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Accuracy and consistency of different global ionospheric maps released by IGS ionosphere associate analysis centers
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ScienceDirect Advances in Space Research xxx (2019) xxx–xxx www.elsevier.com/locate/asr
Accuracy and consistency of different global ionospheric maps released by IGS ionosphere associate analysis centers Peng Chen a,b,⇑, Hang Liu a, Yongchao Ma a, Naiquan Zheng a b
a College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China Deutsches Geoda¨tisches Forschungsinstitut der Technischen Universita¨t Mu¨nchen (DGFI-TUM), Arcisstraße 21, 80333 Mu¨nchen, Germany
Received 12 July 2019; received in revised form 20 September 2019; accepted 23 September 2019
Abstract Due to the differences of ionospheric modeling methods and selected tracking stations, the accuracy and consistency of Global Ionospheric Maps (GIMs) released by Ionosphere Associate Analysis Centers (IAACs) are different. In this study, we evaluate and analyze in detail the accuracy and consistency of GIMs final products provided by six IAACs from three different aspects. Firstly, the comparison of these GIMs shows that the mean bias (MEAN) is related to the modeling methods of various IAACs. The variation trend of the standard deviation (STD) is consistent with the solar activities, and accompanied by certain seasonal and annual periodic variations. The MEAN between IGS and each center is about 1.3 to 1.0 TECU, and the STD is about 1.4–2.5 TECU. Secondly, the validation with GPS TEC shows that the STD of CODE is the smallest at various latitudes, and the STD is about 0.7–4.5 TECU. Thirdly, The validation with the Jason2 VTEC shows that the STD between Jason2 and IAACs is about 4.4–5.2 TECU. In addition, the STD between Jason2 and six GIMs in the areas with more tracking stations is better than that of the regions with fewer tracking stations in different latitude regions. Regardless of whether the tracking stations are more or less, the MEAN and STD in high solar activity are larger than in low solar activity. Ó 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.
Keywords: Total electron content; Global ionospheric maps; Satellite altimeter; Accuracy and consistency
1. Introduction The ionosphere is a significant part of the atmosphere, extending from approximately 60 to 1000 km above the Earth’s surface (Gao and Liu, 2002), where free electrons and ions have important implications for radio communications, navigation, satellite positioning, and human space activities. The total electron content (TEC) is defined as the integral of the electron density along a path from the receiver to transmitter, and is one of the most important quantitative characteristics of the ionosphere. Global Navigation ⇑ Corresponding author.
E-mail address: [email protected] (P. Chen).
Satellite Systems (GNSS) can be utilized to monitor spatio-temporal variations of the ionospheric TEC during the last two decades, which has greatly facilitated the ionospheric research and the development of various applications and services (Mannucci et al., 1998; Herna´ndezPajares et al., 1999; Jakowski et al., 2005a, 2005b; Stankov et al., 2006; Coster and Komjathy, 2008; Buresova et al., 2009; Bilitza and Reinisch, 2015). One typical example is that, increasing IAACs use GNSS observation data to calculate GIMs. Since the late 1990s, Jet Propulsion Laboratory (JPL), Center for Orbit Determination in Europe (CODE), Universitat Polite`cnica de Catalunya (UPC) and European Space Operations Center of European Space Agency (ESA) have established global ionospheric models and routinely supplied TEC GIMs on
https://doi.org/10.1016/j.asr.2019.09.042 0273-1177/Ó 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.
Please cite this article as: P. Chen, H. Liu, Y. Ma et al., Accuracy and consistency of different global ionospheric maps released by IGS ionosphere associate analysis centers, Advances in Space Research, https://doi.org/10.1016/j.asr.2019.09.042
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P. Chen et al. / Advances in Space Research xxx (2019) xxx–xxx
a daily basis, respectively (Mannucci et al., 1998; Schaer, 1999; Herna´ndez-Pajares et al., 1999, 2009), and the International GNSS Service (IGS) has been openly providing final TEC GIMs in the uniform IONosphere Map Exchange (IONEX) format by comparing and combining the results of IAACs GIMs with the corresponding weights (Herna´ndez-Pajares et al., 2009). Canadian Geodetic Survey of Natural Resources Canada (NRCan) has resumed the submission of GIMs to IGS since April 2015, and Chinese Academy of Sciences (CAS) and Wuhan University (WHU) started to officially provided GIMs products and services in 2016. Additionally, DGFI-TUM also became new member of IGS IAACs in 2018 (Villiger and Dach, 2019). In terms of evaluating the accuracy performance of GIMs, Ho et al. (1996) concluded that the TEC derived from GIMs has a good agreement with TOPEX /Poseidon measurement. Oru´s et al. (2002) compared the ionospheric correction effects of GIMs, IRI (International Reference Ionosphere) model (Rawer et al., 1978) and Bent model (Bent and Llewllyn, 1973), and held that the performance of GIMs was better than IRI and Bent models. Herna´ndez-Pajares et al. (2009) fully verified the generation of GIMs and compared them with altimeter VTEC (vertical total electron content) measurements to validate reliability of GIMs. Luo et al. (2014) evaluated performance of five global ionospheric models, and found that IRI model and GIMs have the best consistency. Xiang et al. (2015) conducted thorough accuracy analysis of four different GIMs products over China, and suggested that UPC GIMs have strong applicability in solar maximum and low-latitude region. Chen et al. (2017) focused on the uneven distribution of GNSS tracking stations, integrated GNSS, satellite altimetry, radio occultation and DORIS (Doppler orbitography and radio positioning integrated by satellite) data to develop multi-source global ionospheric model, and the accuracy and reliability of GIMs in marine was improved significantly after fusing. Li et al. (2017) assessed and analyzed the internal and external accuracies of five different GIMs during two solar activity cycles, and offered systematic bias between individual IAAC GIMs and different altimeter satellites. Herna´ndezPajares et al. (2017) checked the consistency of GIMs by means of two independent and complementing assessing methods, i.e., dSTEC-GPS and VTEC-altimeter from 2010 to 2016. Roma et al. (2018) introduced detailed methods used by IAACs and compared the classical ones (CODE, ESA, JPL and UPC) with the new ones (NRCAN, CAS, WHU). GIMs can provide ionospheric temporal and spatial variation information, which greatly facilitates ionospheric scientific research and ionospheric correction for singlefrequency receivers. However, analyses of the GIMs accuracy over extended periods and on a global scale are still rare. This study not only validates and contrasts the consistency of six GIMs corresponding to different IAACs (CODE, JPL, UPC, CAS, ESA and WHU), but also
evaluate and analyze in detail the accuracy performance of GIMs by means of GPS (Global Positioning System) and altimeter satellite TEC observations. To examine the consistency and accuracy of these GIMs, three methods are applied as follows: Firstly, Section 2 analyzes the consistency of these six GIMs; Secondly, validation with VTEC derived from measured GNSS observations is investigated in Section 3. Thirdly, Section 4 presents the accuracy performance of GIMs by comparing with Jason2. Finally, The preliminary findings and conclusions are summarized in Section 5. 2. Consistency with each other There are several versions of GIMs - final (latency 1– 2 weeks), rapid (latency 1 day), predicted. The final GIMs are more reliable and practical, hence, this paper is devoted to analyze the accuracy and consistency of final GIMs. In the first step, we reflect on whether the modeling methods and data sources of the six GIMs assessed are consistent with each other, which is conducive to the data analysis of results, and these differences are summarized in Table 1. As shown in table, ‘‘CASG‘‘, ‘‘CODG”, ‘‘ESAG‘‘, ‘‘JPLG”, ‘‘UPCG‘‘, ‘‘WHUG” and ‘‘IGSG‘‘ represent the final GIMs products provided by CAS, CODE, ESA, JPL, UPC, WHU, and IGS respectively, and ‘‘SH” and ‘‘GTS‘‘ signify spherical harmonics and generalized trigonometric series. In order to achieve the primary objective of high-accuracy GIMs to continuously monitor the variation of ionosphere, some IGS-IAACs not only incorporate GLONASS (global navigation satellite system) and even BEIDOU data, but also improve time resolution, and the resolution of CAS GIMs reached 30 min in 2016 especially. In the next step, taking into account the effects of solar activities on the ionosphere, the period analyzed includes both high and low solar activities from January 1, 2009 to December 31, 2018, which allows for more detailed and thorough accuracy analysis and evaluation of the final GIMs. Moreover, considering the impact of station number on GIMs computation, the number comparison of global tracking stations used to compute daily GIMs at IAACs is shown in Fig. 1. According to the figure, there are large differences in the number of tracking stations used by each IAAC, but they are basically between 100 and 500. The number of stations used in JPL GIMs computation significantly lower than that of other IAACs. Besides, CAS and WHU have officially provided GIMs products and services, and the number of contributing stations of CAS increases from approximately 270 to over 400 at the beginning of 2016. 2.1. Validation with each other Comparing GIMs from different IAACs can reflect the consistency of the GIMs with respect to modeling methods. We calculate the MEAN and STD among these six
Please cite this article as: P. Chen, H. Liu, Y. Ma et al., Accuracy and consistency of different global ionospheric maps released by IGS ionosphere associate analysis centers, Advances in Space Research, https://doi.org/10.1016/j.asr.2019.09.042
P. Chen et al. / Advances in Space Research xxx (2019) xxx–xxx
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Table 1 Summary of GIMs corresponding to different IAACs for the test period. GIMs ID
Methods
UPCG CASG
Tomography with splines SH and GTS
CODG ESAG JPLG WHUG IGSG
SH SH Three-shell model SH Weighed mean
Modeling based on singlestation Integrate the global and local models Global modeling Global modeling Global modeling Global modeling
GNSSs
Temporal resolution
Start date
Reference
GPS
1 h, 2h
1998.6
GPS + GLONASS + BEIDOU GPS + GLONASS GPS GPS GPS + GLONASS
30 min, 1h, 2 h
2016
Herna´ndez-Pajares et al. (1999) Li et al. (2015)
1 h, 2h 2h 2h 1 h, 2h 2h
1998.6 1998.6 1998.6 2016 1998.6
Schaer (1999) Feltens (2007) Mannucci et al. (1998) Zhang et al. (2013) Herna´ndez-Pajares et al. (2009)
Fig. 1. The number of global GNSS stations contributing to daily GIMs calculated by IGS-IAACs from January 1 st, 2009 to December 31st, 2018.
