Complexity, nonlinearity and high-dimensionality constitute the main features of the weather and climate. Advances in computer power and observing systems have led to the generation and accumulation of large-scale weather and climate data, which beg for exploration and analysis.

The book presents, from different perspectives, most available, novel and conventional, approaches used to analyse multivariate time series in atmospheric and oceanographic science to identify patterns of variability, teleconnections, and reduce dimensionality.

The book discusses in detail linear and nonlinear methods to identify stationary and propagating patterns of spatio-temporal, single and combined fields. The book also presents machine learning with a particular focus on the main methods used in climate science.

About the author
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.

Read more about the book on Springer Link’s website.