Chaos and Predictability in Ionospheric Time Series.

Area of Science:

• Geophysics
• Space Physics
• Dynamical Systems Theory

Background:

• The Earth's ionosphere is a complex system challenging to model, with current models primarily based on physics and chemistry influenced by space weather.
• The predictability of the ionosphere's residual or mismodelled behavior, whether it's a simple dynamical system or chaotic, remains unclear.

Purpose of the Study:

• To apply data analysis techniques to assess the chaotic nature and predictability of the local ionosphere.
• To quantify the ionosphere's behavior using correlation dimension (D2) and Kolmogorov entropy rate (K2).

Main Methods:

• Calculation of correlation dimension (D2) as a proxy for dynamical complexity.
• Calculation of Kolmogorov entropy rate (K2) to determine the predictability horizon (K2-1).
• Analysis of two one-year vertical total electron content (vTEC) time series from Matera, Italy (Solar Maximum 2001, Solar Minimum 2008).

Main Results:

• Preliminary results demonstrate the feasibility of applying D2 and K2 analyses to ionospheric variability.
• The calculated D2 and K2 values provide a quantitative measure of the ionosphere's chaos and predictability.

Conclusions:

• The study provides a method to measure the chaos and predictability of the Earth's ionosphere.
• These findings are expected to inform and potentially limit claims regarding the predictive capacity of ionospheric models.