Data sets in the geosciences are often full of gaps. This is usually the case for geological and paleoclimatological proxy data from the remote past, as well as for historical records from the more recent past. In modern times, data points may be missing because of the way the measurements are obtained. For example, remote sensing instrumentation can be hampered by clouds, aerosols, or heavy precipitation. The presence of gaps in data sets presents various problems, for example in spectral estimation, specifying boundary conditions in numerical models, and so on. While the microwave sensors installed on recently launched NASA satellites provided unprecedented quality of sea-surface temperature and sea-level wind observations in the region of the Southern Ocean, there are still gaps in data coverage in the heavy rain regions, as well as in the regions of strong winds. We demonstrate how Singular Spectrum Analysis (SSA) and multi-channel SSA can be applied to fill the missing data in the remotely sensed datasets of the Southern Ocean with an iteratively inferred smooth "signal" that represents coherent spatio-temporal structures, while the "noise" variance is discarded or reduced (Kondrashov and Ghil, 2006). Doing so can be quite valuable in a wide variety of other applications, ranging from reconstruction of paleoclimatic and instrumental climate data to oceanographic and space physics data.
AGU Fall Meeting Abstracts
- Pub Date:
- December 2007
- 3270 Time series analysis (1872;