Nonlinear Time Series Analysis in the Geosciences: Applications in Climatology, Geodynamics and Solar-Terrestrial Physics

ISBN-13: 9783540789376 / Angielski / Twarda / 2008 / 390 str.

Donner, Reik V.

Opis

The enormous progress over the last decades in our understanding of the mechanisms behind the complex system Earth is to a large extent based on the availability of enlarged data sets and sophisticated methods for their analysis. Univariate as well as multivariate time series are a particular class of such data which are of special importance for studying the dynamical p- cesses in complex systems. Time series analysis theory and applications in geo- and astrophysics have always been mutually stimulating, starting with classical (linear) problems like the proper estimation of power spectra, which hasbeenputforwardbyUdnyYule(studyingthefeaturesofsunspotactivity) and, later, by John Tukey. In the second half of the 20th century, more and more evidence has been accumulated that most processes in nature are intrinsically non-linear and thus cannot be su?ciently studied by linear statistical methods. With mat- matical developments in the ?elds of dynamic system s theory, exempli?ed by Edward Lorenz s pioneering work, and fractal theory, starting with the early fractal concepts inferred by Harold Edwin Hurst from the analysis of geoph- ical time series, nonlinear methods became available for time seriesanalysis as well. Over the last decades, these methods have attracted an increasing int- est in various branches of the earth sciences. The world s leading associations of geoscientists, the American Geophysical Union (AGU) and the European Geosciences Union (EGU) have reacted to these trends with the formation of special nonlinear focus groups and topical sections, which are actively present at the corresponding annual assemblies."

Spis treści

Applications in Climatology and Atmospheric Sciences.- Subsampling Methodology for the Analysis of Nonlinear Atmospheric Time Series.- Global Patterns of Nonlinearity in Real and GCM-Simulated Atmospheric Data.- Prediction of Extreme Events.- Analysis of Geophysical Time Series Using Discrete Wavelet Transforms: An Overview.- Automatic Parameter Estimation in a Mesoscale Model Without Ensembles.- Towards Robust Nonlinear Multivariate Analysis by Neural Network Methods.- Complexity of Spatio-Temporal Correlations in Japanese Air Temperature Records.- Applications in Oceanography and Seismology.- Time Series Analysis of Sea-Level Records: Characterising Long-Term Variability.- Empirical Global Ocean Tide and Mean Sea Level Modeling Using Satellite Altimetry Data Case Study: A New Empirical Global Ocean Tide and Mean Sea Level Model Based on Jason-1 Satellite Altimetry Observations.- Fourier, Scattering, and Wavelet Transforms: Applications to Internal Gravity Waves with Comparisons to Linear Tidal Data.- Crustal Deformation Models and Time-Frequency Analysis of GPS Data from Deception Island Volcano (South Shetland Islands, Antarctica).- Describing Seismic Pattern Dynamics by Means of Ising Cellular Automata.- Applications in Solar-Terrestrial Physics.- Template Analysis of the Hide, Skeldon, Acheson Dynamo.- Methods to Detect Solitons in Geophysical Signals: The Case of the Derivative Nonlinear Schrödinger Equation.- Detecting Oscillations Hidden in Noise: Common Cycles in Atmospheric, Geomagnetic and Solar Data.- Phase Coherence Analysis of Decadal-Scale Sunspot Activity on Both Solar Hemispheres.

Blurb

This book presents recent developments in nonlinear time series which have been motivated by present day problems in geosciences. Modern methods of spatio-temporal data analysis, time-frequency analysis, dimension analysis, nonlinear correlation and synchronization analysis and other nonlinear concepts are used to study emerging questions in climatology, geophysics, solar-terrestrial physics and related scientific disciplines. This volume collects contributions of some of the world's leading experts in geoscientific time series analysis. The methods presented may help researchers as well as practitioners to significantly improve their understanding of the data.