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
Purchase options
Institutional subscription on ScienceDirect
Request a sales quoteData 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.
- Includes content co-authored by scientists from academia and industry, both of whom have more than 30 years of experience in oceanographic research and field work
- Provides boxed worked examples that address typical data analysis problems, including examples with computer code (e.g., python code, MATLAB code)
- Presents brief summaries at the end of the more difficult sections to help readers looking for foundational information
Oceanographers, atmospheric scientists, meteorologists, climatologists, and upper-undergraduate and graduate students studying statistical methods in oceanography, meteorology, and climatology, Engineers
- 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
- No. of pages: 890
- Language: English
- Edition: 4
- Published: July 16, 2024
- Imprint: Elsevier Science
- Paperback ISBN: 9780323917230
- eBook ISBN: 9780323993135
RT
Richard E. Thomson
Richard E. Thomson is a researcher in coastal and deep-sea physical oceanography within the Ocean Sciences Division. Coastal oceanographic processes on the continental shelf and slope including coastally trapped waves, upwelling and baroclinic instability; hydrothermal venting and the physics of buoyant plumes; linkage between circulation and zooplankton biomass aggregations at hydrothermal venting sites; analysis and modelling of landslide generated tsunamis; paleoclimate using tree ring records and sediment cores from coastal inlets and basins.
Affiliations and expertise
Senior Research Scientist and Head of the Ocean Dynamics and Processes Section, Institute of Ocean Sciences, Sidney, British Columbia, CanadaWE
William J. Emery
William (Bill) Emery worked as a professor in Aerospace Engineering Sciences at the University of Colorado from 1987, prior to which he worked in the University of British Columbia where he created a Satellite Oceanography education/research program. He has authored over 220-refereed publications and 4 textbooks in addition to having given 200 conference papers. He is a fellow of: the IEEE (2002), the American Meteorological Society (2010), the American Astronautical Society (2011) and the American Geophysical Union (2012). He was recently elected to the IEEE TAB Hall of Honor (2020). In 2022 he received the GRSS Fawaz Ulaby Distinguised Achievement Award.
Affiliations and expertise
Professor, Aerospace Engineering Sciences, University of Colorado, Boulder, USARead Data Analysis Methods in Physical Oceanography on ScienceDirect