Fig. 2. The mean (left) and standard deviation (right) of the VTEC differences of GIMs from part IAACs, and the evolution of sunspot is also given for the test period.
GIMs at different levels of solar activities from 2009 to 2018. Fig. 2 (left) shows MEAN fluctuations of WHUUPC, CODE-UPC and ESA-UPC are large, and the annual periodic variations are more obvious, while the MEAN of CAS-JPL are relatively stable, which may be related to the different modeling methods of IAACs, and it will be further analyzed below. Additionally, the
MEANs of CODE-JPL and CODE-UPC from 292 day to 365 day in 2010 are larger. Fig. 2 (right) shows the variation trend of the STD is overall consistent with that of the solar activity intensity, and there are certain seasonal and annual periodicity variations, which is related to the ionosphere itself affected by time, season and solar activity.
Please cite this article as: P. Chen, H. Liu, Y. Ma et al., Accuracy and consistency of different global ionospheric maps released by IGS ionosphere associate analysis centers, Advances in Space Research, https://doi.org/10.1016/j.asr.2019.09.042
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CODE, ESA, CAS and WHU use SH functions to produce the corresponding GIMs, while JPL uses three-shell model as shown in Table 1. In order to further explore the variation regulations between the mean of the VTEC differences of GIMs and the modeling methods, the MEAN between JPL GIMs and other GIMs are given in this study, which can be seen from Fig. 3 (left). And the mean of the VTEC differences of GIMs developed by the SH functions are presented in Fig. 3 (right). Fig. 3 (left) shows that the MEAN between other IAACs and JPL is relatively stable with a systematic bias of 2 TEC units (1 TECU = 1016 el/m2). Moreover, the MEAN among the four IAACs modeled by the SH functions has a good consistency and no obvious systematic bias. Tables 2 and 3 present the accuracy statistics between these six GIMs in 2009 and 2015, respectively. When the solar activity is high (2015), the absolute values of the maximum and minimum errors are significantly larger than those during low solar activity (2009). In 2009, the maximum bias between GIMs exceeds 40 TECU, while nearly 80 TECU in 2015, which indicates that the level of solar activity has an significant impact on the GIMs products. Whether it is high solar activity (2015) or low solar activity (2009), the MEAN between JPL and other IAACs is noticeable. The MEAN, STD and root mean square (RMS) of the VTEC differences of GIMs among CAS, CODE, ESA, and WHU is closer and has better consistency than UPC and JPL. However, it shows that the STD between ESA and other IAACs using SH functions is larger, while CODE-CAS is the smallest in 2009 and 2015, this is because these four GIMs from CAS, CODE, ESA, and WHU modeled by using the SH functions, but their processing strategies used in GIMs computation are different.
According to the figure, the MEAN of IGS-UPC is larger than that between IGS and other IAACs. The MEAN values of IGS-CODE, IGS-ESA, IGS-CAS and IGS-WHU are basically positive, and the consistency is better. The MEAN of IGS-JPL is relatively gentle and the values are mainly negative. The above phenomenon may be related to different modeling methods and the mapping functions used in TEC computation. Moreover, the MEAN variation of IGS-CODE at the end of 2010 is obvious as mentioned above, because IGS GIMs are generated by combining the GIMs of CODE, ESA, JPL and UPC with the corresponding weights. When CODE GIMs have large bias, IGS GIMs will be affected. In addition, the variation trend of the STD is consistent with the level of solar activity. Besides, the STD of IGS-CODE is small whether it is high or low solar activity, indicating that there is a good agreement between IGS GIMs and CODE GIMs. Table 4 shows annual accuracy statistics of the IGS combined GIMs with respect to these GIMs from 2009 to 2018. In terms of STD, the annual STD is consistent with the level of solar activity. When the solar activity is the strongest (2014), the annual STD reaches the maximum, and the STD of each IAAC in the high solar activity is larger than that in the low solar activity. At the same time, the difference of IGS-CODE is the smallest, and thus they have a good consistency. In terms of the MEAN, JPLIGS is large, indicating that there is a relatively obvious systematic bias, while UPC-IGS is the smallest. The preliminary analysis is mainly related to the modeling methods.
3. Validation with GPS TEC 3.1. GPS data and computation
2.2. Validation with the IGS combined final GIMs Fig. 4 depicts the mean and standard deviation of the VTEC differences between IAACs and IGS at different levels of solar activities from 2009 to 2018, respectively.
To further analyze the accuracy and reliability of these GIMs from six IAACs, 26 IGS tracking stations are randomly selected on a global scale to perform the test from 2009 to 2018. The distribution of the selected stations is illustrated in Fig. 5, which is evenly distributed over the
Fig. 3. The mean of the VTEC differences of the GIMs from CODE, ESA, CAS, and WHU relative to JPL (left). The mean of the VTEC differences between the four GIMs developed by the SH functions, including CODE, ESA, CAS, and WHU (right).
Please cite this article as: P. Chen, H. Liu, Y. Ma et al., Accuracy and consistency of different global ionospheric maps released by IGS ionosphere associate analysis centers, Advances in Space Research, https://doi.org/10.1016/j.asr.2019.09.042
P. Chen et al. / Advances in Space Research xxx (2019) xxx–xxx
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Table 2 This is multiple comparisons result of a reference epoch in 2009 (unit: TECU). Agency
Agency
Maximum
CAS
CODE ESA JPL UPC WHU
16.9 23.6 12.8 36.8 25.1
CODE
CAS ESA JPL UPC WHU
ESA
MEAN
STD
RMS
37.9 23.9 20.1 42.1 35.0
0.07 0.09 2.10 1.06 0.18
0.98 1.76 1.19 2.21 1.45
0.98 1.77 2.42 2.45 1.46
37.9 37.8 36.5 36.9 23.1
16.9 22.0 20.4 43.2 34.2
0.07 0.02 2.17 1.13 0.11
0.98 1.78 1.43 2.27 1.37
0.98 1.78 2.60 2.53 1.37
CAS CODE JPL UPC WHU
23.9 22.0 23.7 29.0 24.7
23.6 37.8 26.9 44.4 36.9
0.09 0.02 2.19 1.15 0.09
1.76 1.78 1.88 2.42 1.70
1.77 1.78 2.89 2.68 1.70
JPL
CAS CODE ESA UPC WHU
20.1 20.4 26.9 36.3 28.5
12.8 36.5 23.7 40.4 31.2
2.10 2.17 2.19 1.04 2.28
1.19 1.43 1.88 2.31 1.69
2.42 2.60 2.89 2.54 2.84
UPC
CAS CODE ESA JPL WHU
42.1 43.2 44.4 40.4 44.3
36.8 36.9 29.0 36.3 34.4
1.06 1.13 1.15 1.04 1.24
2.21 2.27 2.42 2.31 2.38
2.45 2.53 2.68 2.54 2.68
WHU
CAS CODE ESA JPL UPC
35.0 34.2 36.9 31.2 34.4
25.1 23.1 24.7 28.5 44.3
0.18 0.11 0.09 2.28 1.24
1.45 1.37 1.70 1.69 2.38
1.46 1.37 1.70 2.84 2.68
world, and thus can reflect the performance of these GIMs at various latitudes and longitudes. The computation process of GPS TEC is as follows: Firstly, the data of tracking stations is preprocessed, and then the DCBs (Differential Code Bias) are corrected directly by the DCB product released by the CODE. Finally, the slant total electron content (STEC) at each Ionospheric pierce points is obtained and projected to the VTEC in the zenith direction. The specific calculation formula is detailed in reference (Schaer, 1999). 3.2. TEC-GPS assessment results In order to better attribute the data variations under different levels of solar activity and at different latitudes, we select three tracking stations: FAIR (147.50°W, 64.98°N), MIZU (141.13°E, 39.14°N) and BOGT (74.08°W, 4.64° N) as the data representatives of high-latitude, midlatitude and low-latitude, respectively. The mean values of VTEC data throughout each day are shown in Fig. 6. It can be seen that the higher the latitude is, the smaller the mean values of the daily VTEC are, that is because the ionization energy of the ionosphere mainly comes from the sun, and it is obvious that the low-latitude solar radia-
Minimum
tion is higher, the VTEC in the low latitude area is higher. In addition, as the latitude increases, the variation of VTEC becomes smaller with the solar activity intensity. To know the long-term variation characteristics of the ERROR (relative error) and STD results of the six GIMs products relative to the ionospheric observations from GPS stations, the statistical results of three stations from different latitudes are plotted in Fig. 7. In terms of the ERROR, the relative errors of JPL and UPC in high latitude are larger than that of other centers. Moreover, when the solar activity is low, the ERROR is significantly larger than the high solar activity at the high latitude. On the one hand, the ionosphere in the high latitude is quiet compared to the middle and low latitudes. On the other hand, in the years when the solar activity is low, the TEC in the high latitude is even smaller, and thus the ERROR will be larger with small ionospheric deviations. At the middle (MIZU) and low latitude (BOGT), most ERROR between the measured VTEC and the GIMs VTEC data of each IAAC is less than 30%, which indicates that they have a good consistency. The STD of ESA-FAIR and ESA-MIZU decreased significantly in late 2013, mainly due to the increase in the number of stations used to calculate GIMs per day in the late 2013, as shown in Fig. 1. What’s more,
Please cite this article as: P. Chen, H. Liu, Y. Ma et al., Accuracy and consistency of different global ionospheric maps released by IGS ionosphere associate analysis centers, Advances in Space Research, https://doi.org/10.1016/j.asr.2019.09.042
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Table 3 This is multiple comparisons result of a reference epoch in 2015 (unit: TECU). Agency
Agency
Maximum
CAS
CODE ESA JPL UPC WHU
40.3 63.8 41.6 48.0 59.3
CODE
CAS ESA JPL UPC WHU
ESA
Minimum
MEAN
STD
RMS
38.0 79.7 43.8 47.6 46.1
0.08 0.13 2.18 0.64 0.37
1.60 3.28 2.35 2.95 2.73
1.60 3.29 3.21 3.02 2.75
38.0 60.8 45.1 39.0 54.2
40.3 77.7 51.8 46.9 54.7
0.08 0.06 2.24 0.68 0.30
1.60 3.17 2.69 3.05 2.57
1.60 3.17 3.50 3.13 2.59
CAS CODE JPL UPC WHU
79.7 77.7 75.5 78.4 78.1
63.8 60.8 64.4 53.8 55.9
0.13 0.06 2.31 0.75 0.24
3.28 3.17 3.69 3.15 2.61
3.29 3.17 4.35 3.24 2.62
JPL
CAS CODE ESA UPC WHU
43.8 51.8 64.4 53.5 61.7
41.6 45.1 75.5 51.3 51.8
2.18 2.24 2.18 2.18 2.55
2.35 2.69 2.35 2.35 3.36
3.21 3.50 3.21 3.21 4.22
UPC
CAS CODE ESA JPL WHU
47.6 46.9 53.8 51.3 46.7
48.0 39.0 78.4 53.5 51.9
0.64 0.68 0.75 1.56 0.99
2.95 3.05 3.15 3.04 3.27
3.02 3.13 3.24 3.42 3.42
WHU
CAS CODE ESA JPL UPC
46.1 54.7 55.9 51.8 51.9
59.3 54.2 78.1 61.7 46.7
0.37 0.30 0.24 2.55 0.99
2.73 2.57 2.61 3.36 3.27
2.75 2.59 2.62 4.22 3.42
Fig. 4. The mean (left) and standard deviation (right) of the VTEC differences of GIMs from six IAACs with regard to IGS GIMs, and the evolution of sunspot is also given at different levels of solar activities.
