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A Neural Network-Based TEC Model Capable of Reproducing Nighttime Winter Anomaly

Adolfs, Marjolijn und Hoque, Mohammed Mainul (2021) A Neural Network-Based TEC Model Capable of Reproducing Nighttime Winter Anomaly. Remote Sensing, 13 (22), e4559. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/rs13224559. ISSN 2072-4292.

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Offizielle URL: https://www.mdpi.com/2072-4292/13/22/4559

Kurzfassung

With the availability of fast computing machines, as well as the advancement of machine learning techniques and Big Data algorithms, the development of a more sophisticated total electron content (TEC) model featuring the Nighttime Winter Anomaly (NWA) and other effects is possible and is presented here. The NWA is visible in the Northern Hemisphere for the American sector and in the Southern Hemisphere for the Asian longitude sector under solar minimum conditions. During the NWA, the mean ionization level is found to be higher in the winter nights compared to the summer nights. The approach proposed here is a fully connected neural network (NN) model trained with Global Ionosphere Maps (GIMs) data from the last two solar cycles. The day of year, universal time, geographic longitude, geomagnetic latitude, solar zenith angle, and solar activity proxy, F10.7, were used as the input parameters for the model. The model was tested with independent TEC datasets from the years 2015 and 2020, representing high solar activity (HSA) and low solar activity (LSA) conditions. Our investigation shows that the root mean squared (RMS) deviations are in the order of 6 and 2.5 TEC units during HSA and LSA period, respectively. Additionally, NN model results were compared with another model, the Neustrelitz TEC Model (NTCM). We found that the neural network model outperformed the NTCM by approximately 1 TEC unit. More importantly, the NN model can reproduce the evolution of the NWA effect during low solar activity, whereas the NTCM model cannot reproduce such effect in the TEC variation.

elib-URL des Eintrags:https://elib.dlr.de/145701/
Dokumentart:Zeitschriftenbeitrag
Titel:A Neural Network-Based TEC Model Capable of Reproducing Nighttime Winter Anomaly
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Adolfs, MarjolijnMarjolijn.Adolfs (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Hoque, Mohammed MainulMainul.Hoque (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:November 2021
Erschienen in:Remote Sensing
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Ja
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:13
DOI:10.3390/rs13224559
Seitenbereich:e4559
Verlag:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2072-4292
Status:veröffentlicht
Stichwörter:ionosphere; total electron content; nighttime winter anomaly; neural network; NTCM
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Kommunikation, Navigation, Quantentechnologien
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R KNQ - Kommunikation, Navigation, Quantentechnologie
DLR - Teilgebiet (Projekt, Vorhaben):R - Ionosphäre
Standort: Neustrelitz
Institute & Einrichtungen:Institut für Solar-Terrestrische Physik > Weltraumwetterbeobachtung
Hinterlegt von: Adolfs, Marjolijn
Hinterlegt am:02 Dez 2021 07:18
Letzte Änderung:06 Dez 2021 14:45

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