A Bayesian Inference‐Based Empirical Model for Scintillation Indices for High‐Latitude

• Published in: Space Weather Vol. 19; no. 6
• Main Authors: Meziane, K., Kashcheyev, A., Jayachandran, P. T., Hamza, A. M.
• Format: Journal Article
• Language: English
• Published: Washington John Wiley & Sons, Inc 01.06.2021; Wiley
• Subjects: Amplitudes; Auroral zone; Auroral zones; Bayesian analysis; Bayesian inference; Declination; Electric currents; Electrojets; Electron density; Empirical models; Fluctuations; Global positioning systems; GPS; Interplanetary magnetic field; Ionosphere; Ionospheric propagation; ionospheric scintillation; Latitude; Magnetic fields; Magnetospheres; Navigation satellites; Navigation systems; Polar caps; Polar cusps; Radio signals; Ram pressure; Refractive index; Refractivity; Scintillation; Solar activity; Solar wind; solar wind disturbances; Solar wind parameters; Solar wind velocity; Statistical inference; Wind speed
• ISSN: 1542-7390; 1539-4964; 1542-7390
• DOI: 10.1029/2020SW002710

Solar wind parameters, the solar radio flux index (F10.7), the Sun's declination and the SuperMAG Electrojet index are used to construct a Bayesian inference‐based empirical model for scintillation indices (S4 and σΦ) at high latitudes. For the present study, measurements from three Global Positioning System (GPS) L1 receivers located in the auroral zone, the cusp and in the polar cap are selected, respectively. The solar wind characteristics include the solar wind speed (VSW) and ram pressure (ρSW) as well as the Geocentric Solar Magnetospheric (GSM) By and the Bz components of the interplanetary magnetic field (IMF). Following a brief assessment on the independence of the variables (predictors), prior probabilities of occurrence in the case of a multinomial classification are constructed. Posterior‐probabilities are then deduced for any arbitrary set of predictors. We show that the model captures most variations seen in the measured indices whether they are associated or not with transient interplanetary events. Although the model tends to underestimate the actual phase index measurements, 95% of the validated events are predicted with an error less than 0.034 rad in σΦ. For the amplitude scintillation index, 5% of validated events have an error larger than 0.019. Plain Language Summary Ionospheric scintillation is the result of rapid fluctuations in amplitude and phase of radio signals propagating through the ionosphere. These signals are emitted by the global navigation satellite systems, which provide, among other things, a geolocation to a Ground Positioning System receiver operating at L‐band frequencies (1,176.45–1,575.42 MHz). Scintillation is due to small‐scale inhomogeneities in the refractive index caused by electron density fluctuations in the ionosphere. Ionospheric scintillation is a post‐sunset phenomenon, but it can also occur following the propagation of disturbances in the solar wind medium. In the present study, physical quantities describing the solar wind, the solar activity, and the electric currents present in the ionosphere are used to construct an empirical model to predict the variations in the amplitude and phase scintillation. The Bayesian formula provides the basic framework of the model. We show that the model satisfactorily captures most variations seen in the measured scintillation indices. Key Points The Bayesian inference method is used for forecast GPS L1 scintillation indices S4 and σΦ Using solar wind characteristics in addition to the SuperMAG auroral electrojet index, the model satisfactorily captures all the essential dynamics of the scintillation indices The method can be used to forecast the ionospheric scintillation activity subject to the availability of the interplanetary driver's data