This article discusses the numerical approximation oftime dependent Ginzburg-Landau equations. Optimal error estimates which are robust with respectto a large Ginzburg-Landau parameter are established for asemi-discrete in time and a fully discrete approximationscheme. The proofs rely on an asymptotic expansion of the exact solution and a stability resultfor degree-one Ginzburg-Landau vortices. The error boundsprove that degree-one vortices can be approximated robustlywhile unstable higher degree vortices are critical.

A spectral density statistic obtained by averaging periodograms over overlapping time intervals is considered where the periodograms are calculated using a data window. The asymptotic mean square error of this estimate for scale parameter windows is determined and, as an example, it is shown that the use of the Tukey–Hanning window leads partially to a smaller mean square error than a window suggested by Kolmogorov and Zhurbenko. Furthermore the Tukey–Hanning window is independent of the unknown spectral density, which is not the case for the Kolmogorov–Zhurbenko window. The mean square error of this estimate is also less than the mean square error of commonly used window estimates. Finally, a central limit theorem for the estimate is established.