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AN EXAMPLE OF A DATA PROCESSING PROCEDURE FOR DETECTION AND VISUALIZATION OF A SUDDEN D-LAYER IONOSPHERE DISTURBANCE

Jovan Bajčetić

Abstract

The sudden and intensive variations of the ionosphere could be represented through its basic property ? electron density which is the main parameter concerning radio propagation through this part of the atmosphere. It is radio sounding as one of the main measurement methods applied among numerous methods for electron density estimation suitable for different ionosphere sub-regions monitoring as a part of the remote sensing Earth observation technics. Collecting VLF/LF (3-300 kHz) radio sounding data which reflect electron density changes of the low latitude ionosphere (60-90 km) and correlating them with other sources of space weather disturbances, the data analysis could give a whole new aspect of natural phenomena examination. The acquired measured data from various types of measurement sensors (e.g. X-ray sensors and radio sounding receivers) represent a valuable source of information which can additionally be used as the part of the vast amount of data for creating database structures, above which big data analysis could be performed. The result of specific data processing involving statistical analysis tools is of multiple benefits ? after basic statistical tests performed above raw data of one kind, the correlation analysis could tell the relation of two different events along a period of time, and finally, a research group could be provided by sufficiently enough starting presumptions for event prediction and machine learning. Even though it would be magnificent if all data combined are analyzed, it is a huge task which is still a challenge to perform in near real time with adequate accuracy. Therefore, subsequent analyses can provide enough suppositions for so called ?triggers? for the deep data analysis. This paper overviews the experimental setup and data processing procedure model which could as a final product give valuable information about the low ionosphere reactions to various kinds of natural and manmade disturbances. It is shown how the model performs raw data acquisition, basic data processing, statistics processing, visualization and GUI building mostly done by Python. Data set under which data processing is done particularly is chosen to reflect the ionosphere response to the strong X-flare occurred during September, 2017. Since the detections for such events are easily performed by the X-ray sensors that GOES-15 satellite is equipped with, the presented model testing is convenient to accomplish.

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DOI resolver: doi.org/10.5593/sgem2018/2.2/s08.004

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