Reconstructing Regional Ionospheric Electron Density: A Combined Spherical Slepian Function and Empirical Orthogonal Function Approach

Farzaneh, Saeed; Forootan, Ehsan

doi:10.1007/s10712-017-9446-y

Cited by 28 publications

(24 citation statements)

References 45 publications

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“…The FORMOSAT-3/COSMIC constellation contains six satellites with 30° separations and keeps an orbital period of 100 min. The orbit maintains an altitude of 800 km for the period of this study (Farzaneh and Forootan 2018). The second-level data 'ionprf files' describe the vertical profiles of IED.…”

Section: Vertical Profile Validationmentioning

confidence: 99%

Virtual reference station-based computerized ionospheric tomography

Wan

et al. 2020

GPS Solut

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In computerized ionospheric tomography (CIT) with ground-based GNSS, the voxels without satellite-receiver ray traversing cannot be reconstructed directly. We present a CIT algorithm based on virtual reference stations (VRSs), called VRS–CIT, to decrease the number of unilluminated voxels and improve the precision of the estimated ionospheric electron density (IED). The VRSs are set at the nodes of grids with a 0.5° × 0.5° resolution in longitude and latitude. We generate the virtual observations with the observations from nearby six or three stations selected according to azimuths and distances. The generation utilizes multi-quadric surface fitting with six stations and triangular linear interpolation with three stations. With the virtual observations added, the IED distribution is reconstructed by the multiplicative algebraic reconstruction technique with the initial values obtained from IRI-2016. The performance of VRS–CIT is examined by using the data from 127 GNSS stations located in 24–46° N and 122–146° E to derive the IED every 30 min. The study focuses on April 29, 2014, with the adaptability of VRS–CIT analyzed by 12 days, evenly distributed around equinoxes and solstices of 2014. The accuracy of the virtual observation is about 1 TECU. Comparing to that derived from CIT with only real observations, the unsolvability of VRS–CIT declined by 4–12% for the whole region, and for the main area, the improvement can be up to 70%. Taking two IED profiles from radio occultation as reference measurements, the mean absolute error (MAE) of IED by VRS–CIT decreases by 6.88% and 8.43%, respectively. Comparing with slant total electron content (STEC) extracted from five additional GNSS stations, the MAE and the root mean square error of the estimated STEC can be reduced up to 17.24% and 33.81%, respectively.

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Section: Vertical Profile Validationmentioning

confidence: 99%

Virtual reference station-based computerized ionospheric tomography

Wan

et al. 2020

GPS Solut

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“…The Empirical Orthogonal Function (EOF), also known as feature vector analysis, is a method for analyzing the feature structure of the matrix data and extracting the feature quantities of the data. It is a commonly used method in oceanography and meteorology and has advantages in mining spatial commonality [30].…”

Section: Empirical Orthogonal Functionmentioning

confidence: 99%

A Regional Photovoltaic Output Prediction Method Based on Hierarchical Clustering and the mRMR Criterion

Yang

Yao

et al. 2019

Energies

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Photovoltaic (PV) power generation is greatly affected by meteorological environmental factors, with obvious fluctuations and intermittencies. The large-scale PV power generation grid connection has an impact on the source-load stability of the large power grid. To scientifically and rationally formulate the power dispatching plan, it is necessary to realize the PV output prediction. The output prediction of single power plants is no longer applicable to large-scale power dispatching. Therefore, the demand for the PV output prediction of multiple power plants in an entire region is becoming increasingly important. In view of the drawbacks of the traditional regional PV output prediction methods, which divide a region into sub-regions based on geographical locations and determine representative power plants according to the correlation coefficient, this paper proposes a multilevel spatial upscaling regional PV output prediction algorithm. Firstly, the sub-region division is realized by an empirical orthogonal function (EOF) decomposition and hierarchical clustering. Secondly, a representative power plant selection model is established based on the minimum redundancy maximum relevance (mRMR) criterion. Finally, the PV output prediction for the entire region is achieved through the output prediction of representative power plants of the sub-regions by utilizing the Elman neural network. The results from a case study show that, compared with traditional methods, the proposed prediction method reduces the normalized mean absolute error (nMAE) by 4.68% and the normalized root mean square error (nRMSE) by 5.65%, thereby effectively improving the prediction accuracy.

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“…The selection of a proper empirical model which allows analyzing local structures and deducing physical interpretations from the model parameters is crucial. Ionosphere models relying on localized basis functions have been increasingly gaining popularity, for instance, applications of B-spline functions [26][27][28][29] and Slepian functions [30]. The B-spline representation of VTEC provides an empirical approximation for evaluating temporal and spatial variations.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Modeling of the Global Ionosphere Vertical Total Electron Content

Erdogan¹,

Schmidt²,

Goss³

et al. 2020

Remote Sensing

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The Kalman filter (KF) is widely applied in (ultra) rapid and (near) real-time ionosphere modeling to meet the demand on ionosphere products required in many applications extending from navigation and positioning to monitoring space weather events and naturals disasters. The requirement of a prior definition of the stochastic models attached to the measurements and the dynamic models of the KF is a drawback associated with its standard implementation since model uncertainties can exhibit temporal variations or the time span of a given test data set would not be large enough. Adaptive methods can mitigate these problems by tuning the stochastic model parameters during the filter run-time. Accordingly, one of the primary objectives of our study is to apply an adaptive KF based on variance component estimation to compute the global Vertical Total Electron Content (VTEC) of the ionosphere by assimilating different ionospheric GNSS measurements. Secondly, the derived VTEC representation is based on a series expansion in terms of compactly supported B-spline functions. We highlight the morphological similarity of the spatial distributions and the magnitudes between VTEC values and the corresponding estimated B-spline coefficients. This similarity allows for deducing physical interpretations from the coefficients. In this context, an empirical adaptive model to account for the dynamic model uncertainties, representing the temporal variations of VTEC errors, is developed in this work according to the structure of B-spline coefficients. For the validation, the differential slant total electron content (dSTEC) analysis and a comparison with Jason-2/3 altimetry data are performed. Assessments show that the quality of the VTEC products derived by the presented algorithm is in good agreement, or even more accurate, with the products provided by IGS ionosphere analysis centers within the selected periods in 2015 and 2017. Furthermore, we show that the presented approach can be applied to different ionosphere conditions ranging from very high to low solar activity without concerning time-variable model uncertainties, including measurement error and process noise of the KF because the associated covariance matrices are computed in a self-adaptive manner during run-time.

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Reconstructing Regional Ionospheric Electron Density: A Combined Spherical Slepian Function and Empirical Orthogonal Function Approach

Cited by 28 publications

References 45 publications

Virtual reference station-based computerized ionospheric tomography

Virtual reference station-based computerized ionospheric tomography

A Regional Photovoltaic Output Prediction Method Based on Hierarchical Clustering and the mRMR Criterion

Adaptive Modeling of the Global Ionosphere Vertical Total Electron Content

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