the STD in the low latitude (BOGT) has obvious fluctuations, indicating that the stability is poor. The mean and standard deviation of VTEC differences between GIMs and GPS-VTEC during the experimental period are listed in Fig. 8. Regardless of whether it is a high-latitude, mid-latitude or low-latitude station, the MEAN of VTEC differences between the JPL GIMs and
the measured data is basically larger, indicating that there is a large systematic bias, and the MEAN of CAS and CODE has a good consistency. Moreover, the MEAN of UPC in low latitude region are greater, which agrees with the analysis of Xiang et al. (2015). In terms of STD, the results in the low latitude are also larger than that at the middle and high latitudes overall. Whether it is high, mid-
Please cite this article as: P. Chen, H. Liu, Y. Ma et al., Accuracy and consistency of different global ionospheric maps released by IGS ionosphere associate analysis centers, Advances in Space Research, https://doi.org/10.1016/j.asr.2019.09.042
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Table 4 Statistics of the differences between the GIMs from six IAACs and the IGS combined GIMs for the test period (unit: TECU). Year
IGS-CAS
IGS-CODE
IGS-ESA
IGS-JPL
IGS-UPC
IGS-WHU
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Average
(0.76,0.97) (1.01,1.27) (0.70,1.55) (0.65,1.78) (0.66,1.69) (0.68,1.76) (0.92,1.42) (1.05,2.91) (1.11,1.36) (1.15,1.01) (0.87,1.67)
(0.83,0.95) (0.38,1.63) (0.79,1.66) (0.76,1.93) (0.76,1.82) (0.75,1.87) (0.99,1.20) (0.85,0.99) (0.93,0.62) (0.92,0.53) (0.79,1.42)
(0.85,1.30) (1.15,1.91) (1.03,2.73) (0.94,3.06) (0.91,2.97) (0.92,3.15) (1.05,3.10) (0.83,2.20) (0.58,1.69) (1.08,1.47) (0.93,2.46)
( ( ( ( ( ( ( ( ( ( (
( 0.30,1.67) (0.07,1.81) ( 0.13,2.45) ( 0.07,2.67) ( 0.07,2.94) (0.05,3.38) (0.30,2.73) (0.13,2.01) (0.07,1.65) (0.06,1.34) (0.01,2.38)
(0.94,1.31) (0.88,1.71) (0.93,2.43) (0.90,2.56) (0.92,2.48) (1.11,2.85) (1.29,2.60) (0.89,2.05) (0.27,2.06) (0.73,1.27) (0.89,2.21)
1.34,1.04) 1.17,1.31) 1.52,1.70) 1.57,2.11) 1.50,2.05) 1.48,2.25) 1.26,1.53) 1.29,1.17) 1.22,0.80) 1.08,0.62) 1.34,1.56)
Note: A and B in (A, B) represent the MEAN and STD, respectively.
Fig. 5. Distribution of the selected global IGS GPS stations.
dle or low latitude, the STD of CODE is the smallest. The differences of the STD among CAS, CODE and WHU are smaller at most stations. 4. Validation with the Jason2 based ionospheric VTEC 4.1. VTEC of ocean altimetry satellite The currently operating ocean altimetry satellites are mainly Jason2 and Jason3. These satellites have the orbital height of 1336 km, the orbital inclination of 66.04°, the lat-
itude coverage between 66.15oS–66.15oN and the return period of 9.9156 days (Brunini et al., 2005). Since Jason2 data can cover the entire test period, we select the Jason2 data in this study, and its satellite transmits dualfrequency signals, i.e., Ku-band (13.575 GHz) and Cband (5.3 GHz), and VTEC can be directly obtained. Jason2 altimeter satellite can not only be used as an independent TEC data source, but also obtain observations from areas that are difficult to observe by GNSS, such as ocean areas or somewhere that far away from the receivers. Therefore, the TEC can be compared to GIMs TEC. The sampling frequency of the Jason2 ocean altimeter satellite is 1 Hz, and Jason2 advances by 1° in about 18 s. This paper performs median smoothing on the VTEC data in 18 s. Fig. 9 shows the smoothed VTEC data distribution on January 1, 2015. The figure shows that the ionospheric VTEC is higher at low latitudes and the maximum value is close to 70 TECU. In order to reflect the variation characteristics of daily ionospheric TEC under different levels of the solar activity, the variations of mean TEC data on a daily basis are shown in Fig. 10, and is consistent with the level of solar activity. Note that the time resolution of the final GIMs from IAACs used is two hours, whereas the Jason2 ionospheric
Fig. 6. Time series of daily mean VTEC for FAIR (high latitude), MIZU (middle latitude), BOGT (low latitude) and sunspot number for the test period.
Please cite this article as: P. Chen, H. Liu, Y. Ma et al., Accuracy and consistency of different global ionospheric maps released by IGS ionosphere associate analysis centers, Advances in Space Research, https://doi.org/10.1016/j.asr.2019.09.042
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Fig. 7. The relative error (left) and standard deviation (right) results of the VTEC differences of the six GIMs products relative to the ionospheric observations from three selected stations from 2009 to 2018.
Fig. 8. The mean (top) and standard deviation (bottom) results of the VTEC differences of the six GIMs products relative to the ionospheric observations from the selected GPS stations for the test period.
TEC time resolution is 18 s in this paper. Therefore, the linear interpolation method is used to interpolate the GIMs data, and the Jason2 VTEC is derived from shown in references (Brunini et al., 2005; Yasyukevich et al., 2010).
4.2. VTEC-Jason2 assessment results Fig. 11 depicts the relative error and standard deviation between Jason2 and six GIMs at different levels of solar
Please cite this article as: P. Chen, H. Liu, Y. Ma et al., Accuracy and consistency of different global ionospheric maps released by IGS ionosphere associate analysis centers, Advances in Space Research, https://doi.org/10.1016/j.asr.2019.09.042
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Fig. 9. The global distribution and value of Jason2 VTEC data on January 1, 2015.
activities in 2009–2018, respectively. Fig. 11 illustrates that the ERROR is less than 25%. Except for JPL, the ERROR of Jason2 relative to other IAACs is mostly less than 15%. There are two main reasons for this deviation. On the one hand, measurement principle and tracking method of Jason2 are different from those of GNSS satellites, resulting in systematic differences in observation results (Roma-Dollase et al., 2018). On the other hand, the global ionospheric modeling methods and computation strategies of IAACs are different. In the ocean area, Jason2 can
9
directly obtain the ionospheric VTEC, while the GIMs of various IAACs need to use certain extrapolation methods. Moreover, the variation trend of the STD is consistent with the severity of solar activity, and there are certain annual and seasonal periodicity variations, which are related to the ionosphere itself affected by time, season and solar activity. Additionally, whether the solar activity is high or low, the standard deviations of Jason2-UPC and Jason2-CODE are smaller than other analysis centers. Table 5 shows the statistics of the annual mean and annual standard deviation of differences between Jason2 and six GIMs for the test period. On the whole, the annual MEAN and annual STD trends are consistent with the solar activity. When the solar activity is the strongest (2014), the annual STD reaches the maximum, and the variation of the STD are more significant than those of other years. And the annual MEAN value changes relatively smoothly. The MEAN of Jason2-JPL is 2.69 TECU, while Jason2 relative to with other analysis centers is 0.88 to -0.07 TECU. From 2009 to 2018, the annual STD between Jason2 and individual IAAC is 4.44–5.19 TECU.
