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Versions of the Toolkit 4.0 and later utilize a convenient X-windows graphical users interface (GUI), based on X-Motif libraries. This feature makes the Toolkit more flexible to use and much easier to install than previous versions, since X-Motif is standard on Unix computer systems. Prebuilt binary executables of the latest Toolkit version are available for download.
Dynamical memory management makes Toolkit flexible for analysis of the time series of any length. The results and data are managed using matrices and vectors with the names supplied by the user in GUI.
The linear algebra routines in the Toolkit have been taken from the latest release of the LAPACK package (Version 3.0, June 1999).
Currently, the main Toolkit components are :
The GUI displays time series, eigenvalues (variance) spectra, and power spectra to windows. It can also--through the graphics packages used to display Toolkit graphs-- copy these to Postscript files for printing. The visualization requirements to generate and display these graphs are relatively simple, and the Toolkit has been designed so that different graphics packages can be accommodated. The version 4.2 of the Toolkit supports visualization using public-domain Grace , its predessor ACE/gr, and commercial IDL plotting software (versions 5.0 and later).
Practical size constraints on Toolkit analyses arise from the width of the SSA (MSSA) or correlogram windows or the number of Monte Carlo realizations used to form error bars, rather than the length of the time series. Most matrices to be diagonalized in the various procedures can have dimensions up to those allowed by the window lengths, that determine the computational times and memory requirements. As a practical matter, window widths should be less than about 300-500, but such limits depend on the size and speed of the computational device--workstation or mainframe--used. An exception to this "rule" is the Burg estimate of autocorrelation structures for SSA and AR coefficients for MEM; for long series (order of 5,000 samples), this algorithm can be time consuming also.
Data should be in the form of ascii columns, and can be either a column vector of a single time series, or a matrix of several columns, one for each time series.As indicated, Toolkit outputs take the form of postscript graphics, ASCII tables and logs, and ASCII vectors, series and power spectra. Please consult Getting Started Section for further information.
Questions, suggestions, and bug reports should be sent to ssahelp@atmos.ucla.edu.
The Toolkit is an evolving package and we encourage feedback on its form and future. We will make every effort to help you get the most from the Toolkit.
The development and application of SSA to geophysical time series was carried out by the present authors in collaboration with M. Kimoto, J.M. Lees, N. Jiang, C.L. Keppenne, K.-C. Mo, J.D. Neelin, J. Park, M.C. Penland, G. Plaut, A. Saunders, E. Simmonet, L.A. Smith, A.W. Robertson, S. Speich, C. Taricco, Y.S. Unal, R. Vautard and W. Weibel. Comments by these collaborators and numerous other colleagues on test versions of the Toolkit are gratefully acknowledged. The Toolkit was developed mostly on equipment and with support provided by Digital Equipment Corporation to the University of California, Los Angeles, Department of Atmospheric Sciences, as part of the Sequoia 2000 project. Research for the development of the methods and their applications to climatic time series was supported by NSF Grant ATM93-13217, NOAA Grant NA36G90245, and an NSF Special Creativity Award to M. Ghil. Use of trade names in this article is for identification purposes only and does not constitute endorsement by the U.S. Geological Survey.
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