Real-time Ionosphere Monitoring by Three-dimensional Tomography Over Japan

Saito, Susumu; Suzuki, Shota; Yamamoto, Mamoru; Chen, Chia Hun; Saito, Akinori

doi:10.33012/2016.14818

Cited by 8 publications

(9 citation statements)

References 13 publications

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“…Yin et al (2006) studied the hmF 2 variations during three geomagnetic storms using the Multi‐instrument Data Analysis System (MIDAS) (Mitchell et al, 2002), and their results showed a dramatic increase of hmF 2 during geomagnetic storms. The constrained least squares method (Seemala et al, 2014) is used to reconstruct the density perturbations during medium‐scale traveling ionospheric disturbance (MSTID) events (Chen et al, 2016) and to develop real‐time three‐dimensional ionosphere monitoring (Saito et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

3‐D Tomographic Reconstruction of SED Plume During 17 March 2013 Storm

Zhai

Yao

et al. 2020

JGR Space Physics

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The three-dimensional computerized ionospheric tomography (3DCIT) technique is used to reconstruct the spatial distribution of storm-enhanced density (SED) based on the global positioning system total electron content measurements over the North American area during the 17 March 2013 storm. The reconstruction results are carefully validated with observations from three ionosonde stations, the constellation observing system for meteorology, ionosphere, and climate (COSMIC) radio occultations, and the Millstone Hill incoherent scatter radar. The electron density profiles from the 3DCIT reconstruction show a good agreement with the ionosonde and COSMIC electron density profiles. The 3DCIT-derived electron density difference between the storm day of 17 March and the quiet day of 16 March also captures the similar SED plume signature that was observed by the Millstone Hill incoherent scatter radar. The 3DCIT reconstruction allows us for the first time to unveil the 3-D configuration of the SED plume and its spatiotemporal evolution. It was found that the SED plume first appeared around 400 km and then expanded downward to~300 km as well as upward to~500 km over the course of a 3-hr period from 19 to 22 UT on 17 March. Our study also showed that the density enhancement within the SED plume occurred mostly above the storm time F layer peak height.

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Section: Introductionmentioning

confidence: 99%

3‐D Tomographic Reconstruction of SED Plume During 17 March 2013 Storm

Zhai

Yao

et al. 2020

JGR Space Physics

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“…To improve on estimations of climatologic models and still obtain a full 3-D picture of the ionosphere, tomography techniques have extensively been applied (e.g., Hong et al, 2017;Raymund et al, 1990;Saito et al, 2016;Seemala et al, 2014;Ssessanga et al, 2015Ssessanga et al, , 2017Tsai et al, 2002). However, ionosphere tomography techniques have a deficiency of not being able to adequately spread observation information within the grid, specifically over remote areas where measurements are lacking.…”

Section: Introductionmentioning

confidence: 99%

Assimilation of Multiple Data Types to a Regional Ionosphere Model With a 3D‐Var Algorithm (IDA4D)

et al. 2019

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For the purpose of building a regional (bound 20–60°N in latitude and 110–160°E in longitude) ionospheric nowcast model, we investigated the performance of IDA4D (Ionospheric Data Assimilation Four‐Dimension) technique considering International Reference Ionosphere model as the background. The data utilized in assimilation were slant total electron content (STEC) from 27 ground GPS (Global Positioning System) receiver stations and NmF2 (ionospheric F2 peak density) from five ionosondes and COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate) Data Analysis Archive Center. The period analyzed covered both geomagnetic quiet and disturbed days (15–18 March 2015). Assimilations were run under the following data combinations (cases): (1) GPS‐STEC's only; (2) GPS‐STEC's and NmF2's from five ionosondes; (3) only NmF2's from five ionosondes; and (4) GPS‐STEC's and NmF2's from both five ionosondes and COSMIC. Results showed that under case 1 the root‐mean‐square error (RMSE) in STEC reduced by 44% over the background International Reference Ionosphere values and on averaged over all ionosonde stations in the analysis RMSE values of foF2 (F2 layer critical frequency) reduced by 21%. Furthermore, foF2 RMSE values under Case 2 were 36% smaller than those under Case 1. Under Case 4, IDA4D performance improved even further in areas not covered by GPS and ionosonde measurements. Therefore, IDA4D is a potential candidate for regional ionosphere modeling that exhibits improved performance with assimilation of different data types.

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“…The downside of Bayesian statistical approach for inverse problems is that the assumption of a proper prior distribution (5) results with a dense N × N covariance matrix pr . The estimators (6) and (7) contain inverted covariance matrices and the posterior covariance estimator involves one more matrix inversion. Hence, the solution becomes exceedingly demanding computationally.…”

Section: Computationmentioning

confidence: 99%

“…In [3], an extensive overview on different approaches and their development is provided. More recently, the topic has been studied in [4]- [7].…”

mentioning

confidence: 99%

Gaussian Markov Random Field Priors in Ionospheric 3-D Multi-Instrument Tomography

Norberg

Vierinen

Roininen

et al. 2018

IEEE Trans. Geosci. Remote Sensing

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In ionospheric tomography, the atmospheric electron density is reconstructed from different electron density related measurements, most often from ground-based measurements of satellite signals. Typically, ionospheric tomography suffers from two major complications. First, the information provided by measurements is insufficient and additional information is required to obtain a unique solution. Second, with necessary spatial and temporal resolutions, the problem becomes very high dimensional, and hence, computationally infeasible. With Bayesian framework, the required additional information can be given with prior probability distributions. The approach then provides physically quantifiable probabilistic interpretation for all model variables. Here, Gaussian Markov random fields (GMRFs) are used for constructing the prior electron density distribution. The use of GMRF introduces sparsity to the linear system, making the problem computationally feasible. The method is demonstrated over Fennoscandia with measurements from global navigation satellite system (GNSS) and low Earth orbit (LEO) satellite receiver networks, GNSS occultation receivers, LEO satellite Langmuir probes, and ionosonde and incoherent scatter radar measurements.Index Terms-Bayesian, Gaussian Markov random fields (GMRFs), ionospheric tomography, multi-instrument.

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Real-time Ionosphere Monitoring by Three-dimensional Tomography Over Japan

Cited by 8 publications

References 13 publications

3‐D Tomographic Reconstruction of SED Plume During 17 March 2013 Storm

3‐D Tomographic Reconstruction of SED Plume During 17 March 2013 Storm

Assimilation of Multiple Data Types to a Regional Ionosphere Model With a 3D‐Var Algorithm (IDA4D)

Gaussian Markov Random Field Priors in Ionospheric 3-D Multi-Instrument Tomography

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