技术
全球定位系统
环境科学
气象学
地球磁场
纬度
电离层
地磁风暴
人工神经网络
卫星
计算机科学
地理
大地测量学
地质学
电信
人工智能
工程类
航空航天工程
物理
磁场
量子力学
地球物理学
作者
Ambelu Tebabal,S.M. Radicella,Melessew Nigussie,B. Damtie,B. Nava,E. Yizengaw
标识
DOI:10.1016/j.jastp.2018.03.004
摘要
Modelling the Earth's ionospheric characteristics is the focal task for the ionospheric community to mitigate its effect on the radio communication, and satellite navigation. However, several aspects of modelling are still challenging, for example, the storm time characteristics. This paper presents modelling efforts of TEC taking into account solar and geomagnetic activity, time of the day and day of the year using neural networks (NNs) modelling technique. The NNs have been designed with GPS-TEC measured data from low and mid-latitude GPS stations. The training was conducted using the data obtained for the period from 2011 to 2014. The model prediction accuracy was evaluated using data of year 2015. The model results show that diurnal and seasonal trend of the GPS-TEC is well reproduced by the model for the two stations. The seasonal characteristics of GPS-TEC is compared with NN and NeQuick 2 models prediction when the latter one is driven by the monthly average value of solar flux. It is found that NN model performs better than the corresponding NeQuick 2 model for low latitude region. For the mid-latitude both NN and NeQuick 2 models reproduce the average characteristics of TEC variability quite successfully. An attempt of one day ahead forecast of TEC at the two locations has been made by introducing as drivers previous day solar flux and geomagnetic index values. The results show that a reasonable day ahead forecast of local TEC can be achieved.
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