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

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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Marjolijn Adolfs, Mohammed Mainul Hoque
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	ionosphere total electron content nighttime winter anomaly neural network NTCM Science Q
Acceso en línea:	https://doaj.org/article/5cd74ab2b27346bc87f6cab41dbf5435
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:5cd74ab2b27346bc87f6cab41dbf5435
record_format	dspace
spelling	oai:doaj.org-article:5cd74ab2b27346bc87f6cab41dbf54352021-11-25T18:54:18ZA Neural Network-Based TEC Model Capable of Reproducing Nighttime Winter Anomaly10.3390/rs132245592072-4292https://doaj.org/article/5cd74ab2b27346bc87f6cab41dbf54352021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4559https://doaj.org/toc/2072-4292With 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.Marjolijn AdolfsMohammed Mainul HoqueMDPI AGarticleionospheretotal electron contentnighttime winter anomalyneural networkNTCMScienceQENRemote Sensing, Vol 13, Iss 4559, p 4559 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ionosphere total electron content nighttime winter anomaly neural network NTCM Science Q
spellingShingle	ionosphere total electron content nighttime winter anomaly neural network NTCM Science Q Marjolijn Adolfs Mohammed Mainul Hoque A Neural Network-Based TEC Model Capable of Reproducing Nighttime Winter Anomaly
description	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.
format	article
author	Marjolijn Adolfs Mohammed Mainul Hoque
author_facet	Marjolijn Adolfs Mohammed Mainul Hoque
author_sort	Marjolijn Adolfs
title	A Neural Network-Based TEC Model Capable of Reproducing Nighttime Winter Anomaly
title_short	A Neural Network-Based TEC Model Capable of Reproducing Nighttime Winter Anomaly
title_full	A Neural Network-Based TEC Model Capable of Reproducing Nighttime Winter Anomaly
title_fullStr	A Neural Network-Based TEC Model Capable of Reproducing Nighttime Winter Anomaly
title_full_unstemmed	A Neural Network-Based TEC Model Capable of Reproducing Nighttime Winter Anomaly
title_sort	neural network-based tec model capable of reproducing nighttime winter anomaly
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/5cd74ab2b27346bc87f6cab41dbf5435
work_keys_str_mv	AT marjolijnadolfs aneuralnetworkbasedtecmodelcapableofreproducingnighttimewinteranomaly AT mohammedmainulhoque aneuralnetworkbasedtecmodelcapableofreproducingnighttimewinteranomaly AT marjolijnadolfs neuralnetworkbasedtecmodelcapableofreproducingnighttimewinteranomaly AT mohammedmainulhoque neuralnetworkbasedtecmodelcapableofreproducingnighttimewinteranomaly
_version_	1718410589812817920

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

Ejemplares similares