Comments on the percentage of occurrence methodology used in “a study of L band scintillations during the initial phase of rising solar activity at an Indian low latitude station” by H J Tanna, S P Karia and K N Pathak

Paul, Bapan; De, B. K.; Guha, Anirban

doi:10.1016/j.asr.2018.12.003

Cited by 7 publications

(2 citation statements)

References 52 publications

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“…1) which is in operation in the EIA crest region over Agartala, India (23.760°N, 91.260°E). The receiver tracks up to 11 GPS satellites at the L1 (1575.42 MHz) and L2 (1227.6 MHz) frequencies simultaneously (Guha et al 2016;Paul et al 2018Paul et al , 2019aPaul et al , 2020a. The GPS TEC and scintillation Fig.…”

Section: Methodsmentioning

confidence: 99%

Statistics of GPS TEC at the northern EIA crest region of the Indian subcontinent during the solar cycle 24 (2013-2018): comparison with IRI-2016 and IRI-2012 models

Patari

Paul

Guha

2021

Astrophys Space Sci

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In this study the statistics of ionospheric total electron content (TEC), derived from a GSV4004B dualfrequency Global Positioning System (GPS) receiver at Agartala station (23.450°N, 91.150°E) located in northern equatorial ionization anomaly (EIA) crest region of the Indian subcontinent, is reported with a performance analysis of IRI-2016 and IRI-2012 models during the ascending, maxima, declining and minima phases (2013)(2014)(2015)(2016)(2017)(2018) of the solar cycle 24. Variations of model total electron content, as obtained from the IRI-2016 and IRI-2012 for the three options of topside electron density namely NeQuick, IRI 2001 and IRI 01-corr, are compared with the observed total electron content during different periods of interest viz. monthly, seasonal, annual and the correlations with solar activity parameters viz. sunspot number (SSN), 10.7 cm solar radio flux (F10.7), solar EUV flux, are also investigated. All the three options of IRI-2016 and IRI-2012 models show an earlier occurrence of diurnal maximum total electron content, as compared to the observed diurnal maximum GPS total electron content, throughout all the months during the complete period of observation. As the solar activity decreases (from 2015 to 2018), the model starts underestimating GPS total electron content, which becomes significantly high during the very low solar activity period of 2017-18 for all the months. IRI-2016 model underestimates the GPS total electron content before the hours of diurnal maximum and overestimates after the hours of diurnal maximum in the years from 2013-2018. IRI-2012 model underestimates the GPS total electron content before the hours of diurnal maximum and overestimates after the hours of diurnal maximum in the A. Guha

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Section: Methodsmentioning

confidence: 99%

Statistics of GPS TEC at the northern EIA crest region of the Indian subcontinent during the solar cycle 24 (2013-2018): comparison with IRI-2016 and IRI-2012 models

Patari

Paul

Guha

2021

Astrophys Space Sci

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“…15 With the high sunspot number and 10.7 radio flux, high scintillations are expected in the month of May 2023. 16 As solar cycle 25 progresses toward its maximum, high scintillations are expected in the month of May 2023. 17 The classification of phase scintillations is performed by using decision tree for both 1 week phase scintillation dataset and 1 month phase scintillation set.…”

Section: B Phase Scintillation Computationsmentioning

confidence: 99%

Machine learning approach for prediction of total electron content and classification of ionospheric scintillations over Visakhapatnam region

Nimmakayala,

Dutt V.B.S

2023

AIP Advances

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Ionospheric scintillations, which are due to ionospheric plasma density anomalies, negatively impact trans-ionospheric signals and the positioning accuracy of the global navigation satellite system (GNSS). One of the crucial variables for comprehending space weather conditions is the total electron content (TEC) of the ionosphere. It is vital to predict the ionospheric TEC before making efforts to enhance the GNSS system. In this article, the long short-term memory machine learning approach for TEC prediction is presented, based on which the ionospheric phase scintillations are identified and classified using popular classifiers: support vector machines and decision trees. In this article, the comparative analysis of these classifiers is presented using the standard performance metrics: accuracy, recall, precision, and F1 score.

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Study of the low and mid-latitude ionospheric response to the geomagnetic storm of 20th December 2015

Paul¹,

Galav²

2020

Astrophys Space Sci

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Comments on the percentage of occurrence methodology used in “a study of L band scintillations during the initial phase of rising solar activity at an Indian low latitude station” by H J Tanna, S P Karia and K N Pathak

Cited by 7 publications

References 52 publications

Statistics of GPS TEC at the northern EIA crest region of the Indian subcontinent during the solar cycle 24 (2013-2018): comparison with IRI-2016 and IRI-2012 models

Statistics of GPS TEC at the northern EIA crest region of the Indian subcontinent during the solar cycle 24 (2013-2018): comparison with IRI-2016 and IRI-2012 models

Machine learning approach for prediction of total electron content and classification of ionospheric scintillations over Visakhapatnam region

Study of the low and mid-latitude ionospheric response to the geomagnetic storm of 20th December 2015

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