Adaptive filtering and prediction of noisy multivariate signals: an application to subannual variability in atmospheric angular momentum

Citation:

Keppenne, Christian L., and Michael Ghil. 1993. “Adaptive filtering and prediction of noisy multivariate signals: an application to subannual variability in atmospheric angular momentum.” International Journal of Bifurcation and Chaos 3: 625–634.

Abstract:

Principal component analysis (PCA) in the space and time domains is applied to filter adaptively the dominant modes of subannual (SA) variability of a 12-year long multivariate time series of Northern Hemisphere atmospheric angular momentum (AAM); AAM is computed in 23 latitude bands of equal area from operational analyses of the U.S. National Meteorological Center. PCA isolates the leading empirical orthogonal functions (EOFs) of spatial dependence, while multivariate singular spectrum analysis (M-SSA) yields filtered time series that capture the dominant low-frequency modes of SA variability. The time series prefiltered by M-SSA lend themselves to prediction by the maximum entropy method (MEM). Whole-field predictions are made by combining the forecasts so obtained with the leading spatial EOFs obtained by PCA. The combination of M-SSA and MEM has predictive ability up to about a month. These methods are essentially linear but data-adaptive. They seem to perform well for short, noisy, multivariate time series, to which purely nonlinear, deterministically based methods are difficult to apply.

Last updated on 08/09/2016