Fig. 10. Time series of the daily mean Jason2 VTEC and sunspot number in 2009–2018.
Fig. 11. The relative error and standard deviation (right) results of the VTEC differences of Jason2 relative to the six GIMs products, and the evolution of sunspot is also given for the test period.
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Table 5 Statistics of VTEC annual mean and annual standard deviation of differences between Jason2 and six GIMs for the test period (unit: TECU). Year
JAS2-CAS
JAS2-ESA
JAS2-WHU
JAS2-UPC
JAS2-CODE
JAS2-JPL
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Average
(0.67,2.67) (0.16,3.10) ( 0.93,4.67) ( 1.14,4.62) ( 1.29,4.50) ( 1.37,7.78) ( 1.19,6.43) (0.32,6.46) (0.72,3.08) (0.98,2.70) ( 0.47,5.19)
(0.72,3.16) (0.35,3.95) ( 0.48,5.57) ( 0.56,6.18) ( 0.69,6.04) ( 0.91,6.54) ( 0.29,5.57) (0.49,4.05) (0.65,3.47) (1.21,3.09) ( 0.07,5.19)
(1.01,3.05) (0.22,3.60) ( 0.44,5.18) ( 0.72,5.29) ( 0.73,5.25) ( 0.63,7.78) ( 0.32,5.21) (0.45,3.89) (0.50,3.23) (0.73,2.83) ( 0.10,5.03)
(0.23,2.57) ( 0.15,3.07) ( 1.24,4.44) ( 1.43,4.81) ( 1.45,4.72) ( 1.62,6.75) ( 1.25,4.39) ( 0.39,3.15) ( 0.04,2.77) (0.26,2.36) ( 0.88,4.44)
(0.91,2.80) ( 0.37,3.72) ( 0.66,4.94) ( 0.93,4.99) ( 1.06,4.78) ( 0.96,8.54) ( 0.79,5.11) (0.14,3.59) (0.65,2.92) (0.87,2.66) ( 0.33,4.87)
( ( ( ( ( ( ( ( ( ( (
1.59,2.82) 2.18,3.22) 3.14,4.81) 3.32,5.14) 3.33,4.98) 3.38,8.36) 3.21,5.10) 2.33,3.58) 1.75,3.10) 1.41,2.71) 2.69,4.98)
Note: A and B in (A, B) represent the MEAN and STD, respectively.
Since there are more lands in the Northern hemisphere and most of the Southern hemisphere are marine areas, the density of the GNSS tracking stations is much higher in the Northern hemisphere than in the Southern hemisphere. This paper selects two locations at each latitude, i.e., areas near stations and far from stations, respectively. The distribution of smoothed VTEC data on Jason2 ocean altimeter satellite on January 1, 2015 is shown in Fig. 12. Area (a) and area (b) are in low latitude, area (c) and (d) are in middle latitude, area (e) and (f) are high latitude. Areas (a), (c) and (e) belong to marine areas with less reference stations, areas (b), (d) and (f) belong to ocean areas with more tracking stations.
Fig. 13 shows the mean and standard deviation of differences between Jason2 and six GIMs in area (a) and area (b). Area (a) has no IGS tracking station, while there are considerable IGS tracking stations near area (b). The MEAN and STD of the region (b) are overall smaller than the region (a). In addition, when the intensity of solar activity strong, the MEAN and STD of region (a) and region (b) are both larger. Table 6 lists the accuracy statistics of differences between Jason2 and these GIMs in six regions from 2009 to 2018. From the overall perspective, the STD in the mid-latitude (area (d)) with more tracking stations is higher than in the high latitude (area (f)) and low latitude (area
Fig. 12. The distribution of selected GPS stations (purple dots) used to computed the different GIMs corresponding to IAACs. Red rectangles limit the selected six areas at different latitudes. Light blue dots represent the distribution of Jason2 VTEC. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Please cite this article as: P. Chen, H. Liu, Y. Ma et al., Accuracy and consistency of different global ionospheric maps released by IGS ionosphere associate analysis centers, Advances in Space Research, https://doi.org/10.1016/j.asr.2019.09.042
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Fig. 13. Time series of mean (left) and standard deviation (right) of differences between Jason2 and six GIMs in area a (top) and b (bottom).
Table 6 Accuracy statistics of Jason2 relative to each IAAC in six regions from 2009 to 2018 (unit: TECU). Latitude
Area
JAS2-CAS
JAS2-CODE
JAS2-ESA
JAS2-JPL
JAS2-UPC
JAS2-WHU
Low (<30°)
a b
( 1.66,6.17) (0.05,4.32)
(0.97,2.93) (0.45,4.98)
( 0.58,5.35) (1.40,4.40)
( 4.03,6.43) ( 2.53,4.10)
( 0.43,4.66) ( 0.64,4.57)
( 1.46,5.84) (0.94,3.58)
Middle (30–60°)
c d
(0.12,3.61) (0.79,2.46)
(0.78,4.64) (1.17,2.83)
( 0.23,4.35) (1.37,2.42)
( 1.84,3.20) ( 1.23,2.31)
( 1.02,3.47) (0.25,2.22)
(0.25,4.03) (1.90,2.22)
High (>60°)
e f
(1.52,3.46) (1.84,3.28)
(2.13,3.97) (2.50,3.71)
(3.38,4.66) (3.62,4.37)
( 0.24,3.69) ( 0.59,2.72)
(0.03,3.18) (0.70,4.19)
(3.60,3.95) (3.04,3.82)
Note: A and B in (A, B) represent the MEAN and STD, respectively.
(b)), which is mainly due to the relatively stable ionosphere in the mid-latitudes. At the same time, the STD between Jason2 and six GIMs in the areas with more tracking stations is better than that of the regions with fewer tracking stations in different latitude regions. 5. Conclusion In this study, we evaluate and analyze the performance of the global ionospheric maps provided by six IGS IAACs, including CODE, JPL, UPC, CAS, WHU and ESA in three different aspects. The purpose is to provide a thorough assessment of GIMs, mostly in terms of accuracy, to help the ionospheric research, applications and services. The preliminary findings and conclusions are as follows: The comparison of these GIMs shows that the mean of VTEC differences is related to the modeling methods of various IAACs. The variation trend of the STD is consis-
tent with the solar activities, and accompanied by certain seasonal and annual periodic variations. The MEAN between IGS and individual IAAC is about 1.3 to 1.0 TECU, and the STD is about 1.4–2.5 TECU. The validation with GPS TEC shows that the STD at the low latitude is larger than that in the middle and high latitudes overall. Whether it is high, middle or low latitude, the STD of CODE is the smallest, and the differences of the STD between CAS and CODE is smaller at most stations. The validation with the Jason2 VTEC shows that the MEAN of Jason2-JPL is about 2.7 TECU, while Jason2 relative to other IAACs is about 0.8 to 0 TECU, and the STD between Jason2 and IAACs is about 4.4–5.2 TECU. At the same time, the STD of GIMs corresponding to each IAAC varies with the solar activities. When the solar activity is high, the MEAN and STD between the Jason2 and IAACs are significantly larger than the low solar activity. In different latitude areas, the accuracy between Jason2 and each IAAC with more tracking stations is higher than
Please cite this article as: P. Chen, H. Liu, Y. Ma et al., Accuracy and consistency of different global ionospheric maps released by IGS ionosphere associate analysis centers, Advances in Space Research, https://doi.org/10.1016/j.asr.2019.09.042
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that in the areas with fewer tracking stations. In areas with more tracking stations, the accuracy in the mid-latitudes is higher than at high and low latitudes. Acknowledgements The authors are very grateful to the Crustal Dynamics Data Information System (CDDIS) data center for providing observation data and navigation file by the following FTP server: ftp://cddis.gsfc.nasa.gov/pub/gps/data/daily/. The data of the Jason2 are available via the FTP server: ftp://data.nodc.noaa.gov/pub/data.nodc/. The data of the final GIMs products of different IAACs are collected by the Chinese Academy of Sciences and can available via the FTP server: ftp://ftp.gipp.org.cn/product/ionex. We also gratefully acknowledged the use of Generic Mapping Tool (GMT) and MATrix LABoratory (MATLAB) software. This study was funded by the National Natural Science Foundation of China (41404031) and Outstanding Youth Science Fund of Xi’an University of Science and Technology (2018YQ2-10). This study was also supported by the CSC scholarship. References Bent, RB., Llewllyn, SK., 1973. Documentation and description of the Bent ionospheric model. SAMSO Technical, Report, pp, 73–252. Bilitza, D., Reinisch, B., 2015. Preface: International Reference Ionosphere and Global Navigation Satellite Systems. Adv. Space Res. 55 (8), 1913–2148. Brunini, C., Meza, A., Bosch, W., 2005. Temporal and spatial variability of the bias between Topex- and GPS-derived total electron content. J. Geod. 79 (4–5), 175–188. https://doi.org/10.1007/s00190-005-0448-z. Buresova, D., Nava, B., Galkin, I., Galkin, I., 2009. Data ingestion and assimilation in ionospheric models. Ann Geophys. 52 (3–4), 235–253. Chen, P., Yao, Y., Yao, W., 2017. Global ionosphere maps based on GNSS, satellite altimetry, radio occultation and DORIS. GPS Solut. 21 (2), 639–650. https://doi.org/10.1007/s10291-016-0554-9. Coster, A., Komjathy, A., 2008. Space weather and the global positioning system. Space Weather 6, S06D04. https://doi.org/10.1029/ 2008SW000400. Gao, Y., Liu, Z., 2002. Precise ionosphere modeling using regional GPS network data. J. Global Position. Syst. 1 (1), 18–24. Feltens, J., 2007. Development of a new three-dimensional mathematical ionosphere model at European Space Agency/European Space Operations Centre. Space Weather. 5 (12), 1–17. https://doi.org/10.1029/ 2006SW000294. Herna´ndez-Pajares, M., Juan, J., Sanz, J., 1999. New approaches in global ionospheric determination using ground GPS data. J. Atmos. Sol. Terr. Phys. 61 (16), 1237–1247. https://doi.org/10.1016/s1364-6826(99) 00054-1. Herna´ndez-Pajares, M., Juan, J., Sanz, J., Orus, R., Garcia-Rigo, A., Feltens, J., Komjathy, A., Schaer, S., Krankowski, A., 2009. The IGS VTEC maps: a reliable source of ionospheric information since 1998. J. Geod. 83 (3–4), 263–275. https://doi.org/10.1007/s00190-008-0266-1. Herna´ndez-Pajares, M., Roma-Dollase, D., Krankowski, A., Garcı´aRigo, A., Oru´s-Pe´rez, R., 2017. Methodology and consistency of slant
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Please cite this article as: P. Chen, H. Liu, Y. Ma et al., Accuracy and consistency of different global ionospheric maps released by IGS ionosphere associate analysis centers, Advances in Space Research, https://doi.org/10.1016/j.asr.2019.09.042
ScienceDirect Advances in Space Research xxx (2019) xxx–xxx www.elsevier.com/locate/asr
Accuracy and consistency of different global ionospheric maps released by IGS ionosphere associate analysis centers Peng Chen a,b,⇑, Hang Liu a, Yongchao Ma a, Naiquan Zheng a b
a College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China Deutsches Geoda¨tisches Forschungsinstitut der Technischen Universita¨t Mu¨nchen (DGFI-TUM), Arcisstraße 21, 80333 Mu¨nchen, Germany
