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3rd Edition, Volume 100 - May 20, 2011

Author: Daniel S. Wilks

Language: EnglishHardback ISBN:

9 7 8 - 0 - 1 2 - 3 8 5 0 2 2 - 5

eBook ISBN:

9 7 8 - 0 - 1 2 - 3 8 5 0 2 3 - 2

Statistical Methods in the Atmospheric Sciences, Third Edition, explains the latest statistical methods used to describe, analyze, test, and forecast atmospheric data. This revi… Read more

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Immediately download your ebook while waiting for your print delivery. No promo code is needed.

*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

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

I PreliminariesChapter 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 StatisticsChapter 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 StatisticsChapter 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

- No. of pages: 704
- Language: English
- Edition: 3
- Volume: 100
- Published: May 20, 2011
- Imprint: Academic Press
- Hardback ISBN: 9780123850225
- eBook ISBN: 9780123850232

DW

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.

Affiliations and expertise

Department of Earth and Atmospheric Sciences, Cornell University, USARead *Statistical Methods in the Atmospheric Sciences* on ScienceDirect