Modeling ionograms with deep neural networks: applications to foF2 forecasting

Descripción del Articulo

Poster presented at the 2021 CEDAR Virtual Workshop, June 20-25.
Detalles Bibliográficos
Autores: Aricoché, J., Rojas, E., Milla, Marco
Formato: objeto de conferencia
Fecha de Publicación:2021
Institución:Instituto Geofísico del Perú
Repositorio:IGP-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.igp.gob.pe:20.500.12816/4965
Enlace del recurso:http://hdl.handle.net/20.500.12816/4965
Nivel de acceso:acceso abierto
Materia:Ionograms
Neural networks
foF2
https://purl.org/pe-repo/ocde/ford#1.05.01
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dc.title.es_ES.fl_str_mv Modeling ionograms with deep neural networks: applications to foF2 forecasting
title Modeling ionograms with deep neural networks: applications to foF2 forecasting
spellingShingle Modeling ionograms with deep neural networks: applications to foF2 forecasting
Aricoché, J.
Ionograms
Neural networks
foF2
https://purl.org/pe-repo/ocde/ford#1.05.01
title_short Modeling ionograms with deep neural networks: applications to foF2 forecasting
title_full Modeling ionograms with deep neural networks: applications to foF2 forecasting
title_fullStr Modeling ionograms with deep neural networks: applications to foF2 forecasting
title_full_unstemmed Modeling ionograms with deep neural networks: applications to foF2 forecasting
title_sort Modeling ionograms with deep neural networks: applications to foF2 forecasting
author Aricoché, J.
author_facet Aricoché, J.
Rojas, E.
Milla, Marco
author_role author
author2 Rojas, E.
Milla, Marco
author2_role author
author
dc.contributor.author.fl_str_mv Aricoché, J.
Rojas, E.
Milla, Marco
dc.subject.es_ES.fl_str_mv Ionograms
Neural networks
foF2
topic Ionograms
Neural networks
foF2
https://purl.org/pe-repo/ocde/ford#1.05.01
dc.subject.ocde.es_ES.fl_str_mv https://purl.org/pe-repo/ocde/ford#1.05.01
description Poster presented at the 2021 CEDAR Virtual Workshop, June 20-25.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-07-09T14:29:01Z
dc.date.available.none.fl_str_mv 2021-07-09T14:29:01Z
dc.date.issued.fl_str_mv 2021-06
dc.type.es_ES.fl_str_mv info:eu-repo/semantics/conferenceObject
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dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/20.500.12816/4965
url http://hdl.handle.net/20.500.12816/4965
dc.language.iso.es_ES.fl_str_mv eng
language eng
dc.rights.es_ES.fl_str_mv info:eu-repo/semantics/openAccess
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eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.format.es_ES.fl_str_mv application/pdf
dc.publisher.es_ES.fl_str_mv Instituto Geofísico del Perú
dc.source.none.fl_str_mv reponame:IGP-Institucional
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instname_str Instituto Geofísico del Perú
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spelling Aricoché, J.Rojas, E.Milla, Marco2021-07-09T14:29:01Z2021-07-09T14:29:01Z2021-06http://hdl.handle.net/20.500.12816/4965Poster presented at the 2021 CEDAR Virtual Workshop, June 20-25.The ionosphere state parameters are of fundamental importance not only for radio communication but also for space weather. As most of the space phenomena, the dynamics are governed by nonlinear processes that make forecasts a challenging endeavor. We now have available enormous datasets and ubiquitous experimental sources that can help us finding the intricate regularities in these phenomena. In this work, we will focus on the forecasting of some parameters of the steady-state low latitude ionosphere. We used ionograms from Jicamarca Radio Observatory digisonde to train two neural networks. We produced forecasts of ionospheric parameters such as virtual heights and foF2 taking into consideration ionogram characteristics. These estimations were compared to the corresponding values obtained from the digisonde, the persistence model, and foF2 values obtained from the International reference ionosphere.application/pdfengInstituto Geofísico del Perúinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/4.0/IonogramsNeural networksfoF2https://purl.org/pe-repo/ocde/ford#1.05.01Modeling ionograms with deep neural networks: applications to foF2 forecastinginfo:eu-repo/semantics/conferenceObjectreponame:IGP-Institucionalinstname:Instituto Geofísico del Perúinstacron:IGPORIGINALPoster_Aricoche_et_al_2021.pdfPoster_Aricoche_et_al_2021.pdfapplication/pdf1305967https://repositorio.igp.gob.pe/bitstreams/7713d4d9-b041-4fc7-b4fe-2e26d2252d9c/download77b591862a0b83505dd2f57bef38739aMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.igp.gob.pe/bitstreams/c0461d3f-a118-4347-a91a-6061edc5b72c/download8a4605be74aa9ea9d79846c1fba20a33MD52TEXTPoster_Aricoche_et_al_2021.pdf.txtPoster_Aricoche_et_al_2021.pdf.txtExtracted texttext/plain6532https://repositorio.igp.gob.pe/bitstreams/e85de40c-8838-4cc1-9e4f-72ebe30f8616/download7b225665ad39cc7686635fdb9b6458ecMD53THUMBNAILPoster_Aricoche_et_al_2021.pdf.jpgPoster_Aricoche_et_al_2021.pdf.jpgIM Thumbnailimage/jpeg150373https://repositorio.igp.gob.pe/bitstreams/b2533055-894e-470c-81f9-9ef9ea7aeb36/download66d9bbe1ce474658f7306fd36f131095MD5420.500.12816/4965oai:repositorio.igp.gob.pe:20.500.12816/49652021-07-09 14:42:11.119https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessopen.accesshttps://repositorio.igp.gob.peRepositorio Geofísico del Perudspace-help@myu.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Modeling ionograms with deep neural networks: applications to foF2 forecasting
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