A Guide To Empirical Orthogonal Functions For Climate Data Analysis
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A Guide to Empirical Orthogonal Functions for Climate Data Analysis
Author: Antonio Navarra
language: en
Publisher: Springer Science & Business Media
Release Date: 2010-04-05
Climatology and meteorology have basically been a descriptive science until it became possible to use numerical models, but it is crucial to the success of the strategy that the model must be a good representation of the real climate system of the Earth. Models are required to reproduce not only the mean properties of climate, but also its variability and the strong spatial relations between climate variability in geographically diverse regions. Quantitative techniques were developed to explore the climate variability and its relations between different geographical locations. Methods were borrowed from descriptive statistics, where they were developed to analyze variance of related observations-variable pairs, or to identify unknown relations between variables. A Guide to Empirical Orthogonal Functions for Climate Data Analysis uses a different approach, trying to introduce the reader to a practical application of the methods, including data sets from climate simulations and MATLAB codes for the algorithms. All pictures and examples used in the book may be reproduced by using the data sets and the routines available in the book . Though the main thrust of the book is for climatological examples, the treatment is sufficiently general that the discussion is also useful for students and practitioners in other fields. Supplementary datasets are available via http://extra.springer.com
Statistical Methods in the Atmospheric Sciences
Statistical Methods in the Atmospheric Sciences, Fourth Edition, continues the tradition of trying to meet the needs of students, researchers and operational practitioners. This updated edition not only includes expanded sections built upon the strengths of the prior edition, but also provides new content where there have been advances in the field, including Bayesian analysis, forecast verification and a new chapter dedicated to ensemble forecasting. - Provides a strong, yet concise, introduction to applied statistics that is specific to atmospheric science - Contains revised and expanded sections on nonparametric tests, test multiplicity and quality uncertainty descriptors - Includes new sections on ANOVA, quantile regression, the lasso and other regularization methods, regression trees, changepoint detection, ensemble forecasting and exponential smoothing
Time Series Data Analysis in Oceanography
Author: Chunyan Li
language: en
Publisher: Cambridge University Press
Release Date: 2022-05-05
Textbook for students and researchers in oceanography and Earth science on theory and practice of time series analysis using MATLAB.