Received 12 July 2019; received in revised form 20 September 2019; accepted 23 September 2019
Abstract Due to the differences of ionospheric modeling methods and selected tracking stations, the accuracy and consistency of Global Ionospheric Maps (GIMs) released by Ionosphere Associate Analysis Centers (IAACs) are different. In this study, we evaluate and analyze in detail the accuracy and consistency of GIMs final products provided by six IAACs from three different aspects. Firstly, the comparison of these GIMs shows that the mean bias (MEAN) is related to the modeling methods of various IAACs. The variation trend of the standard deviation (STD) is consistent with the solar activities, and accompanied by certain seasonal and annual periodic variations. The MEAN between IGS and each center is about 1.3 to 1.0 TECU, and the STD is about 1.4–2.5 TECU. Secondly, the validation with GPS TEC shows that the STD of CODE is the smallest at various latitudes, and the STD is about 0.7–4.5 TECU. Thirdly, The validation with the Jason2 VTEC shows that the STD between Jason2 and IAACs is about 4.4–5.2 TECU. In addition, the STD between Jason2 and six GIMs in the areas with more tracking stations is better than that of the regions with fewer tracking stations in different latitude regions. Regardless of whether the tracking stations are more or less, the MEAN and STD in high solar activity are larger than in low solar activity. Ó 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.
Keywords: Total electron content; Global ionospheric maps; Satellite altimeter; Accuracy and consistency
1. Introduction The ionosphere is a significant part of the atmosphere, extending from approximately 60 to 1000 km above the Earth’s surface (Gao and Liu, 2002), where free electrons and ions have important implications for radio communications, navigation, satellite positioning, and human space activities. The total electron content (TEC) is defined as the integral of the electron density along a path from the receiver to transmitter, and is one of the most important quantitative characteristics of the ionosphere. Global Navigation ⇑ Corresponding author.
E-mail address: [email protected] (P. Chen).
Satellite Systems (GNSS) can be utilized to monitor spatio-temporal variations of the ionospheric TEC during the last two decades, which has greatly facilitated the ionospheric research and the development of various applications and services (Mannucci et al., 1998; Herna´ndezPajares et al., 1999; Jakowski et al., 2005a, 2005b; Stankov et al., 2006; Coster and Komjathy, 2008; Buresova et al., 2009; Bilitza and Reinisch, 2015). One typical example is that, increasing IAACs use GNSS observation data to calculate GIMs. Since the late 1990s, Jet Propulsion Laboratory (JPL), Center for Orbit Determination in Europe (CODE), Universitat Polite`cnica de Catalunya (UPC) and European Space Operations Center of European Space Agency (ESA) have established global ionospheric models and routinely supplied TEC GIMs on
https://doi.org/10.1016/j.asr.2019.09.042 0273-1177/Ó 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.
Please cite this article as: P. Chen, H. Liu, Y. Ma et al., Accuracy and consistency of different global ionospheric maps released by IGS ionosphere associate analysis centers, Advances in Space Research, https://doi.org/10.1016/j.asr.2019.09.042
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a daily basis, respectively (Mannucci et al., 1998; Schaer, 1999; Herna´ndez-Pajares et al., 1999, 2009), and the International GNSS Service (IGS) has been openly providing final TEC GIMs in the uniform IONosphere Map Exchange (IONEX) format by comparing and combining the results of IAACs GIMs with the corresponding weights (Herna´ndez-Pajares et al., 2009). Canadian Geodetic Survey of Natural Resources Canada (NRCan) has resumed the submission of GIMs to IGS since April 2015, and Chinese Academy of Sciences (CAS) and Wuhan University (WHU) started to officially provided GIMs products and services in 2016. Additionally, DGFI-TUM also became new member of IGS IAACs in 2018 (Villiger and Dach, 2019). In terms of evaluating the accuracy performance of GIMs, Ho et al. (1996) concluded that the TEC derived from GIMs has a good agreement with TOPEX /Poseidon measurement. Oru´s et al. (2002) compared the ionospheric correction effects of GIMs, IRI (International Reference Ionosphere) model (Rawer et al., 1978) and Bent model (Bent and Llewllyn, 1973), and held that the performance of GIMs was better than IRI and Bent models. Herna´ndez-Pajares et al. (2009) fully verified the generation of GIMs and compared them with altimeter VTEC (vertical total electron content) measurements to validate reliability of GIMs. Luo et al. (2014) evaluated performance of five global ionospheric models, and found that IRI model and GIMs have the best consistency. Xiang et al. (2015) conducted thorough accuracy analysis of four different GIMs products over China, and suggested that UPC GIMs have strong applicability in solar maximum and low-latitude region. Chen et al. (2017) focused on the uneven distribution of GNSS tracking stations, integrated GNSS, satellite altimetry, radio occultation and DORIS (Doppler orbitography and radio positioning integrated by satellite) data to develop multi-source global ionospheric model, and the accuracy and reliability of GIMs in marine was improved significantly after fusing. Li et al. (2017) assessed and analyzed the internal and external accuracies of five different GIMs during two solar activity cycles, and offered systematic bias between individual IAAC GIMs and different altimeter satellites. Herna´ndezPajares et al. (2017) checked the consistency of GIMs by means of two independent and complementing assessing methods, i.e., dSTEC-GPS and VTEC-altimeter from 2010 to 2016. Roma et al. (2018) introduced detailed methods used by IAACs and compared the classical ones (CODE, ESA, JPL and UPC) with the new ones (NRCAN, CAS, WHU). GIMs can provide ionospheric temporal and spatial variation information, which greatly facilitates ionospheric scientific research and ionospheric correction for singlefrequency receivers. However, analyses of the GIMs accuracy over extended periods and on a global scale are still rare. This study not only validates and contrasts the consistency of six GIMs corresponding to different IAACs (CODE, JPL, UPC, CAS, ESA and WHU), but also
evaluate and analyze in detail the accuracy performance of GIMs by means of GPS (Global Positioning System) and altimeter satellite TEC observations. To examine the consistency and accuracy of these GIMs, three methods are applied as follows: Firstly, Section 2 analyzes the consistency of these six GIMs; Secondly, validation with VTEC derived from measured GNSS observations is investigated in Section 3. Thirdly, Section 4 presents the accuracy performance of GIMs by comparing with Jason2. Finally, The preliminary findings and conclusions are summarized in Section 5. 2. Consistency with each other There are several versions of GIMs - final (latency 1– 2 weeks), rapid (latency 1 day), predicted. The final GIMs are more reliable and practical, hence, this paper is devoted to analyze the accuracy and consistency of final GIMs. In the first step, we reflect on whether the modeling methods and data sources of the six GIMs assessed are consistent with each other, which is conducive to the data analysis of results, and these differences are summarized in Table 1. As shown in table, ‘‘CASG‘‘, ‘‘CODG”, ‘‘ESAG‘‘, ‘‘JPLG”, ‘‘UPCG‘‘, ‘‘WHUG” and ‘‘IGSG‘‘ represent the final GIMs products provided by CAS, CODE, ESA, JPL, UPC, WHU, and IGS respectively, and ‘‘SH” and ‘‘GTS‘‘ signify spherical harmonics and generalized trigonometric series. In order to achieve the primary objective of high-accuracy GIMs to continuously monitor the variation of ionosphere, some IGS-IAACs not only incorporate GLONASS (global navigation satellite system) and even BEIDOU data, but also improve time resolution, and the resolution of CAS GIMs reached 30 min in 2016 especially. In the next step, taking into account the effects of solar activities on the ionosphere, the period analyzed includes both high and low solar activities from January 1, 2009 to December 31, 2018, which allows for more detailed and thorough accuracy analysis and evaluation of the final GIMs. Moreover, considering the impact of station number on GIMs computation, the number comparison of global tracking stations used to compute daily GIMs at IAACs is shown in Fig. 1. According to the figure, there are large differences in the number of tracking stations used by each IAAC, but they are basically between 100 and 500. The number of stations used in JPL GIMs computation significantly lower than that of other IAACs. Besides, CAS and WHU have officially provided GIMs products and services, and the number of contributing stations of CAS increases from approximately 270 to over 400 at the beginning of 2016. 2.1. Validation with each other Comparing GIMs from different IAACs can reflect the consistency of the GIMs with respect to modeling methods. We calculate the MEAN and STD among these six
Please cite this article as: P. Chen, H. Liu, Y. Ma et al., Accuracy and consistency of different global ionospheric maps released by IGS ionosphere associate analysis centers, Advances in Space Research, https://doi.org/10.1016/j.asr.2019.09.042
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Table 1 Summary of GIMs corresponding to different IAACs for the test period. GIMs ID
Methods
UPCG CASG
Tomography with splines SH and GTS
CODG ESAG JPLG WHUG IGSG
SH SH Three-shell model SH Weighed mean
Modeling based on singlestation Integrate the global and local models Global modeling Global modeling Global modeling Global modeling
GNSSs
Temporal resolution
Start date
Reference
GPS
1 h, 2h
1998.6
GPS + GLONASS + BEIDOU GPS + GLONASS GPS GPS GPS + GLONASS
30 min, 1h, 2 h
2016
Herna´ndez-Pajares et al. (1999) Li et al. (2015)
1 h, 2h 2h 2h 1 h, 2h 2h
1998.6 1998.6 1998.6 2016 1998.6
Schaer (1999) Feltens (2007) Mannucci et al. (1998) Zhang et al. (2013) Herna´ndez-Pajares et al. (2009)
Fig. 1. The number of global GNSS stations contributing to daily GIMs calculated by IGS-IAACs from January 1 st, 2009 to December 31st, 2018.
