Highlights about the performances of storm-time TEC modelling techniques for low/equatorial and mid-latitude locations
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378289%3A_____%2F19%3A00505529" target="_blank" >RIV/68378289:_____/19:00505529 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S027311771930047X" target="_blank" >https://www.sciencedirect.com/science/article/pii/S027311771930047X</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.asr.2019.01.027" target="_blank" >10.1016/j.asr.2019.01.027</a>
Alternative languages
Result language
angličtina
Original language name
Highlights about the performances of storm-time TEC modelling techniques for low/equatorial and mid-latitude locations
Original language description
A statistical evaluation of storm-time total electron content (TEC) modelling techniques over various latitudes of the African sector and surrounding areas is presented. The source of observational TEC data used in this study is the Global Navigation Satellite Systems (GNSS), specifically the Global Positioning Systems (GPS) receiver networks. For each selected receiver station, three different storm time models based on empirical orthogonal functions (EOF) analysis, non-linear regression analysis (NLRA) and Artificial neural networks (ANN), were implemented. Storm-time GPS TEC data used for both development and validation of the models was selected based on the storm criterion of Dst <= -50 nT or K-p >= 4 to take into account both coronal mass ejections (CMEs) and co-rotating interaction regions (CIRs) driven storms, respectively. To make an independent test of the models, storm periods considered for validation were excluded from datasets used during the implementation of the models and results are compared with observations, monthly median values, and International Reference Ionosphere (IRI-2016) predictions. Considering GPS TEC as reference, a statistical analysis performed over six storm periods reserved for validation revealed that ANN model is about 10%, 26%, and 58% more accurate than EOF, NLRA, and IRI models, respectively. It was further found that, EOF model performs 15%, and 44% better than NLRA, and IRI models, respectively, while NLRA is 25% better than IRI. On the other hand, results are also discussed referring to the background ionosphere represented by monthly median TEC (MM TEC) and statistics are provided. Moreover, strengths and weaknesses of each model are highlighted. (C) 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10509 - Meteorology and atmospheric sciences
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Advances in Space Research
ISSN
0273-1177
e-ISSN
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Volume of the periodical
63
Issue of the periodical within the volume
10
Country of publishing house
GB - UNITED KINGDOM
Number of pages
17
Pages from-to
3102-3118
UT code for WoS article
000467513600003
EID of the result in the Scopus database
2-s2.0-85061647802