Synopses & Reviews
Praise for the First Edition:
"I recommend this book, without hesitation, as either a reference or course text...Wilks' excellent book provides a thorough base in applied statistical methods for atmospheric sciences."--BAMS (Bulletin of the American Meteorological Society)
Fundamentally, statistics is concerned with managing data and making inferences and forecasts in the face of uncertainty. It should not be surprising, therefore, that statistical methods have a key role to play in the atmospheric sciences. It is the uncertainty in atmospheric behavior that continues to move research forward and drive innovations in atmospheric modeling and prediction.
This revised and expanded text explains the latest statistical methods that are being used to describe, analyze, test and forecast atmospheric data. It features numerous worked examples, illustrations, equations, and exercises with separate solutions. Statistical Methods in the Atmospheric Sciences, Second Edition will help advanced students and professionals understand and communicate what their data sets have to say, and make sense of the scientific literature in meteorology, climatology, and related 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 would strongly recommend this book... To those who already posses the first edition...you would be hard-pressed to do without the second."
--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
Table of Contents
Ch. 1 Introduction
Ch. 2 Review of Probability
II Univariate Statistics
Ch. 3 Empirical Distributions and Exploratory Data Analysis
Ch. 4 Parametric Probability Distributions
Ch. 5 Frequentist Statistical Inference
Ch. 6 Bayesian Inference
Ch. 7 Statistical Forecasting
Ch. 8 Forecast Verification
Ch. 9 Time Series
III Multivariate Statistics
Ch. 10 Matrix Algebra and Random Matrices
Ch. 11 The Multivariate Normal (MVN) Distribution
Ch. 12 Principal Component (EOF) Analysis
Ch. 13 Canonical Correlation Analysis (CCA)
Ch. 14 Discrimination and Classification
Ch. 15 Cluster Analysis
Appendix A Example Data Sets
Appendix B Probability Tables
Appendix C Answers to Exercises