Fig. 2. The mean (left) and standard deviation (right) of the VTEC differences of GIMs from part IAACs, and the evolution of sunspot is also given for the test period.
GIMs at different levels of solar activities from 2009 to 2018. Fig. 2 (left) shows MEAN fluctuations of WHUUPC, CODE-UPC and ESA-UPC are large, and the annual periodic variations are more obvious, while the MEAN of CAS-JPL are relatively stable, which may be related to the different modeling methods of IAACs, and it will be further analyzed below. Additionally, the
MEANs of CODE-JPL and CODE-UPC from 292 day to 365 day in 2010 are larger. Fig. 2 (right) shows the variation trend of the STD is overall consistent with that of the solar activity intensity, and there are certain seasonal and annual periodicity variations, which is related to the ionosphere itself affected by time, season and solar activity.
Please cite this article as: P. Chen, H. Liu, Y. Ma et al., Accuracy and consistency of different global ionospheric maps released by IGS ionosphere associate analysis centers, Advances in Space Research, https://doi.org/10.1016/j.asr.2019.09.042
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CODE, ESA, CAS and WHU use SH functions to produce the corresponding GIMs, while JPL uses three-shell model as shown in Table 1. In order to further explore the variation regulations between the mean of the VTEC differences of GIMs and the modeling methods, the MEAN between JPL GIMs and other GIMs are given in this study, which can be seen from Fig. 3 (left). And the mean of the VTEC differences of GIMs developed by the SH functions are presented in Fig. 3 (right). Fig. 3 (left) shows that the MEAN between other IAACs and JPL is relatively stable with a systematic bias of 2 TEC units (1 TECU = 1016 el/m2). Moreover, the MEAN among the four IAACs modeled by the SH functions has a good consistency and no obvious systematic bias. Tables 2 and 3 present the accuracy statistics between these six GIMs in 2009 and 2015, respectively. When the solar activity is high (2015), the absolute values of the maximum and minimum errors are significantly larger than those during low solar activity (2009). In 2009, the maximum bias between GIMs exceeds 40 TECU, while nearly 80 TECU in 2015, which indicates that the level of solar activity has an significant impact on the GIMs products. Whether it is high solar activity (2015) or low solar activity (2009), the MEAN between JPL and other IAACs is noticeable. The MEAN, STD and root mean square (RMS) of the VTEC differences of GIMs among CAS, CODE, ESA, and WHU is closer and has better consistency than UPC and JPL. However, it shows that the STD between ESA and other IAACs using SH functions is larger, while CODE-CAS is the smallest in 2009 and 2015, this is because these four GIMs from CAS, CODE, ESA, and WHU modeled by using the SH functions, but their processing strategies used in GIMs computation are different.
According to the figure, the MEAN of IGS-UPC is larger than that between IGS and other IAACs. The MEAN values of IGS-CODE, IGS-ESA, IGS-CAS and IGS-WHU are basically positive, and the consistency is better. The MEAN of IGS-JPL is relatively gentle and the values are mainly negative. The above phenomenon may be related to different modeling methods and the mapping functions used in TEC computation. Moreover, the MEAN variation of IGS-CODE at the end of 2010 is obvious as mentioned above, because IGS GIMs are generated by combining the GIMs of CODE, ESA, JPL and UPC with the corresponding weights. When CODE GIMs have large bias, IGS GIMs will be affected. In addition, the variation trend of the STD is consistent with the level of solar activity. Besides, the STD of IGS-CODE is small whether it is high or low solar activity, indicating that there is a good agreement between IGS GIMs and CODE GIMs. Table 4 shows annual accuracy statistics of the IGS combined GIMs with respect to these GIMs from 2009 to 2018. In terms of STD, the annual STD is consistent with the level of solar activity. When the solar activity is the strongest (2014), the annual STD reaches the maximum, and the STD of each IAAC in the high solar activity is larger than that in the low solar activity. At the same time, the difference of IGS-CODE is the smallest, and thus they have a good consistency. In terms of the MEAN, JPLIGS is large, indicating that there is a relatively obvious systematic bias, while UPC-IGS is the smallest. The preliminary analysis is mainly related to the modeling methods.
3. Validation with GPS TEC 3.1. GPS data and computation
2.2. Validation with the IGS combined final GIMs Fig. 4 depicts the mean and standard deviation of the VTEC differences between IAACs and IGS at different levels of solar activities from 2009 to 2018, respectively.
To further analyze the accuracy and reliability of these GIMs from six IAACs, 26 IGS tracking stations are randomly selected on a global scale to perform the test from 2009 to 2018. The distribution of the selected stations is illustrated in Fig. 5, which is evenly distributed over the
Fig. 3. The mean of the VTEC differences of the GIMs from CODE, ESA, CAS, and WHU relative to JPL (left). The mean of the VTEC differences between the four GIMs developed by the SH functions, including CODE, ESA, CAS, and WHU (right).
