Description
Statistical Methods in the Atmospheric Sciences, Fifth Edition provides a structured exploration of the statistical techniques essential for analyzing atmospheric data. The book begins with foundational concepts in probability, setting the stage for more advanced topics. It then covers univariate statistics, including empirical distributions, parametric probability models, and both frequentist and Bayesian inference methods, offering tools for rigorous data analysis and interpretation. The text also addresses statistical forecasting and ensemble forecasting, along with methods for verifying forecast accuracy. In addition, time series analysis is explored in detail, enabling readers to understand temporal dependencies in atmospheric data.The book advances into multivariate statistics, presenting matrix algebra and random matrices as mathematical foundations. It discusses the multivariate normal distribution, principal component analysis (EOF), and multivariate analysis of vector pairs to handle complex, multidimensional atmospheric datasets. Techniques for discrimination, classification, and cluster analysis are also examined, providing methods for categorizing and interpreting atmospheric patterns. Supplementary materials include example data sets, probability tables, and a glossary of symbols and acronyms, along with answers to exercises that reinforce learning.- Facilitates understanding and use of applied statistical methods through rigorous yet conversational treatment of applied statistics- Offers a unique, statistical approach to forecasting, ensemble forecasting, and forecast evaluation- Allows readers to see the operation of various methods in an accessible and transparent way using small datasets
Table of Contents
1. Introduction2. Review of Probability II Univariate Statistics3. Empirical Distributions and Exploratory Data Analysis4. Parametric Probability Distributions5. Frequentist Statistical Inference6. Bayesian Inference7. Statistical Forecasting8. Ensemble Forecasting9. Forecast Verification10. Time Series III Multivariate Statistics11. Matrix Algebra and Random Matrices12. The Multivariate Normal (MVN) Distribution13. Principal Component (EOF) Analysis14. Multivariate Analysis of Vector Pairs15. Discrimination and Classification16. Cluster AnalysisAppendixA. Example Data SetsB. Probability TablesC. Symbols and AcronymsD. Answers to Exercises
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人類学者としてのカール・マルクス
