Work

Regional Spatial Mean of Ionospheric Irregularities Based on K-Means Clustering of ROTI Maps

Year: 2024

Type: article

Abstract: In this paper, we investigate and propose the application of an unsupervised machine learning clustering method to characterize the spatial and temporal distribution of ionospheric plasma irregularities over the Western African equatorial region. The ordinary Kriging algorithm was used to interpolate the rate of change of the total electron content (TEC) index (ROTI) over gridded 0.5° by 0.5° latitude and longitude regional maps in order to simulate the level of ionospheric plasma irregularities in a quasi-real-time scenario. K-means was used to obtain a spatial mean index through an optimal stratification of regional post-processed ROTI maps. The results obtained could be adapted by appropriate K-means algorithms to a real-time scenario, as has been performed for other applications. This method could allow us to monitor plasma irregularities in real time over the African region and, therefore, lead to the possibility of mitigating their effects on satellite-based location systems in the said region.

Source: Atmosphere

DOI: 10.3390/atmos15091098

Authors: Yenca Migoya-Orué, Oladipo E. Abe, Sandro Radicella

Institutions: The Abdus Salam International Centre for Theoretical Physics (ICTP), Federal University Oye Ekiti, Boston College

Language: English

Topic: Ionosphere and magnetosphere dynamics

Subfield: Astronomy and Astrophysics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,728

Funders: Australian Government, European Space Agency