Revisiting the Ariel Trough Work for HF Telecommunication Purposes

Muldrew [1] was the pioneer who reported the midlatitude electron density trough at the topside ionosphere. For about ten to fifteen years the trough, its morphology, dynamical behavior, relationship to the equatorial plasmapause, and physical and chemical processes which lead to the trough formation had been extensively investigated. Then, the work on the trough had been slowed down gradually. As the new space systems have become more vulnerable to space weather effects, a need for robust programs and a long track record in space environment sensing and modeling to produce new space environment models and products that would meet high-priority defense and commercial needs arises naturally. In this context, it is intended to go over the reported trough work dating back to the 1970s and some typical findings of later developments briefly. Most of the aspects of the trough studies have been repeated with new data for newer physical models. From this point of view, the Ariel 3 and Ariel 4 satellite trough results are chosen since the work on the Ariel trough had been very original and very extensive quantitatively and qualitatively in the 1970s. The results reviewed here are based on more than 1000 beautiful selected trough cases. Due to the good quality and quantity of the Ariel satellite data, equal coverage in space and time were maintained, which makes the trough results very important. This paper will end with some reference to the trough models, results that establish a link between the topside and the F2 region of the ionosphere. As one typical application, HF radiocommunication is chosen to be the point of interest. In practical applications of the HF radiocommunications any model that does not include the trough is not complete.     

Forecasting magnetopause crossing locations by using Neural Networks

Given the highly complex and nonlinear nature of Near Earth Space processes, mathematical modeling of these processes is usually difficult or impossible. In such cases, modeling methods involving Artificial Intelligence may be employed. We demonstrate that data driven models, such as the Neural Network based approach, shows promise in its ability to forecast or predict the behavior of these processes. In this paper, modeling studies for forecasting magnetopause crossing locations are summarized and a Neural Network algorithm is presented to describe the nonlinear time-dependent response of the subsolar region of the magnetopause to varying solar wind conditions. In our approach the past history of the solar wind has, for the first time to the best knowledge of the authors, been included in forecasting the subsolar region of the magnetopause. It is proposed that the data driven approach is a valid approach to understanding and modeling the physical phenomena of Near Earth Space. The only basic requirement for the data driven approach is the availability of representative data for the phenomena. The objective of this paper is to demonstrate that by using WIND and GEOTAIL satellite data a Neural Network based model can be adapted to the modeling of the Earth’s magnetopause.