Computational Statistics in Climatology

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Oxford University Press, Aug 1, 1996 - Science - 376 pages
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Scientific descriptions of the climate have traditionally been based on the study of average meteorological values taken from different positions around the world. In recent years however it has become apparent that these averages should be considered with other statistics that ultimately characterize spatial and temporal variability. This book is designed to meet that need. It is based on a course in computational statistics taught by the author that arose from a variety of projects on the design and development of software for the study of climate change, using statistics and methods of random functions.
 

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Contents

1 DIGITAL FILTERS
3
2 AVERAGING AND SIMPLE MODELS
61
3 RANDOM PROCESSES AND FIELDS
110
4 VARIABILITY OF ARMA PROCESSES
162
5 MULTIVARIATE AR PROCESSES
208
6 HISTORICAL RECORDS
239
7 THE GCM VALIDATION
266
8 SECOND MOMENTS OF RAIN
302
References
348
Index
355
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Page vi - Because the spectrum estimation methodology is based on the choice of smoothing window, the book opens with a consideration of digital filters (Chapter 1). Questions of averaging (as the dominant statistical procedure in climatology) and fitting simple linear models are given
Page vi - in Chapter 2. Estimations of spectral and correlation functions of random processes and fields are discussed in Chapter 3. Algorithms of univariate (Chapter 4) and multivariate (Chapter 5) modeling, potentially the most important methodologies for climate
Page ix - climate fluctuations for both point gauges and spatially averaged data. In many cases, the closeness of climate fluctuations to white noise or to first-order multivariate AR models is discussed.
Page v - progress from univariate modeling and spectral and correlation analysis to multivariate modeling and multidimensional spectral and correlation analysis.

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