Data Analysis Methods in Physical Oceanography

Fourth Edition

4th Edition - July 16, 2024
Authors: Richard E. Thomson, William J. Emery
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 9 1 7 2 3 - 0
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 9 9 3 1 3 - 5

Data Analysis Methods in Physical Oceanography: Fourth Edition provides a practical reference to established and modern data analysis techniques in earth and ocean sciences.… Read more

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Data Analysis Methods in Physical Oceanography: Fourth Edition provides a practical reference to established and modern data analysis techniques in earth and ocean sciences. In five sections, the book addresses data acquisition and recording, data processing and presentation, statistical methods and error handling, analysis of spatial data fields, and time series analysis methods. The updated edition includes new information on autonomous platforms and new analysis tools such as “deep learning” and convolutional neural networks. A section on extreme value statistics has been added, and the section on wavelet analysis has been expanded.

This book brings together relevant techniques and references recent papers where these techniques have been trialed. In addition, it presents valuable examples using physical oceanography data. For students, the sections on data acquisition are useful for a compilation of all the measurement methods.

Cover image
Title page
Table of Contents
Copyright
Dedication
Preface
Acknowledgments
Chapter 1. Data Acquisition and Recording
1.1. Introduction
1.2. Introduction to TEOS-10
1.3. Temperature
1.4. Salinity
1.5. Density
1.6. Depth or Pressure
1.7. Sea-Level Measurement
1.8. Eulerian Currents
1.9. Lagrangian Current Measurements
1.10. Subsurface Floats
1.11. Surface Displacements in Satellite Imagery
1.12. Autonomous Platforms and Underwater Vehicles
1.13. Wind Measurements
1.14. Precipitation
1.15. Chemical Tracers
1.16. Oceanographic Data Formats
Glossary
Chapter 2. Data Processing and Presentation
2.1. INTRODUCTION
2.2. BASIC SAMPLING REQUIREMENTS
2.3. CALIBRATION
2.4. INTERPOLATION
2.5. MAP PROJECTIONS
2.6. DATA PRESENTATION
GLOSSARY
Chapter 3. Statistical Methods and Error Handling
3.1. Introduction
3.2. Sample Distributions
3.3. Probability
3.4. Moments and Expected Values
3.5. Common PDFs
3.6. Central Limit Theorem
3.7. Estimation
3.8. Confidence Intervals
3.9. Selecting the Sample Size
3.10. Confidence Intervals for Altimeter-Bias Estimates
3.11. Estimation Methods
3.12. Linear Estimation (Regression)
3.13. Relationship Between Regression and Correlation
3.14. Hypothesis Testing
3.15. Effective Degrees of Freedom
3.16. Editing and Despiking: The Nature of Errors
3.17. Interpolation: Filling the Data Gaps
3.18. Covariance and the Covariance Matrix
3.19. The Bootstrap and Jackknife Methods
3.20. Extreme Value Analysis
Chapter 4. The Spatial Analyses of Data Fields
4.1. Introduction
4.2. Traditional Block and Area Averaging
4.3. Computer Contouring
4.4. Optimum Interpolation
4.5. Kriging
4.6. Empirical Orthogonal Functions
4.7. Extended Empirical Orthogonal Functions (EEOFs)
4.8. Cyclostationary Empirical Orthogonal Functions (CSEOFs)
4.9. Factor Analysis
4.10. Normal Mode Analysis
4.11. Self-Organizing Maps
4.12. Kalman Filters
4.13. Mixed Layer Depth Estimation
4.14. Inverse Methods
Glossary
Chapter 5. Time Series Analysis Methods
5.1. Introduction
5.2. Stochastic Processes and Stationarity
5.3. Correlation Functions
5.4. Fourier Analysis
5.5. Harmonic Analysis
5.6. Spectral Analysis
5.7. Spectral Analysis (Parametric Methods)
5.8. Cross-Spectral Analysis
5.9. Wavelet Analysis
5.10. Regime Shift Detection
5.11. Vector Regression
5.12. Fractals
Glossary
Chapter 6. Digital Filters
6.1. Introduction
6.2. Basic Concepts
6.3. Ideal Filters
6.4. Design of Oceanographic Filters
6.5. Running-Mean Filters
6.6. Godin-Type Filters
6.7. Lanczos-Window Cosine Filters
6.8. Butterworth Filters
6.9. Kaiser–Bessel Filters
6.10. Frequency-Domain (Transform) Filtering
Glossary
Chapter 7. Machine Learning Methods
7.1. Introduction
7.2. Maximum Likelihood Analyses
7.3. Decision Trees, Random Forests and Gradient Boosting
7.4. K-Means Clustering
7.5. Support Vector Machines
Glossary
Chapter 8. Neural Networks, Convolutional Neural Networks and Deep Learning
8.1. Introduction
8.2. Models
8.3. Supervised Learning
8.4. Classification
8.5. Unsupervised and Self-Supervised Learning
8.6. Fundamentals
8.7. The Path to Deep Learning
8.8. Linear Neural Networks
8.9. The Structure of More Complex Neural Nets
8.10. Feed Forward Networks
8.11. Gradient Descent
8.12. Convolutional Neural Networks
8.13. Prediction of Sea Level Variations with Artificial Neural Networks
8.14. Prediction of Basin-Scale Sea Surface Temperatures with Artificial Neural Networks
8.15. Neural Networks for Oil Spill Detection Using ERS-SAR Data
Glossary
Appendix A. Units in Physical Oceanography
Appendix B. Glossary of Statistical Terminology
Appendix C. Means, Variances and Moment-Generating Functions for Some Common Continuous Variables
Appendix D. Statistical Tables
Appendix E. Correlation Coefficients at the 5% and 1% Levels of Significance for Various Degrees of Freedom, ν
Appendix F. Approximations and Nondimensional Numbers in Physical Oceanography
Appendix G. Convolution
Appendix H. Optimal Interpolation MATLAB Live Scripts
References
Index

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