Ionospheric imaging in Africa

Chartier, Alex T.; Kinrade, Joe; Mitchell, Cathryn N.; Rose, Julian; Jackson, D. R.; Cilliers, Pierre J.; Habarulema, John Bosco; Katamzi, Zama; McKinnell, Lee Anne; Matamba, Tshimangadzo Merline; Opperman, Ben; Ssessanga, Nicholas; Giday, Nigussie Mezgebe; Tyalimpi, Vumile; Franceschi, Giorgiana De; Romano, Vincenzo; Scotto, Carlo; Notarpietro, Riccardo; Dovis, Fabio; Avenant, Eugene; Wonnacott, Richard; Oyeyemi, E. O.; Mahrous, A.; Tsidu, Gizaw Mengistu; Olwendo, Joseph; Sibanda, Patrick; Gogie, Tsegaye Kassa; Rabiu, Babatunde; Jong, Kees de; Adewale, A.O.

doi:10.1002/2013rs005238

Cited by 15 publications

(22 citation statements)

References 13 publications

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“…The reconstructing techniques used in this work estimate TEC better for the midlatitude than the low latitude. This agrees well with what was previously reported about the performance of IRI (Kumar et al, ) and MIDAS (Chartier et al, ) in low latitude and midlatitude. Generally, the difficulty in reconstructing/modeling the low‐latitude ionosphere has been frequently reported (Adewale et al, ; Kenpankho et al, ; Materassi & Mitchell, ; Panda et al, ).…”

Section: Resultssupporting

confidence: 93%

“…Similar to previous studies (Chartier et al, ; Kumar et al, ; Panda et al, ), storm‐time TEC reconstruction/modeling is more difficult for the low‐latitude than midlatitude ionosphere. The fountain effect and the resulting higher TEC gradients over low‐latitude ionosphere are likely to be the causes of the difficulty in reconstructing/modeling TEC over this region.…”

Section: Resultssupporting

confidence: 83%

“…The influence of high TEC variability due to fountain effect on TEC modeling/reconstruction was reported as the major cause that makes modeling difficult for the low‐latitude ionosphere. This observation was reported by Chartier et al () when performing the tomography of the African ionosphere during geomagnetically quiet conditions, and Panda et al () during TEC reconstruction with IRI model over low latitude in the Indian sector. It can therefore be considered that TEC reconstruction is more difficult for the low latitude than midlatitude and this agrees well with what was previously reported (Chartier et al, ; Kumar et al, ).…”

Section: Resultssupporting

confidence: 76%

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Reconstruction of Storm‐Time Total Electron Content Using Ionospheric Tomography and Artificial Neural Networks: A Comparative Study Over the African Region

et al. 2018

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The work presented here aims to evaluate the capabilities of Multi‐Instrument Data Analysis System (MIDAS) compared with artificial neural networks (ANNs) to reconstruct storm‐time total electron content (TEC) over the African low‐latitude and midlatitude regions. For MIDAS, the inversion was done based on the Global Positioning System (GPS) measurements from receiver stations extending from −30∘ to 36∘ in latitude and 30∘ to 44∘ in longitude while for ANNs, individual storm‐time models based on historical GPS data from receivers within the same region covered by MIDAS were used. Based on the minimum Dst index reached during the storm period, moderate (−50 nT ≥Dst> −100 nT), strong (−100 nT ≥Dst>1em−200 nT), and severe (−200 nT ≥Dst>1em−350 nT) storms were used for validation. MIDAS and ANNs results were compared with IRI‐2016 predictions and validated with real GPS TEC observations. A statistical analysis revealed that MIDAS and ANNs provide comparable results in storm‐time TEC reconstruction with average mean absolute errors of 4.81 and 4.18 TECU respectively. However, MIDAS performed better compared to ANNs in following TEC enhancements and depletions as well as short‐term features observed during the selected storm periods. In terms of latitude, it was found that on average, MIDAS performs 13% better than ANNs in the African midlatitude, while ANN model performs 24% better than MIDAS in low latitudes. Furthermore, comparisons with IRI predictions showed that both MIDAS and ANNs produce more accurate estimations of the storm‐time TEC than IRI model.

