Plasmaspheric Electron Content Inferred from Residuals between GNSS-Derived and TOPEX/JASON Vertical TEC Data

Liu, Lei; Yao, Yibin; Kong, Jian; Shan, Lulu

doi:10.3390/rs10040621

Cited by 22 publications

(13 citation statements)

References 42 publications

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“…Note that the difference in bias between RT and retrospective GIMs is significant, which may be related to the systematic biases of different ionospheric models (e.g., CLK9 vs. UQRG), see previous research in Orús et al (2002); Liu et al (2018), or to the GNSS ionospheric models working in RT or post-processing scenarios (e.g., UQRG vs. USRG), see the comparison and assessment in Li et al (2020). Besides, shown in Figure 12 and associated comments in Hernández-Pajares et al (2009), such few TECU of GIM VTEC greater than altimeter VTEC at low latitudes agrees with the expected plasmaspheric electron content at low latitudes in between altimeter and GPS satellites heights.…”

Section: Bias Of Vtec Jason3 − Vtec Gimmentioning

confidence: 91%

Real-time interpolation of global ionospheric maps by means of sparse representation

Yang

Monte

Hernández‐Pajares

et al. 2021

J Geod

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In this paper, we propose a method for the generation of real-time global ionospheric map (RT-GIM) of vertical total electron content (VTEC) from GNSS measurements. The need for interpolation arises from the fact that the ionospheric pierce point (IPP) measurements from satellites to stations are not distributed uniformly over the ionosphere, leaving unfilled gaps at oceans or poles. The method we propose is based on using a high-quality historical database of post-processed GIMs that comprises more than two solar cycles, calculates the GIM by weighted superposition on a subset of the database with the compatible solar condition. The linear combination of GIMs in the database was obtained by minimizing a 2 distance between VTEC measurements at the IPPs and the VTECs from the database, adding a 1 penalization on the weights to assure a sparse solution. The process uses a Sun-fixed geomagnetic reference frame. This method uses the atomic decomposition/least absolute shrinkage and selection operator (LASSO), which will be denoted as atomic decomposition interpolator of GIMs (ADIGIM). As the computation is done in milliseconds, the interpolation is performed in real time. In this work, two products were developed, denoted as UADG and UARG, the UADG in real time and UARG with a latency of 24 h to benefit from the availability of a greater number of stations. The altimeter JASON3 VTEC measurements were used as reference. The quality of interpolated RT-GIMs from day 258 of 2019 to 155 of the year 2020 is compared with other RT/non-RT GIM products such as those from International GNSS Service (IGS), Centre National d'Etudes Spatiales (CNES), Chinese Academy of Sciences (CAS), Polytechnic University of Catalonia (UPC) and others. The RT ADIGIM performance proved to be better, nearly as good as the rapid or final GIMs computed retrospectively with delays of hours to days. Besides, the non-RT ADIGIM quality is as good or better than most GIM products. The oceanic regions have been included in the assessment which showed that ADIGIM interpolation gives the best estimation (referred to JASON3). The developed method, UADG, will constitute the next-generation UPC RT-GIM, and also UARG will improve the current product UQRG (the current UPC rapid GIM product computed retrospectively) due to its complementary information.Keywords Real-time global ionospheric map (RT-GIM) • Atomic decomposition interpolator of GIMs (ADIGIM) • Sparse representation • Vertical total electron content (VTEC) • Global navigation satellite system (GNSS)

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Section: Bias Of Vtec Jason3 − Vtec Gimmentioning

confidence: 91%

Real-time interpolation of global ionospheric maps by means of sparse representation

Yang

Monte

Hernández‐Pajares

et al. 2021

J Geod

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“…JASON‐3 is a satellite altimeter mission that measures the surface height of global oceans using a dual‐frequency satellite radar. JASON‐3 adopted characteristics of the previous missions (TOPEX, JASON‐1/JASON‐2), operating at 13.575 GHz (Ku band) and 5.3 GHz (C band), flying at an orbit altitude ~1,336 km with an inclination angle of 66° and having a period of 112 min (Fu et al, ; Imel, ; Jee et al, ; Liu et al, ). Ionospheric VTEC from JASON‐3 is a by‐product which is estimated as

JVTEC = \frac{- normald R * f_{Ku}^{2}}{40.3}

where d R is Ku band ionospheric range correction and f Ku is Ku band frequency in Hz (Brunini et al, ; Dumont et al, ).…”

