Space‐time analysis of TIMED Doppler Interferometer (TIDI) measurements

Abstract

Abstract.A technique is developed for reducing the amount of aliasing in the spectral analysis of TIDI observations, by ingestion of ground-based data into the satellite data set. A multi-dimensional (space-time) least squares fitting approach is applied to the satellite and ground-based data to determine the aliasing spectra. The addition of ground-based data to the TIDI data set reduces the aliased components in the aliasing spectrum. For example, at 20 ø latitude, the combined ground-based and TIDI data set of a sampled input semidiurnal (frequency of 2 days -•) signal with zonal wavenumber 2 results in a factor of 2 reduction in the amount of power aliasing into a signal with zonal wavenumber 0 and frequency 0 days -•.

Tidal signatures and aliasing in temperature data from slowly precessing satellites

“…Such a method is currently being developed. Another promising approach for a comprehensive tidal analysis is probably the combination of ground‐based observations with the satellite data [ Azeem et al , 2000; Drob et al , 2000]. However, systematic offsets between the instruments and the widely different sampling volumes also require careful attention.…”

Global structure and seasonal and interannual variability of the migrating diurnal tide seen in the SABER/TIMED temperatures between 20 and 120 km

“…A significant advantage of this approach is that it directly maps the asynoptic data set to the equivalent space‐time spectrum. However, since this approach relies on the discrete Fourier transform operation, any irregularities in the sampling pattern, i.e., not evenly spaced sampling, will destroy the orthogonality of discrete projections of the observations onto the Fourier expansion functions [ Azeem et al , 2000]. To avoid the constraint of sampling uniformity we use a least squares fitting technique to spectrally analyze a two‐dimensional space‐time data set.…”

High-altitude data assimilation system experiments for the northern summer mesosphere season of 2007

“…Data analysis studies to date have employed complex asynoptic mapping or least-squares fitting algorithms that require assumptions about stationarity, aliasing and seasonal dependences (e.g., Wu et al, 1995;Burrage et al, 1995;Forbes et al, 1997;Zhu et al, 2005). While these assumptions can be tested and the procedures improved with the help of MLT fields from general circulation models (GCMs) (Oberheide et al, 2003;McLandress and Zhang, 2007) and addition of data from other instruments (Drob et al, 2000;Azeem et al, 2000), final mean mesospheric temperature estimates from these algorithms can still have large uncertainties (Drob et al, 2000;Oberheide et al, 2003;Zhu et al, 2005). NWP systems combine aspects of all the aforementioned algorithms by optimally assimilating MLT data from a variety of sources with the aid of a full-physics GCM to constrain the system dynamically and optimally fill gaps.…”