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Section: Resultssupporting

confidence: 93%

Section: Resultssupporting

confidence: 83%

Section: Resultssupporting

confidence: 76%

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Reconstruction of Storm‐Time Total Electron Content Using Ionospheric Tomography and Artificial Neural Networks: A Comparative Study Over the African Region

et al. 2018

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“…A similar approach was used by Chartier et al (2012Chartier et al ( , 2014. Figure 9a and b show the reconstruction obtained with SH and DM.…”

Section: Noise Sensitivitymentioning

confidence: 99%

Using sparse regularization for multi-resolution tomography of the ionosphere

Panicciari

Smith

Mitchell

et al. 2015

Nonlin. Processes Geophys.

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Abstract. Computerized ionospheric tomography (CIT) is atechnique that allows reconstructing the state of the ionosphere in terms of electron content from a set of slant total electron content (STEC) measurements. It is usually denoted as an inverse problem. In this experiment, the measurements are considered coming from the phase of the GPS signal and, therefore, affected by bias. For this reason the STEC cannot be considered in absolute terms but rather in relative terms. Measurements are collected from receivers not evenly distributed in space and together with limitations such as angle and density of the observations, they are the cause of instability in the operation of inversion. Furthermore, the ionosphere is a dynamic medium whose processes are continuously changing in time and space. This can affect CIT by limiting the accuracy in resolving structures and the processes that describe the ionosphere. Some inversion techniques are based on 2 minimization algorithms (i.e. Tikhonov regularization) and a standard approach is implemented here using spherical harmonics as a reference to compare the new method. A new approach is proposed for CIT that aims to permit sparsity in the reconstruction coefficients by using wavelet basis functions. It is based on the 1 minimization technique and wavelet basis functions due to their properties of compact representation. The 1 minimization is selected because it can optimize the result with an uneven distribution of observations by exploiting the localization property of wavelets. Also illustrated is how the inter-frequency biases on the STEC are calibrated within the operation of inversion, and this is used as a way for evaluating the accuracy of the method. The technique is demonstrated using a simulation, showing the advantage of 1 minimization to estimate the coefficients over the 2 minimization. This is in particular true for an uneven observation geometry and especially for multi-resolution CIT.

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“…They used a simulated scenario to accurately reconstruct the ionosphere using MART and showed that the reconstruction was highly dependent on the initial guess of the ionosphere. Since then, many other simulations have been conducted to analyze the efficiency of ionospheric tomographic algorithms (Raymund et al 1994, Howe et al 1998, Mitchell and Spencer 2003, Thampi et al 2004, Materassi and Mitchell 2005a, b, Wen et al 2012, Chartier et al 2014, Seemala et al 2014 and for making comparison between ART and MART algorithms for ionospheric studies (Das and Shukla 2011). However, in these simulated studies, the analysis of the efficiency of the algorithms uses climatological models to simulate the ionospheric morphology and/or depends on the real scenario of existing ground-based GNSS receivers used to retrieve the simulated observations of TEC.…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulations to Assess ART and MART Performance for Ionospheric Tomography of Chapman Profiles

Prol

Camargo

Muella

2017

An. Acad. Bras. Ciênc.

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The incomplete geometrical coverage of the Global Navigation Satellite System (GNSS) makes the ionospheric tomographic system an ill-conditioned problem for ionospheric imaging. In order to detect the principal limitations of the ill-conditioned tomographic solutions, numerical simulations of the ionosphere are under constant investigation. In this paper, we show an investigation of the accuracy of Algebraic Reconstruction Technique (ART) and Multiplicative ART (MART) for performing tomographic reconstruction of Chapman profiles using a simulated optimum scenario of GNSS signals tracked by ground-based receivers. Chapman functions were used to represent the ionospheric morphology and a set of analyses was conducted to assess ART and MART performance for estimating the Total Electron Content (TEC) and parameters that describes the Chapman function. The results showed that MART performed better in the reconstruction of the electron density peak and ART gave a better representation for estimating TEC and the shape of the ionosphere. Since we used an optimum scenario of the GNSS signals, the analyses indicate the intrinsic problems that may occur with ART and MART to recover valuable information for many applications of Telecommunication, Spatial Geodesy and Space Weather.

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Ionospheric imaging in Africa

Cited by 15 publications

References 13 publications

Reconstruction of Storm‐Time Total Electron Content Using Ionospheric Tomography and Artificial Neural Networks: A Comparative Study Over the African Region

Reconstruction of Storm‐Time Total Electron Content Using Ionospheric Tomography and Artificial Neural Networks: A Comparative Study Over the African Region

Using sparse regularization for multi-resolution tomography of the ionosphere

Numerical Simulations to Assess ART and MART Performance for Ionospheric Tomography of Chapman Profiles

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