Analysis of Ion Composition Estimation Accuracy for Incoherent Scatter Radars

The Incoherent Scatter Radar (ISR) is one of the most powerful sounding methods developed to estimate the Ionosphere. This radar system determines the plasma parameters by sending powerful electromagnetic pulses to the Ionosphere and analyzing the received backscatter. This analysis provides information about parameters such as electron and ion temperatures, electron densities, ion composition, and ion drift velocities. Nevertheless in some cases the ISR analysis has ambiguities in the determination of the plasma characteristics. It is of particular relevance the ion composition and temperature ambiguity obtained between the F1 and the lower F2 layers. In this case very similar signals are obtained with different mixtures of molecular ions (NO2+ and O2+) and atomic oxygen ions (O+), and consequently it is not possible to completely discriminate between them. The most common solution to solve this problem is the use of empirical or theoretical models of the ionosphere in the fitting of ambiguous data. More recent works take use of parameters estimated from the Plasma Line band of the radar to reduce the number of parameters to determine. In this work we propose to determine the error estimation of the ion composition ambiguity when using Plasma Line electron density measurements. The sensibility of the ion composition estimation has been also calculated depending on the accuracy of the ionospheric model, showing that the correct estimation is highly dependent on the capacity of the model to approximate the real values. Monte Carlo simulations of data fitting at different signal to noise (SNR) ratios have been done to obtain valid and invalid estimation probability curves. This analysis provides a method to determine the probability of erroneous estimation for different signal fluctuations. Also it can be used as an empirical method to compare the efficiency of the different algorithms and methods on when solving the ion composition ambiguity.