Autocorrelation method for temporal interpolation and short-term prediction of ionospheric data

An autocorrelation method is developed for temporal interpolation and short -term prediction of ionospheric characteristics. The ionospheric data are c onsidered as a realization of a periodic process with randomly dispersed me asured values superimposed on it. The autocorrelation function or its norma lized autocorrelation coefficients are determined from the measured data ov er a period of 20-30 days, and on that basis an autocorrelation model is ob tained. This model is then used to interpolate the missing values in the mo nthly tables of ionospheric characteristics, here called "gaps." The interp olation at a given hour is performed by calculating weighting coefficients for the neighboring measured values. The procedure selects those measuremen t values around the gap which have the highest autocorrelation coefficients . The model can be used to extrapolate (predict) the data, treating the pre diction period (usually 24 hours) as a gap placed at the end of the availab le data. The method also calculates the so-called prediction error, which i s found to be close to the standard deviation of the measured data. The int erpolation and prediction error are estimated to be less than 12% in the ca se of f(o)F(2).