Regional GPS TEC modeling; Attempted spatial and temporal extrapolation of TEC using neural networks

Details Regional GPS TEC modeling; Attempted spatial and temporal extrapolation of TEC using neural networks Regional GPS TEC modeling; Attempted spatial and temporal extrapolation of TEC using neural networks

Apr 2011

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011jgra..11604314h&link_type=abstract

Journal of Geophysical Research, Volume 116, Issue A4, CiteID A04314

Physics

1

Ionosphere: Modeling And Forecasting

Scientific paper

In this paper, the potential extrapolation capabilities and limitations of artificial neural networks (ANNs) are investigated. This is primarily done by generating total electron content (TEC) predictions using the regional southern Africa total electron content prediction (SATECP) model based on the Global Positioning System (GPS) data and ANNs with the aid of multiple inputs intended to enable the software to learn and correlate the relationship between their variations and the target parameter, TEC. TEC values are predicted over regions that were not covered in the model's development, although it is difficult to validate their accuracy in some cases. The SATECP model is also used to forecast hourly TEC variability 1 year ahead in order to assess the forecasting capability of ANNs in generalizing TEC patterns. The developed SATECP model has also been independently validated by ionosonde data and TEC values derived from the adapted University of New Brunswick Ionospheric Mapping Technique (UNB-IMT) over southern Africa. From the comparison of prediction results with actual GPS data, it is observed that ANNs extrapolate relatively well during quiet periods while the accuracy is low during geomagnetically disturbed conditions. However, ANNs correctly identify both positive and negative storm effects observed in GPS TEC data analyzed within the input space.

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