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dc.contributor.author Aricoché, J.
dc.contributor.author Rojas, E.
dc.contributor.author Milla, Marco
dc.date.accessioned 2021-07-09T14:29:01Z
dc.date.available 2021-07-09T14:29:01Z
dc.date.issued 2021-06
dc.identifier.uri http://hdl.handle.net/20.500.12816/4965
dc.description Poster presented at the 2021 CEDAR Virtual Workshop, June 20-25.
dc.description.abstract 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. es_ES
dc.format application/pdf es_ES
dc.language.iso eng es_ES
dc.publisher Instituto Geofísico del Perú es_ES
dc.rights info:eu-repo/semantics/openAccess es_ES
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/ es_ES
dc.subject Ionograms es_ES
dc.subject Neural networks es_ES
dc.subject foF2 es_ES
dc.title Modeling ionograms with deep neural networks: applications to foF2 forecasting es_ES
dc.type info:eu-repo/semantics/conferenceObject es_ES
dc.subject.ocde https://purl.org/pe-repo/ocde/ford#1.05.01 es_ES

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