Section: Resultsmentioning

confidence: 99%

On Imaging South African Regional Ionosphere Using 4D‐var Technique

et al. 2019

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One of the major research areas in the space weather community is the ability to understand, characterize, and model a time‐space variant ionosphere through which transionospheric signals propagate. In this paper a strong constraint four‐dimensional variational data assimilation (4D‐var) technique was used to more accurately estimate the South African regional ionosphere (bound latitude 20–35°S, longitude 20–40°E, and altitude 100–1,336 km). The altitude was capped to the JASON‐1 satellite orbital altitude for the purpose of eliminating the plasmasphere contribution hence reducing the computation expense. Background densities were obtained from an empirical internationally recognized ionosphere model (IRI‐2016) and propagated in time using a Gauss‐Markov filter. Ingested data were STECs (slant total electron content) obtained from the South African Global Navigation Satellite System receiver network (TrigNet). The vertically integrated electron content was validated using Global ionosphere Maps and JASON‐3 data over the continent and ocean areas, respectively. Further, vertical profiles after assimilation were compared with data from a network of ground‐based regional ionosondes Hermanus (34.25°S, 19.13°E), Grahamstown (33.3°S, 26.5°E), Louisvale (21.2°S, 28.5°E), and Madimbo (22.4°S, 30.9°E). Results show that assimilation of STEC data has a profound improvement on the estimation of both the horizontal and vertical structures during quiet and storm periods. Accuracy of the horizontal structure decreases from the continent toward the ocean area where GPS receivers are less abundant. Superiority of assimilating STEC is best pronounced during daytime especially when estimating maximum electron density of the F2 layer (NmF2), with a 60% root‐mean‐square error improvement over the background values.

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“…However, both studies directly treated the SA or IRO TEC values, which were greater than IGS GIM TEC as outliers and discarded them in the data processing. Liu et al (2018) noted that TOPEX/Jason TEC values were greater than GNSS-derived TEC while they extracted plasmaspheric TEC and explained the phenomenon as the systematic biases from TOPEX/Jason, which led to an overestimation of TOPEX/Jason VTEC [18].In this research, we evaluated the GNSS GIM TEC with Jason-2 SA and COSMIC IRO TEC and investigated topside TEC variations with local time (LT), latitude, longitude, and season. The global distribution of the difference between space-borne TEC and IGS TEC was plotted to assess anomalous regions and determine possible reasons for such anomalies.…”

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confidence: 98%

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confidence: 99%

Assessing Global Ionosphere TEC Maps with Satellite Altimetry and Ionospheric Radio Occultation Observations

Huang

Zhang

et al. 2019

Sensors

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As global navigation satellite system (GNSS)stations are sparsely distributed in oceanic area, oceanic areas usually have lower precision than continental areas on a global ionosphere maps (GIM). On the other hand, space-borne observations like satellite altimetry (SA) and ionospheric radio occultation (IRO) have substantial dual-frequency observations in oceanic areas, which could be used for total electron content (TEC) retrieval. In this paper, the Jason-2 SA and Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) IRO products were used to assess the precision of IGS GIM products. Both the systematic biases and scaling factors between the international GNSS service (IGS) GIM TEC and space-borne TEC were calculated, and the statistical results show that the biases and the scaling factors obviously vary under different temporal-spatial conditions. This analysis shows that these differences are variable with diurnal and latitude factors, that is, the differences in biases during the day time are higher than those during the night time, and larger biases are experienced at lower latitude areas than at high latitude areas. The results also show that in the southern hemisphere middle-high latitude area and some other central oceanic areas, the space-borne TEC values are even higher than GIM TEC values. As the precision of space-borne TEC should be evenly distributed around different areas on Earth, it can be explain that the TEC in these areas is undervalued by the current GIM model, and the space-borne SA and IRO techniques could be used as complementary observations to improve the accuracy and reliability of TEC values in these areas. maps using the Kriging interpolation algorithm, and the new Polytechnic University of Catalonia (UPC) kriging GIM has a lower RMS of about 16% and 2%, respectively, in the calculated slant TEC (STEC) than the original UPC GIM and IGS GIM in the self-consistency test [4]. Mautz et al. (2005) presented the B-spline wavelets method to represent the spatial and temporal variations of GIM and to solve the problems in ionospheric modeling due to data gaps [5]. The inequality-constrained least square (ICLS) method was proposed by Zhang et al. (2013) to eliminate nonphysical negative values in ionosphere associate analysis centers (IAACs) GIM products [2]. Li et al. (2015) proposed an approach named Spherical Harmonic plus generalized Trigonometric Series functions (SHPTS) to improve the accuracy and resolution of GIM; the accuracy of the SHPTS-based GIM could achieve 2-6 TECU over the area without GNSS measurements [6]. Wang et al. (2016) used priori VTEC values calculated from the IRI 2012 model to replace the grid points of GIM with negative VTEC values [7]. However, such interpolation and background constraint methods could not fully reflect the real situation of the ionosphere in ocean areas due to a lack of GNSS observations.As satellite-based observations are not constrained by the Earth's surface patterns, researchers are trying to use satellite-based dua...

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Plasmaspheric Electron Content Inferred from Residuals between GNSS-Derived and TOPEX/JASON Vertical TEC Data

Cited by 22 publications

References 42 publications

Real-time interpolation of global ionospheric maps by means of sparse representation

Real-time interpolation of global ionospheric maps by means of sparse representation

On Imaging South African Regional Ionosphere Using 4D‐var Technique

Assessing Global Ionosphere TEC Maps with Satellite Altimetry and Ionospheric Radio Occultation Observations

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