Statistical Methods in the Atmospheric Sciences

By

  • Daniel Wilks, Cornell University, Ithaca, New York, U.S.A.

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.
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Audience

Researchers and students in the atmospheric sciences, including meteorology, climatology, and other geophysical disciplines

 

Book information

  • Published: May 2011
  • Imprint: ACADEMIC PRESS
  • ISBN: 978-0-12-385022-5

Reviews

"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




Table of Contents

I Preliminaries
Chapter 1 Introduction
 1.1 What Is Statistics?
 1.2 Descriptive and Inferential Statistics
 1.3 Uncertainty about the Atmosphere

Chapter 2 Review of Probability
 2.1 Background
 2.2 The Elements of Probability
 2.3 The Meaning of Probability
 2.4 Some Properties of Probability
 2.5 Exercises

II Univariate Statistics
Chapter 3 Empirical Distributions and Exploratory Data Analysis
 3.1 Background
 3.2 Numerical Summary Measures
 3.3 Graphical Summary Devices
 3.4 Reexpression
 3.5 Exploratory Techniques for Paired Data
 3.6 Exploratory Techniques for Higher-Dimensional Data
 3.7 Exercises

Chapter 4 Parametric Probability Distributions
 4.1 Background
 4.2 Discrete Distributions
 4.3 Statistical Expectations
 4.4 Continuous Distributions
 4.5 Qualitative Assessments of the Goodness of Fit
 4.6 Parameter Fitting Using Maximum Likelihood
 4.7 Statistical Simulation
 4.8 Exercises

Chapter 5 Frequentist Statistical Inference
 5.1. Background
 5.2 Some Commonly Encountered Parametric Tests
 5.3 Nonparametric Tests
 5.4 Multiplicity and "Field Significance"
 5.5. Exercises

Chapter 6 Bayesian Inference
 6.1 Background
 6.2 The Structure of Bayesian Inference
 6.3 Conjugate Distributions
 6.4 Dealing With Difficult Integrals
 6.5 Exercises

Chapter 7 Statistical Forecasting
 7.1 Background
 7.2 Linear Regression
 7.3 Nonlinear Regression 
 7.4 Predictor Selection
 7.5 Objective Forecasts Using Traditional Statistical Methods
 7.6 Ensemble Forecasting
 7.7 Ensemble MOS
 7.8 Subjective Probability Forecasts
 7.9 Exercises

Chapter 8 Forecast Verification
 8.1 Background
 8.2 Nonprobabilistic Forecasts for Discrete Predictands
 8.3 Nonprobabilistic Forecasts for Continuous Predictands
 8.4 Probability Forecasts for Discrete Predictands
 8.5 Probability Forecasts for Continuous Predictands
 8.6 Nonprobabilistic Forecasts for Fields
 8.7 Verification of Ensemble Forecasts
 8.8 Verification Based on Economic Value
 8.9 Verification When the Observation is Uncertain
 8.10 Sampling and Inference for Verification Statistics 
 8.11 Exercises

Chapter 9 Time Series
 9.1 Background
 9.2 Time Domain-I. Discrete Data
 9.3 Time Domain-II. Continuous Data
 9.4 Frequency Domain-I. Harmonic Analysis
 9.5 Frequency Domain-II. Spectral Analysis
 9.6 Exercises

III Multivariate Statistics
Chapter 10 Matrix Algebra and Random Matrices
 10.1 Background to Multivariate Statistics
 10.2 Multivariate Distance
 10.3 Matrix Algebra Review
 10.4 Random Vectors and Matrices 
 10.5 Exercises

Chapter 11 The Multivariate Normal (MVN) Distribution
 11.1 Definition of the MVN
 11.2 Four Handy Properties of the MVN
 11.3 Assessing Multinormality
 11.4 Simulation from the Multivariate Normal Distribution
 11.5 Inferences about a Multinormal Mean Vector
 11.6 Exercises

Chapter 12 Principal Component (EOF) Analysis
 12.1 Basics of Principal Component Analysis
 12.2 Application of PCA to Geophysical Fields
 12.3 Truncation of the Principal Components
 12.4 Sampling Properties of the Eigenvalues and Eigenvectors
 12.5 Rotation of the Eigenvectors
 12.6 Computational Considerations 
 12.7 Some Additional Uses of PCA
 12.8 Exercises

Chapter 13 Canonical Correlation Analysis (CCA)
 13.1 Basics of CCA
 13.2 CCA Applied to Fields
 13.3 Computational Considerations
 13.4 Maximum Covariance Analysis (MCA)
 13.5 Exercises

Chapter 14 Discrimination and Classification
 14.1 Discrimination vs. Classification
 14.2 Separating Two Populations
 14.3 Multiple Discriminant Analysis (MDA)
 14.4 Forecasting with Discriminant Analysis
 14.5 Alternatives to Classical Discriminant Analysis 
 14.6 Exercises

Chapter 15 Cluster Analysis
 15.1 Background 
 15.2 Hierarchical Clustering
 15.3 Nonhierarchical Clustering
 15.4 Exercises

Appendix A  Example Data Sets
Appendix B Probability Tables
Appendix C Answers to Exercises
References
Index