시공간 자료분석법인 Cyclostationary Empirical Orthogonal Function (CSEOF) 기법은 저자가 20여 년 전 개발한 이래 그 정교함을 인정받고 있는 차세대 데이터 분석 도구로, 지구과학 및 공학을 포함한 다양한 분야의 연구에 적용할 수 있는 이상적인 도구이다. CSEOF 기법의 이론과 개념 및 CSEOF Space에서의 회귀분석을 다룬 이 책은, 종이책의 틀을 벗어나 부록으로 분석 데이터 재생이 가능한 전자책(본책 및 Exercise Book), 실제 분석을 위한 각종 도구들(programs, scripts, datasets)을 DVD에 담아 제공하여 학습자가 더욱 심도 있는 이해를 할 수 있도록 돕고 있다.
This book discusses the concept and applications of cyclostationary EOF (CSEOF) analysis developed by the author. The concept of this innovative space-time analysis technique is fully expounded in comparison with the conventional data analysis techniques. Many scientific examples included in the textbook demonstrate the importance and benefit of analyzing space-time evolutions in order to fully understand physical processes in a physical system. Various applications of scientific interests are developed and implemented based on the CSEOF techniques in the book; these applications are new and original. The book contains more than 100 exercise problems, pedagogical as well as research examples, all of which are fully solved in a separate exercise eBook. This is an advanced book on statistical climatology and an excellent self-teaching tool, which is intended for graduate students and researchers in earth sciences and statistics.
In addition to the hard-copy textbook (448 pp.), this book contains 5 DVDs: (1) window version of eBooks for textbook and exercise book (604 pp.), (2) Macintosh version of eBooks for textbook and exercise book, (3) CSEOF package (written in Fortran, Matlab, and IDL), utility programs, and univariate time series analysis package, (4) datasets for exercise problems, and (5) script files for all the exercise problems. Movies play inside the eBooks and can be played in their original sizes on separate windows. The eBook DVDs contain original movies and PDFs. The script files are detailed enough to follow all the exercise problems.

Preface

Chapter 1. Univariate Time Series Analysis
1.1 Introduction
1.2 Covariance Stationary Time Series
1.3 Sampling Properties of Descriptive Statistics
1.4 Theory of Linear Filters
1.5 Nonparametric Spectral Density Estimation
1.6 Time Series Models
1.7 Glossary of Statistics Terms
1.8 Time Series Analysis Package
Exercise Problems for Chapter 1

Chapter 2. Empirical Orthogonal Function Analysis
2.1 Introduction
2.2 Properties of EOFs
2.3 EOF Decomposition
2.4 Covariance Function in Spectral Domain
2.5 Physical Interpretation of EOFs
2.6 Composite Analysis
2.7 EOF Analysis in a Time Domain
Exercise Problems for Chapter 2

Chapter 3. Cyclostationary EOF Analysis
3.1 Introduction
3.2 Cyclostationary Covariance Statistics
3.3 Cyclostationary EOF Analysis
3.4 Properties of CSEOFs
3.5 Physical Consistency of CSLVs
3.6 CSEOF Analysis in EOF Space
3.7 Comparison with Composite Analysis
3.8 One-Dimensional CSEOF Analysis
3.9 Theory of CSEOF Analysis (Advanced Topic)
3.10 CSEOF Analysis Package
Exercise Problems for Chapter 3

Chapter 4. Physical Inferences Based on CSEOF Analysis
4.1 Introduction
4.2 Physical Interpretation of CSEOFs
4.3 Regression Analysis in CSEOF Space
4.4 Comparison with Composite Analysis
4.5 Comparison with Wavelet Analysis
Exercise Problems for Chapter 4

Chapter 5. Connection between Two Physical Regimes
5.1 Introduction
5.2. Teleconnection Analysis
5.3 Connecting Local and Regional/Global Analysis
5.4 Connecting Temporally Lagged Signals
5.5 Connecting Signals with Distinct Evolution Patterns
5.6 Extraction of Local and Regional Anthropogenic Signals
Exercise Problems for Chapter 5

Chapter 6. Prediction Based on CSEOFs
6.1 Introduction
6.2 Weather Generator
6.3 Prediction
6.4 Forecasting
6.5 Statistical Downscaling
6.6 Super-Ensemble Technique Exercise Problems for Chapter 6

Chapter 7. Estimation Based on CSEOFs
7.1 Introduction
7.2 Estimation of Spatial Averages
7.3 Estimating the Amplitudes of Basis Functions
7.4 Estimation of CSEOF PC Time Series
7.5 Data Initialization
7.6 Estimating Global Warming-Related Signal Exercise Problems for Chapter 7

Chapter 8. Physical Analysis Based on CSEOFs
8.1 Introduction
8.2 Analysis of Aerosol Data
8.3 Defining Background Atmosphere
8.4 Analysis of PM10 in Seoul
8.5 Analysis of Oxygen Isotope (d18O) Time Series
8.6 Weekend Effect Exercise Problems for Chapter 8

Chapter 9. Application of CSEOFs in Numerical Modeling
9.1 Introduction
9.2 Stringent Analysis of Model Datasets
9.3 Ensemble Forecasting
9.4 Open Boundary Conditions for Regional Models
9.5 Subscale Parameterization in Climate Models
9.6 Optimal Interpolation
Exercise Problems for Chapter 9

Chapter 10. Other Eigen-Analysis Techniques
10.1 Introduction
10.2 Extended EOF Analysis
10.3 Complex EOF Analysis
10.4 Principal Oscillation Pattern Analysis
10.5 Canonical Correlation Analysis
10.6 Maximum Covariance Analysis
Exercise Problems for Chapter 10

Appendix
A.1 Univariate Time Series Analysis Package
A.2 CSEOF Analysis Processing Sequence Chart
A.3 Program Descriptions
A.4 Example Script Files
A.5 IDL Version of the CSEOF Package
A.6 MATLAB Version of the CSEOF Package
A.7 Other Eigen-Analysis Programs
A.8 System Requirements
A.9 Setting Up for Execution

References
Index

DVD Contents
DVD1: Electronic Book (eBook for Mac OS)
DVD2: Electronic Book (eBook for Windows)
DVD3: Source Programs (programs)
DVD4: Datasets for Exercise Problems (datasets)
DVD5: Script Files for Exercise Programs (scripts)