Matrix completion methods for the total electron content video reconstruction

Sun, Hu; Zhijun, Hua,; Ren, Jiaen; Zou, Shasha; Sun, Yuekai; Chen, Yang

doi:10.1214/21-aoas1541

Cited by 9 publications

(16 citation statements)

References 34 publications

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“…With altered color scale, the storm-time enhanced density in the mid-latitude is also apparent. Figures 10,11 The validity of the completed TEC maps that we give in the constructed TEC database is manifested by the extensive numerical experiments shown in 17 . Through both simulated experiments and real data application, we demonstrate that the imputed TEC maps provide a reliable estimate of the global TEC, evaluated by the (test set) mean-squared error and relative-squared error.…”

Section: Data Recordsmentioning

confidence: 72%

“…Despite the wide usage of TEC in the ionosphere and space weather community, there are a couple of challenges in using the global TEC map data including large data gaps over oceans and potentially losing meso-scale ionospheric structures when applying typical reconstruction and smoothing algorithms, such as Spherical Harmonics fitting. We have introduced a video imputation algorithm 17 , Video Imputation with SoftImpute, plete TEC maps is the VISTA method 17 , which is based on matrix completion theory [22][23][24][25] and is extended to account for various complexities in TEC observations. It is capable of processing time series of matrix data with missing values: with an output of a complete series of the time sequence of matrices by filling in reasonable values in the missing entries.…”

Section: Background and Summarymentioning

confidence: 99%

“…To estimate A 1:T , B 1:T , the VISTA method aims at solving the following optimization problem: T are set to 0, and ||.|| F is the matrix Frobenius norm. We refer our readers to the algorithm paper 17 for more explanations. In this work, the auxiliary data Y t is obtained by applying the spherical harmonics 26 fitting over X t , independently for all t. Spherical harmonics fitting with a lower order typically results in overly smoothing the data thus not complying with the observations as desired, but they can provide a coarse imputation with great spatial smoothness; on the contrast, spherical harmonics fitting with a high order may create artificial, undesirable structures in the imputed maps.…”

Section: Background and Summarymentioning

confidence: 99%

“…In the next two subsections, we describe the parameter tuning for spherical harmonics fitting and the VISTA algorithm in details. See 17 2015-Sep is a stormy month with much higher activity level based on the geomagnetic Dst index 29 , which estimates the magnitude of the ring current in the inner magnetosphere. The other three months are non-stormy months in different phases of the solar cycle and different season, which makes the four months of data more representative of the whole Madrigal TEC database.…”

Section: Model Fitting: Standardization and Spherical Harmonics Algor...mentioning

confidence: 99%

“…Figure 12 shows the mean and standard deviation of the yearly residual for both the Madrigal TEC and our VISTA database. Since the VISTA algorithm typically performs better on the pixels with original observations 17 , we differentiate the pixels in the VISTA TEC based on whether the pixel has the original Madrigal TEC observation. All pixels of VISTA TEC with Madrigal TEC observations are labelled as "(Madrigal = A)", and "(Madrigal = NA)" is used to denote the remaining pixels of VISTA TEC without the corresponding Madrigal TEC observations.…”

Section: Data Recordsmentioning

confidence: 99%

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Complete Global Total Electron Content Map Dataset based on a Video Imputation Algorithm VISTA

et al. 2023

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Ionospheric total electron content (TEC) derived from multi-frequency Global Navigation Satellite System (GNSS) signals and the relevant products have become one of the most utilized parameters in the space weather and ionospheric research community. However, there are a couple of challenges in using the global TEC map data including large data gaps over oceans and the potential of losing meso-scale ionospheric structures when applying traditional reconstruction and smoothing algorithms. In this paper, we describe and release a global TEC map database, constructed and completed based on the Madrigal TEC database with a novel video imputation algorithm called VISTA (Video Imputation with SoftImpute, Temporal smoothing and Auxiliary data). The complete TEC maps reveal important large-scale TEC structures and preserve the observed meso-scale structures. Basic ideas and the pipeline of the video imputation algorithm are introduced briefly, followed by discussions on the computational costs and fine tuning of the adopted algorithm. Discussions on potential usages of the complete TEC database are given, together with a concrete example of applying this database.

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Section: Data Recordsmentioning

confidence: 72%

Section: Background and Summarymentioning

confidence: 99%

Section: Background and Summarymentioning

confidence: 99%

Section: Model Fitting: Standardization and Spherical Harmonics Algor...mentioning

confidence: 99%

Section: Data Recordsmentioning

confidence: 99%

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Complete Global Total Electron Content Map Dataset based on a Video Imputation Algorithm VISTA

et al. 2023

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Reconstruction of the Regional Total Electron Content Maps Over the Korean Peninsula Using Deep Convolutional Generative Adversarial Network and Poisson Blending

et al. 2022

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The Global Navigation Satellite System (GNSS) is a generic term for the Earth-orbiting satellite constellation that transmits positioning and timing data to users. Besides providing precise positioning and timing information to applications requiring them, GNSS products offer valuable resources for ionospheric research and space weather. One of the key products of GNSS is the total electron content (TEC), the electron column number density from GNSS satellites to receivers. Although the GNSS satellite orbits are above 20,000 km, the TEC is predominantly determined by the dense electron density in the ionospheric F region at a height of 200-1,000 km. Because the GNSS TEC provides diagnostics of ionospheric conditions, TEC measurements on the ground and from space

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Phase and Amplitude Scintillations Associated With Polar Cap Patches: Statistical and Event Analyses

Cardenas‐O’Toole,

Zou,

Ren

et al. 2024

JGR Space Physics

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Ionospheric scintillation causes errors in radio signals. One potential source in the polar region is polar cap patches. Using a polar cap patch database from Ren et al. (2018), https://doi.org/10.1029/2018ja025621 and data provided by a scintillation receiver at Resolute Bay, we evaluated the polar cap patch impact on ionospheric scintillation. In a statistical analysis, we found that 92% of the maximum patch scintillation was under 0.3 for both phase (in the unit of radian) and amplitude scintillations. Of the remaining 8%, less than 1% could be classified as severe phase scintillation (>0.5 radians), while none could be classified as severe amplitude scintillation. Magnetic Local Time (MLT) dependence of the patch phase scintillation shows higher scintillation values near noon MLTs. Patches are classified into cold, hot‐Te and hot‐Te‐Ti types based on their plasma temperatures. The dependence of scintillation on the patch density prominence showed a positive correlation between phase scintillation and cold patches near noon MLT and hot‐Te patches everywhere except midnight MLT. In the event analysis, two events were studied. The first event with large phase scintillation was located in the polar cap with weak polar rain precipitation and large, uniform, antisunward convection flows. The second event with large amplitude scintillation was located in a region with active soft particle precipitation and velocity shear, classical ionospheric signatures of plasma sheet boundary layer. The statistical and event analysis improve understanding of the location and characteristics of patch‐related scintillations.

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Matrix completion methods for the total electron content video reconstruction

Cited by 9 publications

References 34 publications

Complete Global Total Electron Content Map Dataset based on a Video Imputation Algorithm VISTA

Complete Global Total Electron Content Map Dataset based on a Video Imputation Algorithm VISTA

Reconstruction of the Regional Total Electron Content Maps Over the Korean Peninsula Using Deep Convolutional Generative Adversarial Network and Poisson Blending

Phase and Amplitude Scintillations Associated With Polar Cap Patches: Statistical and Event Analyses

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