Time Series Analysis in Meteorology and Climatology: An Introduction

Overview

Time Series Analysis in Meteorology and Climatology provides an accessible overview of this notoriously difficult subject. Clearly structured throughout, the authors develop sufficient theoretical foundation to understand the basis for applying various analytical methods to a time series and show clearly how to interpret the results. Taking a unique approach to the subject, the authors use a combination of theory and application to real data sets to enhance student understanding...

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Time Series Analysis in Meteorology and Climatology: An Introduction

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Overview

Time Series Analysis in Meteorology and Climatology provides an accessible overview of this notoriously difficult subject. Clearly structured throughout, the authors develop sufficient theoretical foundation to understand the basis for applying various analytical methods to a time series and show clearly how to interpret the results. Taking a unique approach to the subject, the authors use a combination of theory and application to real data sets to enhance student understanding throughout the book.

This book is written for those students that have a data set in the form of a time series and are confronted with the problem of how to analyse this data. Each chapter covers the various methods that can be used to carry out this analysis with coverage of the necessary theory and its application. In the theoretical section topics covered include; the mathematical origin of spectrum windows, leakage of variance and understanding spectrum windows. The applications section includes real data sets for students to analyse. Scalar variables are used for ease of understanding for example air temperatures, wind speed and precipitation.

Students are encouraged to write their own computer programmes and data sets are provided to enable them to recognize quickly whether their programme is working correctly- one data set is provided with artificial data and the other with real data where the students are required to physically interpret the results of their periodgram analysis.

Based on the acclaimed and long standing course at the University of Oklahoma and part of the RMetS Advancing Weather and Climate Science Series, this book is distinct in its approach to the subject matter in that it is written specifically for readers in meteorology and climatology and uses a mix of theory and application to real data sets.

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Editorial Reviews

From the Publisher
“In summary, I unequivocally endorse this book as a valuable contribution to the literature of time series analysis in the geosciences. It is clear and includes examples that make it accessible for students; knowledgeable practitioners will also gain new insights from this book.” (Bulletin of the American Meteorological Society,
1 September 2012)
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Product Details

Table of Contents

Series foreword vii

Preface ix

1. Fourier analysis 1

1.1 Overview and terminology 2

1.2 Analysis and synthesis 6

1.3 Example data sets 14

1.4 Statistical properties of the periodogram 23

1.5 Further important topics in Fourier analysis 47

Appendix 1.A Subroutine foranx 83

Appendix 1.B Sum of complex exponentials 86

Appendix 1.C Distribution of harmonic variances 86

Appendix 1.D Derivation of Equation 1.42 92

Problems 93

References 99

2. Linear systems 101

2.1 Input–output relationships 102

2.2 Evaluation of the convolution integral 104

2.3 Fourier transforms for analog data 110

2.4 The delta function 113

2.5 Special input functions 118

2.6 The frequency response function 122

2.7 Fourier transform of the convolution integral 128

2.8 Linear systems in series 130

2.9 Ideal interpolation formula 132

Problems 137

References 142

3. Filtering data 143

3.1 Recursive and nonrecursive filtering 144

3.2 Commonly used digital nonrecursive filters 150

3.3 Filter design 159

3.4 Lanczos filtering 161

Appendix 3.A Convolution of two running mean filters 173

Appendix 3.B Derivation of Equation 3.20 176

Appendix 3.C Subroutine sigma 177

Problems 180

References 182

4. Autocorrelation 183

4.1 Definition and properties 184

4.2 Formulas for the acvf and acf 188

4.3 The acvf and acf for stationary digital processes 192

4.4 The acvf and acf for selected processes 195

4.5 Statistical formulas 201

4.6 Confidence limits for the population mean 206

4.7 Variance of the acvf and acf estimators 211

Appendix 4.A Generating a normal random variable 215

Problems 216

References 221

5. Lagged-product spectrum analysis 223

5.1 The variance density spectrum 223

5.2 Relationship between the variance density spectrum and the acvf 226

5.3 Spectra of random processes 230

5.4 Spectra of selected processes 232

5.5 Smoothing the spectrum 236

Appendix 5.A Proof of Equation 5.11 239

Appendix 5.B Proof of Equation 5.12 240

Problems 241

References 243

Index 245

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