Abstract
Abstract. Space weather effects can strongly influence high-frequency (HF) communications by changing the ionospheric environment through which the radio waves propagate. Since many systems utilize HF communications, the ability to make real-time assessments of propagation conditions is an important part of space weather monitoring systems. In this paper, we present new techniques for measuring high-latitude HF communications link parameters using data from SuperDARN radars. These techniques use ground-scatter returns to define the variation in skip distance with frequency. From these data, the maximum usable frequency (MUF) as a function of range is determined and ionospheric critical frequencies are estimated. These calculations are made in near-real-time and the results are made available on the World Wide Web. F-region critical frequencies calculated using this method show good agreement with ionosonde data.Key words. Ionosphere (active experiments; instruments and techniques) – Radio science (ionospheric propagation)
Highlights
Variations in the solar, magnetospheric, and ionospheric characteristics can affect a variety of ground-based and space-borne technological systems (e.g. Hargreaves, 1995; Feynman and Gabriel, 2000)
We shall give a brief summary of the Radial Basis Function Neural Network (RBF-neural networks (NNs)) model used in this study
A subjective measure of the effectiveness of the technique can be seen in Fig. 1, which shows a 1-hour ahead RBF-NN model following the variations in foF2 through a storm period in February 2000
Summary
Variations in the solar, magnetospheric, and ionospheric characteristics can affect a variety of ground-based and space-borne technological systems (e.g. Hargreaves, 1995; Feynman and Gabriel, 2000). Williscroft and Poole (1996) predicted daily and monthly noon values of the ionospheric parameter foF2 at Grahamstown, South Africa, using seasonal time information, solar and magnetic activities as input data. Cannon: Nonlinear forecasts of foF2 have used only the ionospheric parameter foF2 as input for their neural network models to predict foF2 This is an example of a self-prediction model. The neural network models and their associated error statistics usually presented are quite specific to a particular epoch This was illustrated forcefully when a re-optimized version of the Francis et al (2000) 1-hour ahead forecasting model was incorporated into our real-time forecasting system – the Ionospheric Forecasting Demonstrator, IFD (http://www.cpar.qinetiq.com). We describe a number of methods for assessing the long-term performance of NN models, and illustrate how the predictive accuracy of a NN model can be maintained in a non-stationary environment
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
