Beschreibung
This book covers the comprehensive range of theory, models and algorithms of state-of-the-art multivariate time series analysis and includes a lot of latest research achievements in climate and environmental science. It is a self-contained and accessible guide for researchers and advanced students on how to apply state-of-the-art multivariate time series analysis tools in recent climate and environment research. Main topics 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. This book will be of great value to researchers and advanced students in a wide range of disciplines: researchers and advanced students of Meteorology, Climatology, Oceanography, Earth sciences, Environmental Science can learn various advanced tools for analyzing multivariate data and then push their research ahead greatly. Researchers and advanced students on Applied Mathematics, Statistics, Physics, and Computer sciences can grasp how to use these multivariate time series analysis tools to research climate and environment topics.
Autorenporträt
Prof. Dr. Zhihua Zhang (Beijing Normal University, China) 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.