
Statistical Methods in the Atmospheric Sciences: Volume 100
Volume 100
Daniel S. Wilks(Author)
Academic Press
3rd Edition
Published on 4. July 2011
Book
Hardback
704 pages
978-0-12-385022-5 (ISBN)
Description
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.
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.
Reviews / Votes
"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
More details
Series
Edition
3rd edition
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
Professional and scholarly
Researchers and students in the atmospheric sciences, including meteorology, climatology, and other geophysical disciplines
Product notice
Unsewn / adhesive bound
Paper over boards
Dimensions
Height: 238 mm
Width: 197 mm
Thickness: 43 mm
Weight
1557 gr
ISBN-13
978-0-12-385022-5 (9780123850225)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Daniel S. Wilks
Statistical Methods in the Atmospheric Sciences
E-Book
07/2011
3rd Edition
Academic Press
€75.95
Available for download
Previous edition

Daniel S. Wilks
Statistical Methods in the Atmospheric Sciences: Volume 100
Book
12/2005
2nd Edition
Academic Press
€69.32
Article exhausted; check for reprint
Person
Daniel S. Wilks has been a member of the Atmospheric Sciences faculty at Cornell University since 1987. His research focuses on the application of statistical methods for quantification and analysis of uncertainty in meteorological and climatological data and forecasts. Dr. Wilks has taught courses on statistics in the atmospheric sciences and has been author or coauthor of more than 100 peer-reviewed research articles.
Content
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
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