Singular Value Decomposition of Snowfall Data

SVD_SNOWFALL is a MATLAB library which demonstrates the use of the Singular Value Decomposition (SVD) to analyze a set of historical snowfall data.

The snowfall data consists of records for the winters of 1890-1891 to 2014-2015, of the snowfall in inches, over the months from October to May, as measured at Michigan Tech.

This data can be regarded as an 8 by 125 matrix A. Applying the singular value decomposition produces the factors

A = U * S * V'
and it is the purpose of this library to consider what these factors indicate about the snowfall data.


The computer code and data files described and made available on this web page are distributed under the GNU LGPL license.


SVD_SNOWFALL is available in a C version and a C++ version and a FORTRAN77 version and a FORTRAN90 version and a MATLAB version.

Related Data and Programs:

FINGERPRINTS, a dataset directory which contains a few images of fingerprints.

SVD_BASIS, a MATLAB program which computes a reduced basis for a collection of data vectors using the SVD.

SVD_DEMO, a MATLAB program which demonstrates the singular value decomposition (SVD) for a simple example.

SVD_FINGERPRINT, a MATLAB program which reads a file containing a fingerprint image and uses the singular value decomposition (SVD) to compute and display a series of low rank approximations to the image.

SVD_TRUNCATED, a MATLAB program which demonstrates the computation of the reduced or truncated Singular Value Decomposition (SVD) of an M by N rectangular matrix, in cases where M < N or N < M.

TIME_SERIES, a dataset directory which contains examples of files describing time series.


  1. Edward Anderson, Zhaojun Bai, Christian Bischof, Susan Blackford, James Demmel, Jack Dongarra, Jeremy Du Croz, Anne Greenbaum, Sven Hammarling, Alan McKenney, Danny Sorensen,
    LAPACK User's Guide,
    Third Edition,
    SIAM, 1999,
    ISBN: 0898714478,
    LC: QA76.73.F25L36
  2. Gene Golub, Charles VanLoan,
    Matrix Computations, Third Edition,
    Johns Hopkins, 1996,
    ISBN: 0-8018-4513-X,
    LC: QA188.G65.
  3. David Kahaner, Cleve Moler, Steven Nash,
    Numerical Methods and Software,
    Prentice Hall, 1989,
    ISBN: 0-13-627258-4,
    LC: TA345.K34.
  4. Lloyd Trefethen, David Bau,
    Numerical Linear Algebra,
    SIAM, 1997,
    ISBN: 0-89871-361-7,
    LC: QA184.T74.

Source code:

Examples and Tests:

You can go up one level to the MATLAB source codes.

Last revised on 05 May 2013.