[1] Ground-based ionosphere sounding measurements alone are incapable of reliably modeling the topside electron density distribution above the F layer peak density height. Such information can be derived from Global Positioning System (GPS)-based total electron content (TEC) measurements. A novel technique is presented for retrieving the electron density height profile from three types of measurements: ionosonde ( f o F 2 , f o E, M 3000 F 2 , h m f 2 ), TEC (GPS-based), and O + -H + ion transition level. The method employs new formulae based on Chapman, sech-squared, and exponential ionosphere profilers to construct a system of equations, the solution of which system provides the unknown ion scale heights, sufficient to construct a unique electron density profile at the site of measurements. All formulae are based on the assumption of diffusive equilibrium with constant scale height for each ion species. The presented technique is most suitable for middle-and high-geomagnetic latitudes and possible applications include: development, evaluation, and improvement of theoretical and empirical ionospheric models, development of similar reconstruction methods utilizing low-earth-orbiting satellite measurements of TEC, operational reconstruction of the electron density on a real-time basis, etc.

Abstract. An empirical model is developed to describe the variations of midlatitude F region ionization along all longitudes within the dip latitude band (30ø-55øN), induced by geomagnetic activity, by using the relative deviations (cI)) of the F region critical frequencyfoF2 from its monthly median. The geomagnetic activity is represented by the Kp index. The main statistical relationship between cI) and Kp is obtained by using 11 years of data from 26 midlatitude ionosondes. The statistical analysis reveals that the average dependence of cI) on Kp is quadratic, the average response of the ionosphere to geomagnetic forcing is delayed with a time constant T of about 18 hours, and the instantaneous distribution of cI) along local times can be assumed sinusoidal. A continuity equation is written for cI) with the "Production term" being a

Abstract. An autocorrelation method is developed for temporal interpolation and short-term prediction of ionospheric characteristics. The ionospheric data are considered as a realization of a periodic process with randomly dispersed measured values superimposed on it. The autocorrelation function or its normalized autocorrelation coefficients are determined from the measured data over a period of 20-30 days, and on that basis an autocorrelation model is obtained. This model is then used to interpolate the missing values in the monthly tables of ionospheric characteristics, here called "gaps." The interpolation at a given hour is performed by calculating weighting coefficients for the neighboring measured values. The procedure selects those measurement values around the gap which have the highest autocorrelation coefficients. The model can be used to extrapolate (predict) the data, treating the prediction period (usually 24 hours) as a gap placed at the end of the available data. The method also calculates the so-called prediction error, which is found to be close to the standard deviation of the measured data. The interpolation and prediction error are estimated to be less than 12% in the case offoF2.

A new method for short-term prediction of ionospheric parameters is developed by incorporating the cross-correlation between the ionospheric characteristic of interest and the Ap index into the autocorrelation analysis. We consider the hourly time series of an ionospheric characteristic as composed of a periodic component and a random component. The periodic component containing the average diurnal variation is removed by using its relative deviations from the median values (Φ), which in the case of the critical frequency of the F2 layer, foF2, has the form: Φ = (foF2 - foF2med)/foF2med. The geomagnetically correlated autoregression model (GCAM) is an extrapolation model based on the weighted past data. The new term in the regression equation expresses linearly the dependence of Φ on magnetic activity by introducing a synthetic geomagnetic index G, which approximates the average dependence of Φ on hourly interpolated Kp. Using parametric expressions of the auto- and cross-correlation functions ensures the statistical sufficiency in GCAM; the parameters are then obtained by data fitting. Data from 2 years of high solar activity (1981-2) and 2 years of low solar activity (1985-6) were used to evaluate the prediction accuracy of GCAM. The mean square error in per cent of the 1-day prediction of foF2 relative to the median shows a large gain of accuracy of GCAM in the first 8-10 h of prediction relative to the median based prediction, a diurnal variation of errors and a steady offset of the GCAM prediction error from the median based prediction error. The GCAM error at the first hour is lowest, but gradually approaches the median error with a timescale of 8-10 h. A new error estimate, called `prediction efficiency' that is a good indicator of prediction performance during disturbed ionospheric conditions is defined.

The purpose of the LIEDR (local ionospheric electron density profile reconstruction) system is to acquire and process data from simultaneous ground-based total electron content (TEC) and digital ionosonde measurements, and subsequently to deduce the vertical electron density distribution above the ionosonde's location. LIEDR is primarily designed to operate in real time for service applications and, for research applications and further development of the system, in a post-processing mode. The system is suitable for use at sites where collocated TEC and digital ionosonde measurements are available. Developments, implementations, and some preliminary results are presented and discussed in view of possible applications.

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