Hierarchical Bayesian modeling of ionospheric TEC disturbances as non-stationary processes

Seid, Abdu Mohammed; Berhane, Tesfahun; Roininen, Lassi; Nigussie, Melessew

doi:10.1016/j.asr.2017.12.009

Cited by 2 publications

(3 citation statements)

References 18 publications

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“…It is concluded that Gaussian distribution does not represent within‐the‐hour variability. In Seid et al (2018), TEC is modeled as a sum of regular stationary and irregular nonstationary processes. Hierarchical Bayesian inversion with Gaussian Markov random process priors are used to decompose measurement TEC into trend‐like and irregular variation components.…”

Section: Introductionmentioning

confidence: 99%

“…The random field theory utilizes stochastic processes which can also be characterized through their spatial correlations. With its inherent multiscale random space‐time processes, ionosphere can be defined by the sum of two random fields: primary (trend) and secondary (dynamic) stochastic functions such as those given in Sayin et al (2008) and Seid et al (2018). Probability density functions (PDFs) are descriptors of random variables which can be extended into stochastic processes and further, into random fields (Papoulis & Pillai, 2002; Vanmarcke, 2010).…”

Section: Introductionmentioning

confidence: 99%

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A Methodology for Estimation of Hourly‐Monthly Stochastic Trend Characteristics of Midlatitude Ionosphere

Arıkan

Köroğlu

2020

Radio Science

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Spatiotemporal variability of ionosphere poses a major challenge in characterization of observable parameters, critical frequency of F2 layer (foF2), maximum ionization height (hmF2), total electron content (TEC), and slab thickness. The nature of the multiscale space-time variability of ionosphere can be modeled as a random field. In this study, as a first step in random field modeling of the trend, hourly-monthly parametric probability density functions (PPDF) of foF2, hmF2, slab thickness, and TEC are obtained using IONOLAB-PDF method for three midlatitude stations (namely, Juliusruh, Pruhonice, and Rome) for low, moderate, and high solar activity years of 2009, 2012, and 2014, respectively. foF2 and hmF2 are obtained from ionosondes. TEC is estimated from dual-frequency Global Positioning System (GPS) receivers. It is observed that hourly-monthly PPDFs of all ionospheric parameters at the three midlatitude stations can best be represented by lognormal and Weibull distributions. The estimates of mean and standard deviations of hourly-monthly PPDFs indicate that the spatiotemporal stochastic trend variation of the midlatitude ionosphere differs in terms of foF2 and GPS-TEC. The hourly-monthly PPDF of foF2 represents all major anomalies as well as seasonal and hourly trends, whereas those of GPS-TEC is highly dependent on solar activity level and latitude. The general statistical trend of hmF2 is anticorrelated with foF2 as expected. The mean of hourly-monthly PPDF of slab thickness is around 400 km in 2012 and 2014 for all stations between 0200 UT and 1600 UT.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Methodology for Estimation of Hourly‐Monthly Stochastic Trend Characteristics of Midlatitude Ionosphere

Arıkan

Köroğlu

2020

Radio Science

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“…However, autocorrelated measurements are not necessarily from a stationary process. Recently, different models for various nonstationary processes have been proposed in many areas of measurement science (see [3][4][5][6]). For measurements from a nonstationary process, how to evaluate the corresponding uncertainties is a critical task.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of measurement uncertainty from a nonstationary process

Zhang

2019

Meas. Sci. Technol.

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In metrology, when repeated measurements are autocorrelated, it is not appropriate to use the traditional approach to evaluate the uncertainty of the average of repeated measurements. In literature, methodologies were developed to evaluate the uncertainty of measurements from a stationary process, which demonstrated ‘statistical equilibrium’. In this paper, we discuss approaches to evaluate the measurement uncertainty when the data are from a nonstationary process. In particular, for some nonstationary processes with a time-dependent mean and a constant variance, we may be able to assess the uncertainty based on one realization. Specifically, after the effect of the time-dependent mean is removed, the residuals may show equilibrium behavior and thus can be treated as being from a stationary process. We propose approaches to evaluate the uncertainty of measurements based on one realization of such a process with practical examples presented for illustration.

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Hierarchical Bayesian modeling of ionospheric TEC disturbances as non-stationary processes

Cited by 2 publications

References 18 publications

A Methodology for Estimation of Hourly‐Monthly Stochastic Trend Characteristics of Midlatitude Ionosphere

A Methodology for Estimation of Hourly‐Monthly Stochastic Trend Characteristics of Midlatitude Ionosphere

Evaluation of measurement uncertainty from a nonstationary process

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