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Statistical Methods in the Atmospheric Sciences
 
 

Statistical Methods in the Atmospheric Sciences, 3rd Edition

 
Statistical Methods in the Atmospheric Sciences, 3rd Edition,Daniel Wilks,ISBN9780123850225
 
 
 

  

Academic Press

9780123850225

9780123850232

704

235 X 191

Expanded coverage and key updates help readers describe, analyze, test, and forecast atmospheric data.

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Key Features

  • Accessible presentation and explanation of techniques for atmospheric data summarization, analysis, testing and forecasting

  • Many worked examples

  • End-of-chapter exercises, with answers provided

  • Description

    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.

    Readership

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

    Daniel Wilks

    Affiliations and Expertise

    Cornell University, Ithaca, New York, U.S.A.

    Statistical Methods in the Atmospheric Sciences, 3rd Edition

    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 

     

    Quotes and 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

     
     
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