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Statistical Methods in the Atmospheric Sciences
Expanded coverage and key updates help readers describe, analyze, test, and forecast atmospheric data.
Daniel S. Wilks (Author)
9780123850225, Elsevier Science
Hardback, published 4 July 2011
704 pages
23.4 x 19 x 3.7 cm, 1.35 kg
"I would strongly recommend this book... To those who already posses the first edition and are satisfied users, you would be hard-pressed to do without the second edition." --Bulletin of the American Meteorological Society "What makes this book specific to meterology, and not just to applied statistics, are it's extensive examples and two chapters on statistcal forecasting and forecast evaluation." --William (Matt) Briggs, Weill Medical College of Cornell University "Wilks (earth and atmospheric sciences, Cornell U.) presents a textbook for an upper-division undergraduate or beginning graduate course for students who have completed a first course in statistics and are interested in learning further statistics in the context of atmospheric sciences. No mathematics beyond first-year calculus is required, nor any background in atmospheric science, though some would be helpful. He also has in mind researchers using the book as a reference. No dates are cited for previous editions, this one adds a chapter on Bayesian inference, updates the treatment throughout, and includes new references to recently published literature." --SciTech Book News
Statistical Methods in the Atmospheric Sciences, Third Edition, explains the latest statistical methods used to describe, analyze, test, and forecast atmospheric data. This revised and expanded text is intended to help students understand and communicate what their data sets have to say, or to make sense of the scientific literature in meteorology, climatology, and related disciplines. In this new edition, what was a single chapter on multivariate statistics has been expanded to a full six chapters on this important topic. Other chapters have also been revised and cover exploratory data analysis, probability distributions, hypothesis testing, statistical weather forecasting, forecast verification, and time series analysis. There is now an expanded treatment of resampling tests and key analysis techniques, an updated discussion on ensemble forecasting, and a detailed chapter on forecast verification. In addition, the book includes new sections on maximum likelihood and on statistical simulation and contains current references to original research. Students will benefit from pedagogical features including worked examples, end-of-chapter exercises with separate solutions, and numerous illustrations and equations. This book will be of interest to researchers and students in the atmospheric sciences, including meteorology, climatology, and other geophysical disciplines.
I Preliminaries1. Introduction2. Review of Probability II Univariate Statistics3. Empirical Distributions and Exploratory Data Analysis4. Parametric Probability Distributions5. Frequentist Statistical Inference6. Bayesian Inference7. Statistical Forecasting8. Forecast Verification9. Time Series III Multivariate Statistic10. Matrix Algebra and Random Matrices11. The Multivariate Normal (MVN) Distribution12. Principal Component (EOF) Analysis13. Canonical Correlation Analysis (CCA)14. Discrimination and Classification15. Cluster Analysis AppendixA. Example Data SetsB. Probability TablesC. Answers to Exercises
Subject Areas: Meteorology & climatology [RBP], Atmospheric physics [PHVJ], Probability & statistics [PBT]
