Alex magnetometer and telluric station in Egypt: First results on pulsation analysis

Omondi, Stephen; Yoshikawa, Akimasa; Zahra, Waheed K.; Fathy, Ibrahim; Mahrous, Ayman

doi:10.1016/j.asr.2022.09.014

Cited by 2 publications

(1 citation statement)

References 48 publications

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“…We utilized wavelet-based coherence analysis to study the timefrequency properties of the observed and forecast GICs. The approach employed follows the procedure described by (Omondi et al, 2022b(Omondi et al, , 2022a.…”

Section: Network Accuracy Parametersmentioning

confidence: 99%

Forecasting of Geomagnetically Induced Currents using Non- Linear Autoregression with Exogenous Inputs

Pappoe,

Tete,

Mahrous

2023

Preprint

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Geomagnetically induced currents (GICs) are low-frequency quasi direct currents caused by the complex interplay between Earth geomagnetic field and continuous high speed solar wind stream (HSS). The result of this interaction poses significant threats to the operations of technological infrastructures such as power grids. The integrity of oil pipelines may also be affected by the influx of GICs. As such, developing models for accurate and timely forecast of GICs is essential for mitigating the potential hazards and safeguarding valuable systems from the extreme effects of these currents. In this work, we propose a machine learning model based on the Nonlinear Autoregression with Exogenous inputs (NARX) for forecasting GICs. The model developed here forecasts GICs using inputs solely based on the solar and interplanetary parameters. The solar and interplanetary parameters retrieved from Omni Web during the maximum and minimum phases of solar cycle 23 were utilized. The developed model takes the solar wind speed, Bz, IMF, AE, ASYH, and SYMH as inputs and the measured GICs obtained from the Finnish Meteorological Institute (Mäntsälä) as the target. We validated the model using measured GICs during the geomagnetic storm periods of November 20 -23 2003, November 7 -13 2004 and August 24 -26, 2005. The model's performance was evaluated using the cross-correlation coefficient (R), root-mean square error (RMSE)and the wavelet coherence analysis. The prediction accuracy for each of the individual storms are 69 %, 68 %, and 70 %, respectively. For these events, RMSEs of 1.17 A, 1.58 A and 0.56 A respectively were obtained in each case indicating the robustness of the model. The approach presented augments existing works and will contribute to the forecasting of GICs in areas where the geoelectric field and local geophysical parameters are not readily available.

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Section: Network Accuracy Parametersmentioning

confidence: 99%

Forecasting of Geomagnetically Induced Currents using Non- Linear Autoregression with Exogenous Inputs

Pappoe,

Tete,

Mahrous

2023

Preprint

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The Status of Space Weather Infrastructure and Research in Africa

Baki,

Rabiu,

Amory-Mazaudier

et al. 2023

Atmosphere

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Space weather science has been a growing field in Africa since 2007. This growth in infrastructure and human capital development has been accompanied by the deployment of ground-based observing infrastructure, most of which was donated by foreign institutions or installed and operated by foreign establishments. However, some of this equipment is no longer operational due to several factors, which are examined in this paper. It was observed that there are considerable gaps in ground-based space-weather-observing infrastructure in many African countries, a situation that hampers the data acquisition necessary for space weather research, hence limiting possible development of space weather products and services that could help address socio-economic challenges. This paper presents the current status of space weather science in Africa from the point of view of some key leaders in this field, focusing on infrastructure, situation, human capital development, and the research landscape.

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Alex magnetometer and telluric station in Egypt: First results on pulsation analysis

Cited by 2 publications

References 48 publications

Forecasting of Geomagnetically Induced Currents using Non- Linear Autoregression with Exogenous Inputs

Forecasting of Geomagnetically Induced Currents using Non- Linear Autoregression with Exogenous Inputs

The Status of Space Weather Infrastructure and Research in Africa

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