Advances in computer power and observing systems has led to the generation and accumulation of large scale weather & climate data begging for exploration and analysis. Pattern Identification and Data Mining in Weather and Climate presents, from different perspectives, most available, novel and conventional, approaches used to analyze multivariate time series in climate science to identify patterns of variability, teleconnections, and reduce dimensionality. The book discusses different methods to identify patterns of spatiotemporal fields. The book also presents machine learning with a particular focus on the main methods used in climate science. Applications to atmospheric and oceanographic data are also presented and discussed in most chapters. To help guide students and beginners in the field of weather & climate data analysis, basic Matlab skeleton codes are given is some chapters, complemented with a list of software links toward the end of the text. A number of technical appendices are also provided, making the text particularly suitable for didactic purposes.

The topic of EOFs and associated pattern identification in space-time data sets has gone through an extraordinary fast development, both in terms of new insights and the breadth of applications. We welcome this text by Abdel Hannachi who not only has a deep insight in the field but has himself made several contributions to new developments in the last 15 years.

• Huug van den Dool, Climate Prediction Center, NCEP, College Park, MD, U.S.A.

Now that weather and climate science is producing ever larger and richer data sets, the topic of pattern extraction and interpretation has become an essential part. This book provides an up to date overview of the latest techniques and developments in this area.

• Maarten Ambaum, Department of Meteorology, University of Reading, U.K.

This nicely and expertly written book covers a lot of ground, ranging from classical linear pattern identification techniques to more modern machine learning, illustrated with examples from weather & climate science. It will be very valuable both as a tutorial for graduate and postgraduate students and as a reference text for researchers and practitioners in the field.

• Frank Kwasniok, College of Engineering, University of Exeter, U.K.

Abdelwaheb Hannachi is an Associate Professor in the Department of Meteorology of Stockholm University, MISU. He currently serves as editor in chief of Tellus A: Dynamic Meteorology and Oceanography. Abdel. teaches a number of undergraduate and postgraduate courses, including dynamic meteorology, statistical climatology, and numerical weather prediction and data assimilation, and boundary layer turbulence. His main research interests are large-scale dynamics, teleconnections, nonlinearity in weather and climate in addition to extremes and forecasting.

Advances in computer power and observing systems has led to the generation and accumulation of large scale weather & climate data begging for exploration and analysis. Pattern Identification and Data Mining in Weather and Climate presents, from different perspectives, most available, novel and conventional, approaches used to analyze multivariate time series in climate science to identify patterns of variability, teleconnections, and reduce dimensionality. The book discusses different methods to identify patterns of spatiotemporal fields. The book also presents machine learning with a particular focus on the main methods used in climate science. Applications to atmospheric and oceanographic data are also presented and discussed in most chapters. To help guide students and beginners in the field of weather & climate data analysis, basic Matlab skeleton codes are given is some chapters, complemented with a list of software links toward the end of the text. A number oftechnical appendices are also provided, making the text particularly suitable for didactic purposes.

The topic of EOFs and associated pattern identification in space-time data sets has gone through an extraordinary fast development, both in terms of new insights and the breadth of applications. We welcome this text by Abdel Hannachi who not only has a deep insight in the field but has himself made several contributions to new developments in the last 15 years.

• Huug van den Dool, Climate Prediction Center, NCEP, College Park, MD, U.S.A.

Now that weather and climate science is producing ever larger and richer data sets, the topic of pattern extraction and interpretation has become an essential part. This book provides an up to date overview of the latest techniques and developments in this area.

• Maarten Ambaum, Department of Meteorology, University of Reading, U.K.

This nicely and expertly written book covers a lot of ground, ranging from classical linear pattern identification techniques to more modern machine learning, illustrated with examples from weather & climate science. It will be very valuable both as a tutorial for graduate and postgraduate students and as a reference text for researchers and practitioners in the field.

• Frank Kwasniok, College of Engineering, University of Exeter, U.K.

Autorentext

Abdelwaheb Hannachi is an Associate Professor in the Department of Meteorology of Stockholm University, MISU. He currently serves as editor in chief of Tellus A: Dynamic Meteorology and Oceanography. Abdel. teaches a number of undergraduate and postgraduate courses, including dynamic meteorology, statistical climatology, and numerical weather prediction and data assimilation, and boundary layer turbulence. His main research interests are large-scale dynamics, teleconnections, nonlinearity in weather and climate in addition to extremes and forecasting.

Inhalt

Chapter 1 Introduction

Chapter 2 General Setting and Basic Terminology

Chapter 3 Empirical Orthogonal Functions

Chapter 4 Rotated and Simplified EOFs

Chapter 5 Complex/Hilbert EOFs

Chapter 6 Principal Oscillation Patterns and their extension

Chapter 7 Extended EOFs and SSA

Chapter 8 Persistent, Predictive, and Interpolated Patterns

Chapter 9 Principal Coordinates or Multidimensional Scaling

Chapter 10 Factor Analysis

Chapter 11 Projection Pursuit

Chapter 12 Independent Component Analysis

Chapter 13 Kernel EOFs Chapter 14 Functional and Regularised EOFs

Chapter 15 Methods for Coupled Patterns

Chapter 16 Further topics

Chapter 17 Machine Learning

Appendix A Smoothing Techniques

Appendix B Introduction to Probability and Random Variables

Appendix C Stationary Time Series Analysis

Appendix D Matrix Algebra and Matrix Function

Appendix E Optimisation Algorithms

Appendix F Hilbert Space

Appendix G Systems of Linear Ordinary Differential Equations

Appendix H Links for Software Resource Material

Index