Statistical Methods in the Atmospheric Sciences, Volume 100

3rd Edition

Print ISBN: 9780123850225
eBook ISBN: 9780123850232
Imprint: Academic Press
Published Date: 20th May 2011
Page Count: 704
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This revised text introduces and explains the statistical methods used to describe, analyze, test and forecast atmospheric data. It will be useful to scientists, students, and other professionals who seek to make sense of the scientific literature in meteorology, climatology, or other geophysical disciplines, or to understand and communicate what their atmospheric data sets have to say.

The chapters cover exploratory data analysis, probability distributions, hypothesis testing, statistical weather forecasting, forecast verification, time series analysis and multivariate data analysis. Worked examples, exercises, and illustrations facilitate understanding of the material; an extensive and current list of references allows the reader to pursue topics in greater depth.

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



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© Academic Press 2011
Academic Press
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