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.

Related content

A Guide to Empirical
Orthogonal Functions for Climate Data Analysis on Amazon