[1] Near-Earth space processes are highly nonlinear. Since the 1990s, a small group at the Middle East Technical University in Ankara has been working on a data-driven generic model of such processes, that is, forecasting and nowcasting of a near-Earth space parameter of interest. The model developed is called the Middle East Technical University Neural Network (METU-NN) model. The METU-NN is a data-driven neural network model of one hidden layer and several neurons. In order to understand more about the complex response of the magnetosphere and ionosphere to extreme solar events, we chose this time the series of space weather events in November 2003. Total electron content (TEC) values of the ionosphere are forecast during these space weather events. In order to facilitate an easier interpretation of the forecast TEC values, maps of TEC are produced by using the Bezier surface-fitting technique.

No abstract

No abstract

Abstract. The ionospheric critical frequency, foE2, is forecast 1 hour in advance by using artificial neural networks. The value of foE2 at the time instant k of the day is designated byf(k). The inputs used for the neural network are the time of day; the day of year; season information; past observations offoF2; the first difference A l(k ) = f(k)-f(k-1); the second difference A 2 (k) = A 1 (k) -A l(k -1); the relative difference RA (k) = A l(k)/f(k); geomagnetic indices Kp, ap, Dst, sunspot number, and solar 10.7-cm radio flux; and the solar wind magnetic field components By and B•. This paper gives a new method, and it is the first application of neural networks for modeling both temporal and spatial dependencies. In order to understand the physical characteristics of the process and determine how important a particular input is, a test which shows the relative significance of inputs to the neural networks is performed at the output. The performance of a neural network is measured by considering errors. For the errors to be more meaningful, training and test times and times for comparison with other results are selected from the same solar activity period. Among the various neural network structures, the best configuration is found to be the one with one hidden layer with five hidden neurons, giving an absolute overall error of 5.88%, or 0.432 MHz. IntroductionSolar activity has an impact on magnetospheric dynamics, which in turn influences the plasma density in the ionosphere. It is important to quantify the plasma density variability for both scientific and practical reasons. One of the practical methods for determining the ionospheric variability' is to quantify the electron density or the ionospheric critical frequencyfo The objective is to determine the short-term details of the ionospheric variability, and 1 hour is a typical time period for obtaining the details of the medium reasonably well from the point of view of a telecom-1497

No abstract