Developing a Toolkit for Efficient Analysis of Cadi Ionosonde Data

The ionosphere, a region of Earth's upper atmosphere ionized by solar radiation, plays a vital role in applications like radio communication, navigation, and satellites. The Canadian Advanced Digital Ionosonde (CADI), developed in the 1990s, faces limitations due to dated computer architecture, hindering data analysis potential. To gain insights into the ionosphere, efficient automated data analysis methods are required. CADI offers limited ionospheric data through ionograms. Access to comprehensive ionosonde raw data is crucial to monitor and model the ionosphere effectively, including transmitter and receiver configurations, pulse details, post-processing methods, receiver signal components, raw IQ (In-phase and Quadrature) data, and more. This paper introduces the Ionosonde Analysis Toolkit (IAT), with four Python programs simplifying interaction with non-digisonde systems like CADI. IAT converts MD2 or MD4 ionogram files to text-based files, extracts key features for analysis, and visualizes data. This facilitates novel analysis methods, such as machine learning-based autoscaling of ionograms, ultimately improving radio communication and navigation systems reliant on the ionosphere. In conclusion, IAT significantly enhances researchers' ability to work with ionosonde data.