Multivariate Time Series Analysis in Climate and Environmental Research
Description
This book offers comprehensive information on the theory, models and algorithms involved in state-of-the-art multivariate time series analysis and highlights several of the latest research advances in climate and environmental science. The main topics addressed include Multivariate Time-Frequency Analysis, Artificial Neural Networks, Stochastic Modeling and Optimization, Spectral Analysis, Global Climate Change, Regional Climate Change, Ecosystem and Carbon Cycle, Paleoclimate, and Strategies for Climate Change Mitigation.
The self-contained guide will be of great value to researchers and advanced students from a wide range of disciplines: those from Meteorology, Climatology, Oceanography, the Earth Sciences and Environmental Science will be introduced to various advanced tools for analyzing multivariate data, greatly facilitating their research, while those from Applied Mathematics, Statistics, Physics, and the Computer Sciences will learn how to use these multivariate time series analysis tools to approach climate and environmental topics.More details
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Person
Dr. Zhang holds a Ph.D. degree (2007) from the University of California at Davis (USA). He is currently a full Professor of Climate Change at Beijing Normal University (BNU) and the Associate Director of Polar Climate and Environment Laboratory at BNU. He has published more than 50 first-authored research articles and a first-authored monograph entitled "Mathematical and Physical Fundamentals of Climate Change". He has served in the editorial board of several ISI-JCR journals including International Journal of Global Warming, Journal of Cleaner Production, EURASIP Journal on Advances in Signal Processing (Springer) and Open Geosciences. In 2013 Dr. Zhang joined the AJGS as an Associate Editor responsible for evaluating submissions in the fields of Climate Change and Signal Processing.
Content
Multivariate Harmonic Analysis.- Multivariate Wavelets and Framelits.- Artificial Neural Networks.- Stochastic Modeling and Optimization.- Spectral Analysis.- Global Climate Change.- Regional Climate Change.- Ecosystem and Carbon Cycle.- Paleoclimate.- Strategies for climate change mitigation.