Statistical Methods in the Atmospheric Sciences
4th Edition
Published on 11. June 2019
Book
Paperback/Softback
840 pages
978-0-12-815823-4 (ISBN)
Description
Statistical Methods in the Atmospheric Sciences, Fourth Edition, continues the tradition of trying to meet the needs of students, researchers and operational practitioners. This updated edition not only includes expanded sections built upon the strengths of the prior edition, but also provides new content where there have been advances in the field, including Bayesian analysis, forecast verification and a new chapter dedicated to ensemble forecasting.
More details
Edition
4th edition
Language
English
Place of publication
United States
Target group
College/higher education
Researchers and students in the atmospheric sciences, including meteorology, climatology, and other allied disciplines involving atmospheric data
Dimensions
Height: 235 mm
Width: 191 mm
Weight
1720 gr
ISBN-13
978-0-12-815823-4 (9780128158234)
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
New editions
Daniel S. Wilks
Statistical Methods in the Atmospheric Sciences
Book
03/2026
5th Edition
Elsevier
€146.50
Available immediately
Additional editions
Daniel S. Wilks
Statistical Methods in the Atmospheric Sciences
E-Book
06/2019
4th Edition
Elsevier
€109.00
Available for download
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
1. Introduction2. Review of Probability3. Empirical Distributions and Exploratory Data Analysis4. Parametric Probability Distributions5. Frequentist Statistical Inference6. Bayesian Inference7. Statistical Forecasting8. Ensemble Forecasting9. Forecast Verification10. Time Series11. Matrix Algebra and Random Matrices12. Multivariate Normal Distribution13. Principal Component (EOF) Analysis14. Linear multivariate analysis of vector pairs: CCA, MCA, and RA15. Discrimination and Classification16. Cluster Analysis