New Model to Predict Ionosphere Electron Density
Researchers from Indian Institute of Geomagnetism (IIG) have developed an Artificial Neural Networks based global model to predict the Ionospheric electron density with wider data coverage.
• Ionosphere: It plays significant role in atmospheric electricity and forms the inner edge of the earth’s magnetosphere. The ionosphere is among the top layer of the earth’s atmosphere that gets ionized by solar radiation. Radiation from the sun ionizes atoms and molecules, liberating electrons from molecules and creating a space of free electron and ions.It has practical importance as it influences radio propagation to distant places on the Earth. It reflects and modifies radio waves used for communication and navigation.
• The free electron density in the ionosphere varies with the activity of the Sun, the Earth magnetic field and atmospheric parameters. Higher electron concentrations and stronger spatial variations occur mainly in Polar Regions, caused by the shape of the Earth magnetic field.
• Artificial Neural Networks (ANN) is tool used in machine learning. ANN intended to replicate the way that human brain learns. Neural networks consist of input and output layers, and they are excellent tools for finding patterns which are far too complex, require enormous amount of computing data or numerous for a human programmer to extract and teach the machine to recognize.
The model developed by IIG may be utilized as a reference model in the ionospheric predictions and has potential applications in calculating the Global Navigation Satellite System (GNSS) positioning errors.
The Artificial Neural Networks based global model can capture the ionosphere during the disturbed space weather periods, such as geomagnetic storms which occurs when the magnetic cloud originating from Sun (known as Coronal Mass Ejection (CME)) interacts with the Earth’s magnetosphere. By analyzing data the space weather event can be predicted.
Significance: Tracking the variability of the Ionosphere is important for communication and navigation. This model by using electron density anomalies can predict the ionospheric electron density and the peak parameters. This can contribute in suppressing communication losses. Geomagnetism can also impact the electricity-based technology on which we rely. Such model can offer way forward for cutting edge technologies.
Limitation: The ionosphere model relies on theoretical and empirical techniques; however, the accurate prediction of electron density is still a challenging task. The ionospheric variability is greatly influenced by both solar originated processes and the neutral atmosphere origin, therefore, difficult to model.
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Geostrophic Storms, Coronal mass Ejection, Aurora, Solar Flame