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

Adolfs, Marjolijn and 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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Official URL: https://www.mdpi.com/2072-4292/13/22/4559

Abstract

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.

Item URL in elib:https://elib.dlr.de/145701/
Document Type:Article
Title:A Neural Network-Based TEC Model Capable of Reproducing Nighttime Winter Anomaly
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Adolfs, MarjolijnMarjolijn.Adolfs (at) dlr.deUNSPECIFIED
Hoque, Mohammed MainulMainul.Hoque (at) dlr.deUNSPECIFIED
Date:November 2021
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:13
DOI:10.3390/rs13224559
Page Range:e4559
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2072-4292
Status:Published
Keywords:ionosphere; total electron content; nighttime winter anomaly; neural network; NTCM
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Communication, Navigation, Quantum Technology
DLR - Research area:Raumfahrt
DLR - Program:R KNQ - Communication, Navigation, Quantum Technology
DLR - Research theme (Project):R - Ionosphere
Location: Neustrelitz
Institutes and Institutions:Institute for Solar-Terrestrial Physics > Space Weather Observation
Deposited By: Adolfs, Marjolijn
Deposited On:02 Dec 2021 07:18
Last Modified:06 Dec 2021 14:45

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