Statistical Methods in the Atmospheric Sciences
Beschreibung:
Daniel S. Wilks has been a member of the Atmospheric Sciences faculty at Cornell University since 1987, and is the author of Statistical Methods in the Atmospheric Sciences (2011, Academic Press), which is in its third edition and has been continuously in print since 1995. Research areas include statistical forecasting, forecast postprocessing, and forecast evaluation.
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.
- Accessible presentation and explanation of techniques for atmospheric data summarization, analysis, testing and forecasting
- Many worked examples
- End-of-chapter exercises, with answers provided
I Preliminaries 1. Introduction 2. Review of Probability
II Univariate Statistics 3. Empirical Distributions and Exploratory Data Analysis 4. Parametric Probability Distributions 5. Frequentist Statistical Inference 6. Bayesian Inference 7. Statistical Forecasting 8. Forecast Verification 9. Time Series
III Multivariate Statistic 10. Matrix Algebra and Random Matrices 11. The Multivariate Normal (MVN) Distribution 12. Principal Component (EOF) Analysis 13. Canonical Correlation Analysis (CCA) 14. Discrimination and Classification 15. Cluster Analysis
Appendix A. Example Data Sets B. Probability Tables C. Answers to Exercises