Climatology and meteorology have basically been a descriptive
science until it became possible to use numerical models, but it is
crucial to the success of the strategy that the model must be a
good representation of the real climate system of the Earth. Models
are required to reproduce not only the mean properties of climate,
but also its variability and the strong spatial relations between
climate variability in geographically diverse regions. Quantitative
techniques were developed to explore the climate variability and
its relations between different geographical locations. Methods
were borrowed from descriptive statistics, where they were
developed to analyze variance of related observations-variable
pairs, or to identify unknown relations between variables.
A Guide to Empirical Orthogonal Functions for Climate Data
Analysis uses a different approach, trying to introduce the reader
to a practical application of the methods, including data sets from
climate simulations and MATLAB codes for the algorithms. All
pictures and examples used in the book may be reproduced by using
the data sets and the routines available in the book .
Though the main thrust of the book is for climatological
examples, the treatment is sufficiently general that the discussion
is also useful for students and practitioners in other fields.
Supplementary datasets are available via http:
//extra.springer.com
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