Please cite this article as: P. Chen, H. Liu, Y. Ma et al., Accuracy and consistency of different global ionospheric maps released by IGS ionosphere associate analysis centers, Advances in Space Research, https://doi.org/10.1016/j.asr.2019.09.042
P. Chen et al. / Advances in Space Research xxx (2019) xxx–xxx
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Table 2 This is multiple comparisons result of a reference epoch in 2009 (unit: TECU). Agency
Agency
Maximum
CAS
CODE ESA JPL UPC WHU
16.9 23.6 12.8 36.8 25.1
CODE
CAS ESA JPL UPC WHU
ESA
MEAN
STD
RMS
37.9 23.9 20.1 42.1 35.0
0.07 0.09 2.10 1.06 0.18
0.98 1.76 1.19 2.21 1.45
0.98 1.77 2.42 2.45 1.46
37.9 37.8 36.5 36.9 23.1
16.9 22.0 20.4 43.2 34.2
0.07 0.02 2.17 1.13 0.11
0.98 1.78 1.43 2.27 1.37
0.98 1.78 2.60 2.53 1.37
CAS CODE JPL UPC WHU
23.9 22.0 23.7 29.0 24.7
23.6 37.8 26.9 44.4 36.9
0.09 0.02 2.19 1.15 0.09
1.76 1.78 1.88 2.42 1.70
1.77 1.78 2.89 2.68 1.70
JPL
CAS CODE ESA UPC WHU
20.1 20.4 26.9 36.3 28.5
12.8 36.5 23.7 40.4 31.2
2.10 2.17 2.19 1.04 2.28
1.19 1.43 1.88 2.31 1.69
2.42 2.60 2.89 2.54 2.84
UPC
CAS CODE ESA JPL WHU
42.1 43.2 44.4 40.4 44.3
36.8 36.9 29.0 36.3 34.4
1.06 1.13 1.15 1.04 1.24
2.21 2.27 2.42 2.31 2.38
2.45 2.53 2.68 2.54 2.68
WHU
CAS CODE ESA JPL UPC
35.0 34.2 36.9 31.2 34.4
25.1 23.1 24.7 28.5 44.3
0.18 0.11 0.09 2.28 1.24
1.45 1.37 1.70 1.69 2.38
1.46 1.37 1.70 2.84 2.68
world, and thus can reflect the performance of these GIMs at various latitudes and longitudes. The computation process of GPS TEC is as follows: Firstly, the data of tracking stations is preprocessed, and then the DCBs (Differential Code Bias) are corrected directly by the DCB product released by the CODE. Finally, the slant total electron content (STEC) at each Ionospheric pierce points is obtained and projected to the VTEC in the zenith direction. The specific calculation formula is detailed in reference (Schaer, 1999). 3.2. TEC-GPS assessment results In order to better attribute the data variations under different levels of solar activity and at different latitudes, we select three tracking stations: FAIR (147.50°W, 64.98°N), MIZU (141.13°E, 39.14°N) and BOGT (74.08°W, 4.64° N) as the data representatives of high-latitude, midlatitude and low-latitude, respectively. The mean values of VTEC data throughout each day are shown in Fig. 6. It can be seen that the higher the latitude is, the smaller the mean values of the daily VTEC are, that is because the ionization energy of the ionosphere mainly comes from the sun, and it is obvious that the low-latitude solar radia-
Minimum
tion is higher, the VTEC in the low latitude area is higher. In addition, as the latitude increases, the variation of VTEC becomes smaller with the solar activity intensity. To know the long-term variation characteristics of the ERROR (relative error) and STD results of the six GIMs products relative to the ionospheric observations from GPS stations, the statistical results of three stations from different latitudes are plotted in Fig. 7. In terms of the ERROR, the relative errors of JPL and UPC in high latitude are larger than that of other centers. Moreover, when the solar activity is low, the ERROR is significantly larger than the high solar activity at the high latitude. On the one hand, the ionosphere in the high latitude is quiet compared to the middle and low latitudes. On the other hand, in the years when the solar activity is low, the TEC in the high latitude is even smaller, and thus the ERROR will be larger with small ionospheric deviations. At the middle (MIZU) and low latitude (BOGT), most ERROR between the measured VTEC and the GIMs VTEC data of each IAAC is less than 30%, which indicates that they have a good consistency. The STD of ESA-FAIR and ESA-MIZU decreased significantly in late 2013, mainly due to the increase in the number of stations used to calculate GIMs per day in the late 2013, as shown in Fig. 1. What’s more,
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Table 3 This is multiple comparisons result of a reference epoch in 2015 (unit: TECU). Agency
Agency
Maximum
CAS
CODE ESA JPL UPC WHU
40.3 63.8 41.6 48.0 59.3
CODE
CAS ESA JPL UPC WHU
ESA
Minimum
MEAN
STD
RMS
38.0 79.7 43.8 47.6 46.1
0.08 0.13 2.18 0.64 0.37
1.60 3.28 2.35 2.95 2.73
1.60 3.29 3.21 3.02 2.75
38.0 60.8 45.1 39.0 54.2
40.3 77.7 51.8 46.9 54.7
0.08 0.06 2.24 0.68 0.30
1.60 3.17 2.69 3.05 2.57
1.60 3.17 3.50 3.13 2.59
CAS CODE JPL UPC WHU
79.7 77.7 75.5 78.4 78.1
63.8 60.8 64.4 53.8 55.9
0.13 0.06 2.31 0.75 0.24
3.28 3.17 3.69 3.15 2.61
3.29 3.17 4.35 3.24 2.62
JPL
CAS CODE ESA UPC WHU
43.8 51.8 64.4 53.5 61.7
41.6 45.1 75.5 51.3 51.8
2.18 2.24 2.18 2.18 2.55
2.35 2.69 2.35 2.35 3.36
3.21 3.50 3.21 3.21 4.22
UPC
CAS CODE ESA JPL WHU
47.6 46.9 53.8 51.3 46.7
48.0 39.0 78.4 53.5 51.9
0.64 0.68 0.75 1.56 0.99
2.95 3.05 3.15 3.04 3.27
3.02 3.13 3.24 3.42 3.42
WHU
CAS CODE ESA JPL UPC
46.1 54.7 55.9 51.8 51.9
59.3 54.2 78.1 61.7 46.7
0.37 0.30 0.24 2.55 0.99
2.73 2.57 2.61 3.36 3.27
2.75 2.59 2.62 4.22 3.42
Fig. 4. The mean (left) and standard deviation (right) of the VTEC differences of GIMs from six IAACs with regard to IGS GIMs, and the evolution of sunspot is also given at different levels of solar activities.
the STD in the low latitude (BOGT) has obvious fluctuations, indicating that the stability is poor. The mean and standard deviation of VTEC differences between GIMs and GPS-VTEC during the experimental period are listed in Fig. 8. Regardless of whether it is a high-latitude, mid-latitude or low-latitude station, the MEAN of VTEC differences between the JPL GIMs and
the measured data is basically larger, indicating that there is a large systematic bias, and the MEAN of CAS and CODE has a good consistency. Moreover, the MEAN of UPC in low latitude region are greater, which agrees with the analysis of Xiang et al. (2015). In terms of STD, the results in the low latitude are also larger than that at the middle and high latitudes overall. Whether it is high, mid-
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Table 4 Statistics of the differences between the GIMs from six IAACs and the IGS combined GIMs for the test period (unit: TECU). Year
IGS-CAS
IGS-CODE
IGS-ESA
IGS-JPL
IGS-UPC
IGS-WHU
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Average
(0.76,0.97) (1.01,1.27) (0.70,1.55) (0.65,1.78) (0.66,1.69) (0.68,1.76) (0.92,1.42) (1.05,2.91) (1.11,1.36) (1.15,1.01) (0.87,1.67)
(0.83,0.95) (0.38,1.63) (0.79,1.66) (0.76,1.93) (0.76,1.82) (0.75,1.87) (0.99,1.20) (0.85,0.99) (0.93,0.62) (0.92,0.53) (0.79,1.42)
(0.85,1.30) (1.15,1.91) (1.03,2.73) (0.94,3.06) (0.91,2.97) (0.92,3.15) (1.05,3.10) (0.83,2.20) (0.58,1.69) (1.08,1.47) (0.93,2.46)
( ( ( ( ( ( ( ( ( ( (
( 0.30,1.67) (0.07,1.81) ( 0.13,2.45) ( 0.07,2.67) ( 0.07,2.94) (0.05,3.38) (0.30,2.73) (0.13,2.01) (0.07,1.65) (0.06,1.34) (0.01,2.38)
(0.94,1.31) (0.88,1.71) (0.93,2.43) (0.90,2.56) (0.92,2.48) (1.11,2.85) (1.29,2.60) (0.89,2.05) (0.27,2.06) (0.73,1.27) (0.89,2.21)
1.34,1.04) 1.17,1.31) 1.52,1.70) 1.57,2.11) 1.50,2.05) 1.48,2.25) 1.26,1.53) 1.29,1.17) 1.22,0.80) 1.08,0.62) 1.34,1.56)
Note: A and B in (A, B) represent the MEAN and STD, respectively.
Fig. 5. Distribution of the selected global IGS GPS stations.
dle or low latitude, the STD of CODE is the smallest. The differences of the STD among CAS, CODE and WHU are smaller at most stations. 4. Validation with the Jason2 based ionospheric VTEC 4.1. VTEC of ocean altimetry satellite The currently operating ocean altimetry satellites are mainly Jason2 and Jason3. These satellites have the orbital height of 1336 km, the orbital inclination of 66.04°, the lat-
itude coverage between 66.15oS–66.15oN and the return period of 9.9156 days (Brunini et al., 2005). Since Jason2 data can cover the entire test period, we select the Jason2 data in this study, and its satellite transmits dualfrequency signals, i.e., Ku-band (13.575 GHz) and Cband (5.3 GHz), and VTEC can be directly obtained. Jason2 altimeter satellite can not only be used as an independent TEC data source, but also obtain observations from areas that are difficult to observe by GNSS, such as ocean areas or somewhere that far away from the receivers. Therefore, the TEC can be compared to GIMs TEC. The sampling frequency of the Jason2 ocean altimeter satellite is 1 Hz, and Jason2 advances by 1° in about 18 s. This paper performs median smoothing on the VTEC data in 18 s. Fig. 9 shows the smoothed VTEC data distribution on January 1, 2015. The figure shows that the ionospheric VTEC is higher at low latitudes and the maximum value is close to 70 TECU. In order to reflect the variation characteristics of daily ionospheric TEC under different levels of the solar activity, the variations of mean TEC data on a daily basis are shown in Fig. 10, and is consistent with the level of solar activity. Note that the time resolution of the final GIMs from IAACs used is two hours, whereas the Jason2 ionospheric
Fig. 6. Time series of daily mean VTEC for FAIR (high latitude), MIZU (middle latitude), BOGT (low latitude) and sunspot number for the test period.
Please cite this article as: P. Chen, H. Liu, Y. Ma et al., Accuracy and consistency of different global ionospheric maps released by IGS ionosphere associate analysis centers, Advances in Space Research, https://doi.org/10.1016/j.asr.2019.09.042
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P. Chen et al. / Advances in Space Research xxx (2019) xxx–xxx
Fig. 7. The relative error (left) and standard deviation (right) results of the VTEC differences of the six GIMs products relative to the ionospheric observations from three selected stations from 2009 to 2018.
Fig. 8. The mean (top) and standard deviation (bottom) results of the VTEC differences of the six GIMs products relative to the ionospheric observations from the selected GPS stations for the test period.
TEC time resolution is 18 s in this paper. Therefore, the linear interpolation method is used to interpolate the GIMs data, and the Jason2 VTEC is derived from shown in references (Brunini et al., 2005; Yasyukevich et al., 2010).
