Statistical Methods in the Atmospheric Sciences - 2nd Edition - ISBN: 9780127519661, 9780080456225

Statistical Methods in the Atmospheric Sciences, Volume 100

2nd Edition

Authors: Daniel Wilks
eBook ISBN: 9780080456225
Imprint: Academic Press
Published Date: 21st November 2005
Page Count: 648
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Table of Contents

  • Preface to the First Edition
  • Preface to the Second Edition
  • PART 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
  • PART II: Univariate Statistics
    • CHAPTER 3. Empirical Distributions and Exploratory Data Analysis
      • 3.1 Background
      • 3.2 Numerical Summary Measures
      • 3.3 Graphical Summary Techniques
      • 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. Hypothesis Testing
      • 5.1 Background
      • 5.2 Some Parametric Tests
      • 5.3 Nonparametric Tests
      • 5.4 Field Significance and Multiplicity
      • 5.5 Exercises
    • CHAPTER 6. Statistical Forecasting
      • 6.1 Background
      • 6.2 Linear Regression
      • 6.3 Nonlinear Regression
      • 6.4 Predictor Selection
      • 6.5 Objective Forecasts Using Traditional Statistical Methods
      • 6.6 Ensemble Forecasting
      • 6.7 Subjective Probability Forecasts
      • 6.8 Exercises
    • CHAPTER 7. Forecast Verification
      • 7.1 Background
      • 7.2 Nonprobabilistic Forecasts of Discrete Predictands
      • 7.3 Nonprobabilistic Forecasts of Continuous Predictands
      • 7.4 Probability Forecasts of Discrete Predictands
      • 7.5 Probability Forecasts for Continuous Predictands
      • 7.6 Nonprobabilistic Forecasts of Fields
      • 7.7 Verification of Ensemble Forecasts
      • 7.8 Verification Based on Economic Value
      • 7.9 Sampling and Inference for Verification Statistics
      • 7.10 Exercises
    • CHAPTER 8. Time Series
      • 8.1 Background
      • 8.2 Time Domain—I. Discrete Data
      • 8.3 Time Domain—II. Continuous Data
      • 8.4 Frequency Domain—I. Harmonic Analysis
      • 8.5 Frequency Domain—II. Spectral Analysis
      • 8.6 Exercises
  • PART III: Multivariate Statistics
    • CHAPTER 9. Matrix Algebra and Random Matrices
      • 9.1 Background to Multivariate Statistics
      • 9.2 Multivariate Distance
      • 9.3 Matrix Algebra Review
      • 9.4 Random Vectors and Matrices
      • 9.5 Exercises
    • CHAPTER 10. The Multivariate Normal (MVN) Distribution
      • 10.1 Definition of the MVN
      • 10.2 Four Handy Properties of the MVN
      • 10.3 Assessing Multinormality
      • 10.4 Simulation from the Multivariate Normal Distribution
      • 10.5 Inferences about a Multinormal Mean Vector
      • 10.6 Exercises
    • CHAPTER 11. Principal Component (EOF) Analysis
      • 11.1 Basics of Principal Component Analysis
      • 11.2 Application of PCA to Geophysical Fields
      • 11.3 Truncation of the Principal Components
      • 11.4 Sampling Properties of the Eigenvalues and Eigenvectors
      • 11.5 Rotation of the Eigenvectors
      • 11.6 Computational Considerations
      • 11.7 Some Additional Uses of PCA
      • 11.8 Exercises
    • CHAPTER 12. Canonical Correlation Analysis (CCA)
      • 12.1 Basics of CCA
      • 12.2 CCA Applied to Fields
      • 12.3 Computational Considerations
      • 12.4 Maximum Covariance Analysis
      • 12.5 Exercises
    • CHAPTER 13. Discrimination and Classification
      • 13.1 Discrimination vs. Classification
      • 13.2 Separating Two Populations
      • 13.3 Multiple Discriminant Analysis (MDA)
      • 13.4 Forecasting with Discriminant Analysis
      • 13.5 Alternatives to Classical Discriminant Analysis
      • 13.6 Exercises
    • CHAPTER 14. Cluster Analysis
      • 14.1 Background
      • 14.2 Hierarchical Clustering
      • 14.3 Nonhierarchical Clustering
      • 14.4 Exercises
  • APPENDIX A. Example Data Sets
  • APPENDIX B. Probability Tables
  • APPENDIX C. Answers to Exercises
    • Chapter 2
    • Chapter 3
    • Chapter 4
    • Chapter 5
    • Chapter 6
    • Chapter 7
    • Chapter 8
    • Chapter 9
    • Chapter 10
    • Chapter 11
    • Chapter 12
    • Chapter 13
    • Chapter 14
  • References
  • Index
  • International Geophysics Series

Description

Statistical Methods in the Atmospheric Sciences, Second 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.

Key Features

  • Presents and explains techniques used in atmospheric data summarization, analysis, testing, and forecasting
  • Features numerous worked examples and exercises
  • Covers Model Output Statistic (MOS) with an introduction to the Kalman filter, an approach that tolerates frequent model changes
  • Includes a detailed section on forecast verificationNew in this Edition:
  • Expanded treatment of resampling tests and coverage of key analysis techniques
  • Updated treatment of ensemble forecasting
  • Edits and revisions throughout the text plus updated references

Readership

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


Details

No. of pages:
648
Language:
English
Copyright:
© Academic Press 2006
Published:
Imprint:
Academic Press
eBook ISBN:
9780080456225

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


About the Authors

Daniel Wilks Author

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.

Affiliations and Expertise

Department of Earth & Atmospheric Sciences, Cornell University, USA