Upgrading CCIR’s fo F2 maps using available ionosondes and genetic algorithms

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We have developed a new approach towards a new database of the ionospheric parameter f oF 2. This parameter, being the frequency of the maximum of the ionospheric electronic density profile and its main modeller, is of great interest not only in atmospheric studies but also in the realm of radio pro...

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Autores principales: Gularte Scarone, Ángela Erika, Carpintero, Daniel Diego, Jaen, Juliana María
Formato: Articulo
Lenguaje:Inglés
Publicado: 2018
Materias:
Geofísica
fo F2 maps
Genetic algorithm
Ionosphere
F region
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/146954
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-146954
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Geofísica
fo F2 maps
Genetic algorithm
Ionosphere
F region
spellingShingle Geofísica
fo F2 maps
Genetic algorithm
Ionosphere
F region
Gularte Scarone, Ángela Erika
Carpintero, Daniel Diego
Jaen, Juliana María
Upgrading CCIR’s fo F2 maps using available ionosondes and genetic algorithms
topic_facet Geofísica
fo F2 maps
Genetic algorithm
Ionosphere
F region
description We have developed a new approach towards a new database of the ionospheric parameter f oF 2. This parameter, being the frequency of the maximum of the ionospheric electronic density profile and its main modeller, is of great interest not only in atmospheric studies but also in the realm of radio propagation. The current databases, generated by CCIR (Committee Consultative for Ionospheric Radiowave propagation) and URSI (International Union of Radio Science), and used by the IRI (International Reference Ionosphere) model, are based on Fourier expansions and have been built in the 60s from the available ionosondes at that time. The main goal of this work is to upgrade the databases by using new available ionosonde data. To this end we used the IRI diurnal/spherical expansions to represent the f oF 2 variability, and computed its coefficients by means of a genetic algorithm (GA). In order to test the performance of the proposed methodology, we applied it to the South American region with data obtained by RAPEAS (Red Argentina para el Estudio de la Atmo´ sfera Superior, i.e. Argentine Network for the Study of the Upper Atmosphere) during the years 1958–2009. The new GA coefficients provide a global better fit of the IRI model to the observed f oF 2 than the CCIR coefficients. Since the same formulae and the same number of coefficients were used, the overall integrity of IRI’s typical ionospheric feature representation was preserved. The best improvements with respect to CCIR are obtained at low solar activities, at large (in absolute value) modip latitudes, and at nighttime. The new method is flexible in the sense that can be applied either globally or regionally. It is also very easy to recompute the coefficients when new data is available. The computation of a third set of coefficients corresponding to days of medium solar activity in order to avoid the interpolation between low and high activities is suggested. The same procedure as for f oF 2 can be perfomed to obtain the ionospheric parameter M(3000)F2.
format Articulo
Articulo
author Gularte Scarone, Ángela Erika
Carpintero, Daniel Diego
Jaen, Juliana María
author_facet Gularte Scarone, Ángela Erika
Carpintero, Daniel Diego
Jaen, Juliana María
author_sort Gularte Scarone, Ángela Erika
title Upgrading CCIR’s fo F2 maps using available ionosondes and genetic algorithms
title_short Upgrading CCIR’s fo F2 maps using available ionosondes and genetic algorithms
title_full Upgrading CCIR’s fo F2 maps using available ionosondes and genetic algorithms
title_fullStr Upgrading CCIR’s fo F2 maps using available ionosondes and genetic algorithms
title_full_unstemmed Upgrading CCIR’s fo F2 maps using available ionosondes and genetic algorithms
title_sort upgrading ccir’s fo f2 maps using available ionosondes and genetic algorithms
publishDate 2018
url http://sedici.unlp.edu.ar/handle/10915/146954
work_keys_str_mv AT gulartescaroneangelaerika upgradingccirsfof2mapsusingavailableionosondesandgeneticalgorithms
AT carpinterodanieldiego upgradingccirsfof2mapsusingavailableionosondesandgeneticalgorithms
AT jaenjulianamaria upgradingccirsfof2mapsusingavailableionosondesandgeneticalgorithms
bdutipo_str Repositorios
_version_ 1764820460655083521

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