4.2. VTEC-Jason2 assessment results Fig. 11 depicts the relative error and standard deviation between Jason2 and six GIMs at different levels of solar
Please cite this article as: P. Chen, H. Liu, Y. Ma et al., Accuracy and consistency of different global ionospheric maps released by IGS ionosphere associate analysis centers, Advances in Space Research, https://doi.org/10.1016/j.asr.2019.09.042
P. Chen et al. / Advances in Space Research xxx (2019) xxx–xxx
Fig. 9. The global distribution and value of Jason2 VTEC data on January 1, 2015.
activities in 2009–2018, respectively. Fig. 11 illustrates that the ERROR is less than 25%. Except for JPL, the ERROR of Jason2 relative to other IAACs is mostly less than 15%. There are two main reasons for this deviation. On the one hand, measurement principle and tracking method of Jason2 are different from those of GNSS satellites, resulting in systematic differences in observation results (Roma-Dollase et al., 2018). On the other hand, the global ionospheric modeling methods and computation strategies of IAACs are different. In the ocean area, Jason2 can
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directly obtain the ionospheric VTEC, while the GIMs of various IAACs need to use certain extrapolation methods. Moreover, the variation trend of the STD is consistent with the severity of solar activity, and there are certain annual and seasonal periodicity variations, which are related to the ionosphere itself affected by time, season and solar activity. Additionally, whether the solar activity is high or low, the standard deviations of Jason2-UPC and Jason2-CODE are smaller than other analysis centers. Table 5 shows the statistics of the annual mean and annual standard deviation of differences between Jason2 and six GIMs for the test period. On the whole, the annual MEAN and annual STD trends are consistent with the solar activity. When the solar activity is the strongest (2014), the annual STD reaches the maximum, and the variation of the STD are more significant than those of other years. And the annual MEAN value changes relatively smoothly. The MEAN of Jason2-JPL is 2.69 TECU, while Jason2 relative to with other analysis centers is 0.88 to -0.07 TECU. From 2009 to 2018, the annual STD between Jason2 and individual IAAC is 4.44–5.19 TECU.
Fig. 10. Time series of the daily mean Jason2 VTEC and sunspot number in 2009–2018.
Fig. 11. The relative error and standard deviation (right) results of the VTEC differences of Jason2 relative to the six GIMs products, and the evolution of sunspot is also given for the test period.
Please cite this article as: P. Chen, H. Liu, Y. Ma et al., Accuracy and consistency of different global ionospheric maps released by IGS ionosphere associate analysis centers, Advances in Space Research, https://doi.org/10.1016/j.asr.2019.09.042
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Table 5 Statistics of VTEC annual mean and annual standard deviation of differences between Jason2 and six GIMs for the test period (unit: TECU). Year
JAS2-CAS
JAS2-ESA
JAS2-WHU
JAS2-UPC
JAS2-CODE
JAS2-JPL
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Average
(0.67,2.67) (0.16,3.10) ( 0.93,4.67) ( 1.14,4.62) ( 1.29,4.50) ( 1.37,7.78) ( 1.19,6.43) (0.32,6.46) (0.72,3.08) (0.98,2.70) ( 0.47,5.19)
(0.72,3.16) (0.35,3.95) ( 0.48,5.57) ( 0.56,6.18) ( 0.69,6.04) ( 0.91,6.54) ( 0.29,5.57) (0.49,4.05) (0.65,3.47) (1.21,3.09) ( 0.07,5.19)
(1.01,3.05) (0.22,3.60) ( 0.44,5.18) ( 0.72,5.29) ( 0.73,5.25) ( 0.63,7.78) ( 0.32,5.21) (0.45,3.89) (0.50,3.23) (0.73,2.83) ( 0.10,5.03)
(0.23,2.57) ( 0.15,3.07) ( 1.24,4.44) ( 1.43,4.81) ( 1.45,4.72) ( 1.62,6.75) ( 1.25,4.39) ( 0.39,3.15) ( 0.04,2.77) (0.26,2.36) ( 0.88,4.44)
(0.91,2.80) ( 0.37,3.72) ( 0.66,4.94) ( 0.93,4.99) ( 1.06,4.78) ( 0.96,8.54) ( 0.79,5.11) (0.14,3.59) (0.65,2.92) (0.87,2.66) ( 0.33,4.87)
( ( ( ( ( ( ( ( ( ( (
1.59,2.82) 2.18,3.22) 3.14,4.81) 3.32,5.14) 3.33,4.98) 3.38,8.36) 3.21,5.10) 2.33,3.58) 1.75,3.10) 1.41,2.71) 2.69,4.98)
Note: A and B in (A, B) represent the MEAN and STD, respectively.
Since there are more lands in the Northern hemisphere and most of the Southern hemisphere are marine areas, the density of the GNSS tracking stations is much higher in the Northern hemisphere than in the Southern hemisphere. This paper selects two locations at each latitude, i.e., areas near stations and far from stations, respectively. The distribution of smoothed VTEC data on Jason2 ocean altimeter satellite on January 1, 2015 is shown in Fig. 12. Area (a) and area (b) are in low latitude, area (c) and (d) are in middle latitude, area (e) and (f) are high latitude. Areas (a), (c) and (e) belong to marine areas with less reference stations, areas (b), (d) and (f) belong to ocean areas with more tracking stations.
Fig. 13 shows the mean and standard deviation of differences between Jason2 and six GIMs in area (a) and area (b). Area (a) has no IGS tracking station, while there are considerable IGS tracking stations near area (b). The MEAN and STD of the region (b) are overall smaller than the region (a). In addition, when the intensity of solar activity strong, the MEAN and STD of region (a) and region (b) are both larger. Table 6 lists the accuracy statistics of differences between Jason2 and these GIMs in six regions from 2009 to 2018. From the overall perspective, the STD in the mid-latitude (area (d)) with more tracking stations is higher than in the high latitude (area (f)) and low latitude (area
Fig. 12. The distribution of selected GPS stations (purple dots) used to computed the different GIMs corresponding to IAACs. Red rectangles limit the selected six areas at different latitudes. Light blue dots represent the distribution of Jason2 VTEC. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Please cite this article as: P. Chen, H. Liu, Y. Ma et al., Accuracy and consistency of different global ionospheric maps released by IGS ionosphere associate analysis centers, Advances in Space Research, https://doi.org/10.1016/j.asr.2019.09.042
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Fig. 13. Time series of mean (left) and standard deviation (right) of differences between Jason2 and six GIMs in area a (top) and b (bottom).
Table 6 Accuracy statistics of Jason2 relative to each IAAC in six regions from 2009 to 2018 (unit: TECU). Latitude
Area
JAS2-CAS
JAS2-CODE
JAS2-ESA
JAS2-JPL
JAS2-UPC
JAS2-WHU
Low (<30°)
a b
( 1.66,6.17) (0.05,4.32)
(0.97,2.93) (0.45,4.98)
( 0.58,5.35) (1.40,4.40)
( 4.03,6.43) ( 2.53,4.10)
( 0.43,4.66) ( 0.64,4.57)
( 1.46,5.84) (0.94,3.58)
Middle (30–60°)
c d
(0.12,3.61) (0.79,2.46)
(0.78,4.64) (1.17,2.83)
( 0.23,4.35) (1.37,2.42)
( 1.84,3.20) ( 1.23,2.31)
( 1.02,3.47) (0.25,2.22)
(0.25,4.03) (1.90,2.22)
High (>60°)
e f
(1.52,3.46) (1.84,3.28)
(2.13,3.97) (2.50,3.71)
(3.38,4.66) (3.62,4.37)
( 0.24,3.69) ( 0.59,2.72)
(0.03,3.18) (0.70,4.19)
(3.60,3.95) (3.04,3.82)
Note: A and B in (A, B) represent the MEAN and STD, respectively.
(b)), which is mainly due to the relatively stable ionosphere in the mid-latitudes. At the same time, the STD between Jason2 and six GIMs in the areas with more tracking stations is better than that of the regions with fewer tracking stations in different latitude regions. 5. Conclusion In this study, we evaluate and analyze the performance of the global ionospheric maps provided by six IGS IAACs, including CODE, JPL, UPC, CAS, WHU and ESA in three different aspects. The purpose is to provide a thorough assessment of GIMs, mostly in terms of accuracy, to help the ionospheric research, applications and services. The preliminary findings and conclusions are as follows: The comparison of these GIMs shows that the mean of VTEC differences is related to the modeling methods of various IAACs. The variation trend of the STD is consis-
tent with the solar activities, and accompanied by certain seasonal and annual periodic variations. The MEAN between IGS and individual IAAC is about 1.3 to 1.0 TECU, and the STD is about 1.4–2.5 TECU. The validation with GPS TEC shows that the STD at the low latitude is larger than that in the middle and high latitudes overall. Whether it is high, middle or low latitude, the STD of CODE is the smallest, and the differences of the STD between CAS and CODE is smaller at most stations. The validation with the Jason2 VTEC shows that the MEAN of Jason2-JPL is about 2.7 TECU, while Jason2 relative to other IAACs is about 0.8 to 0 TECU, and the STD between Jason2 and IAACs is about 4.4–5.2 TECU. At the same time, the STD of GIMs corresponding to each IAAC varies with the solar activities. When the solar activity is high, the MEAN and STD between the Jason2 and IAACs are significantly larger than the low solar activity. In different latitude areas, the accuracy between Jason2 and each IAAC with more tracking stations is higher than
Please cite this article as: P. Chen, H. Liu, Y. Ma et al., Accuracy and consistency of different global ionospheric maps released by IGS ionosphere associate analysis centers, Advances in Space Research, https://doi.org/10.1016/j.asr.2019.09.042
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Please cite this article as: P. Chen, H. Liu, Y. Ma et al., Accuracy and consistency of different global ionospheric maps released by IGS ionosphere associate analysis centers, Advances in Space Research, https://doi.org/10.1016/j.asr.2019.09.042