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Statistical Methods for Climate Scientists
◆JpGU2025 セール開催中！：2025年6月30日(月)ご注文分まで
※上記表示の販売価格は割引適用後の価格です 出版済み 3-5週間でお届けいたします。 Author: DelSole, Timothy (George Mason University, Virginia) / Tippett, Michael (Columbia University, New York) Publisher: Cambridge University Press ISBN: 9781108472418 Cover: HARDCOVER Date: 2022年02月 こちらの商品は学校・法人様向け（機関契約）のオンラインブック版がございます。 オンラインブックの価格、納期につきましては弊社営業員または当ECサイトよりお問い合わせください。  DESCRIPTION A comprehensive introduction to the most commonly used statistical methods relevant in atmospheric, oceanic and climate sciences. Each method is described step-by-step using plain language, and illustrated with concrete examples, with relevant statistical and scientific concepts explained as needed. Particular attention is paid to nuances and pitfalls, with sufficient detail to enable the reader to write relevant code. Topics covered include hypothesis testing, time series analysis, linear regression, data assimilation, extreme value analysis, Principal Component Analysis, Canonical Correlation Analysis, Predictable Component Analysis, and Covariance Discriminant Analysis. The specific statistical challenges that arise in climate applications are also discussed, including model selection problems associated with Canonical Correlation Analysis, Predictable Component Analysis, and Covariance Discriminant Analysis. Requiring no previous background in statistics, this is a highly accessible textbook and reference for students and early-career researchers in the climate sciences. *A short description of a statistical problem and illustrative example are provided at the start of each chapter, allowing the reader to decide if the technique in the chapter is relevant to a particular problem *Specific statistical challenges that arise in climate applications are addressed, making it highly relevant for climate courses *Each method is described clearly and thoroughly using plain language TABLE OF CONTENTS 1. Basic Concepts in Probability and Statistics 2. Hypothesis Tests 3. Confidence Intervals 4. Statistical Tests Based on Ranks 5. Introduction to Stochastic Processes 6. The Power Spectrum 7. Introduction to Multivariate Methods 8. Linear Regression: Least Squares Estimation 9. Linear Regression: Inference 10. Model Selection 11. Screening: A Pitfall in Statistics 12. Principal Component Analysis 13. Field Significance 14. Multivariate Linear Regression 15. Canonical Correlation Analysis 16. Covariance Discriminant Analysis 17. Analysis of Variance and Predictability 18. Predictable Component Analysis 19. Extreme Value Theory 20. Data Assimilation 21. Ensemble Square Root Filters 22. Appendix References Index
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