSS receiver is vary from -1 meter to 1 meter. This condition leads to a worse code point positioning accuracy in low-cost GNSS receiver rather than geodetic GNSS receiver.  Figure 5 shows the estimated relative slant TEC from code pseudorange data for both of low-cost GNSS receiver and geodetic GNSS receiver for each observed satellite. Both of them show similar magnitude of slant TEC which is up to 120 TECU, however, the estimated slant TEC from low-cost GNSS receiver showed a significant noise compared with the estimated slant TEC from geodetic GNSS receiver. According to equation (4), possible caused that lead estimated slant TEC has significant noise is there receiver clock bias. Receiver clock bias is related with the effect of oscilator instability. That factor may also be the main factor of the high deviation of code mulipath error [12].

Ionosphere Parameter Estimation
To improve the quality of the data observation, further data processing is needed. Data smoothing is applied in this research. Data smoothing is conducted in several steps [13] Figure 6 shows the estimated slant TEC from smoothed low-cost GNSS data. It could be seen that the estimated slant TEC has small biases. Slant TEC has maximum magnitude around 3:00-7:00 UTC or 10:00-14:00 at local time and minimum at the 12:00-00:00 UTC or 19:00-7:00 at local time. These diurnal variation is related to the sun occurance [14,15] Figure 7 shows the zenith wet delay (ZWD) from three days observation by substracting the zenith tropospheric delay with zenith hidrostatic delay using Saastamoinen model. ZWD is vary from 0.22 meter to 0.34 meter. Similar with TEC, ZWD also indicates diurnal variation. Precitable water vapor (PWV) can be estimated from zenith wet delay as follows:

Troposphere Parameter Estimation
where Π is 0.15 as constant. Figure 8 shows the PWV that is vary from 35 mm to 50 mm.

Conclusion
It is now possible to accurately observe variations of atmospheric parameters using a single groundbased low cost GNSS receiver, however, further processing is needed to enhance the data quality. As the implications, atmospheric monitoring using dense low-cost GNSS receiver can provide continuous regional/global monitoring of the ionosphere and troposphere parameters with various spatio-temporal scales (even in real-time).