Ebook: A Guide to Empirical Orthogonal Functions for Climate Data Analysis
- Genre: Geography // Meteorology; Climatology
- Tags: Meteorology/Climatology, Mathematical Applications in Earth Sciences, Computational Science and Engineering, Statistics for Engineering Physics Computer Science Chemistry and Earth Sciences, Climate Change
- Year: 2010
- Publisher: Springer Netherlands
- City: Dordrecht; New York
- Edition: 1
- Language: English
- pdf
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.
I asked my local library to purchase this book before I invested personally in it. Overall, it is very good. It begins with very limited sections on linear algebra & statistics and progresses to a very general overview of empirical orthogonal function analysis. This includes some discussion of principal components, eigenvalues, singular value decomposition, and multiple regression analysis. I liked this book because it started with the basics. However, I'm pretty confident that someone with a greater statistics & linear algebra background would find that the book spends too much time on 'pre-EOF' topics and does not go into enough detail about EOF analysis. Even I felt that way once I finished. The Matlab code the book provides as well as the explanations of different rotations used in EOF analysis are excellent additions which add a lot of value to the book. This is a small book however, and it is overpriced, in my opinion.