Book Details

Statistical Methods in the Atmospheric Sciences provides a thorough and structured exploration of the statistical techniques essential for analyzing atmospheric data, and forecasting atmospheric phenomena. 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 the tools for rigorous data analysis and interpretation. The text also addresses statistical forecasting and ensemble forecasting, which are crucial for predicting atmospheric phenomena, along with methods for verifying forecast accuracy. Time series analysis is explored in detail, enabling the 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 datasets, probability tables, and a glossary of symbols and acronyms, along with answers to exercises that reinforce learning. This comprehensive new edition equips researchers, students, and professionals with the statistical knowledge and practical skills necessary to analyze atmospheric data effectively and to contribute to the advancements in meteorology and climate science.

Key Features

• Facilitates understanding and the use of applied statistical methods through the rigorous yet conversational treatment of applied statistics
• Offers comprehensive treatments of statistical approaches to forecasting, ensemble forecasting, and forecast evaluation
• Allows the readers to see the operation of various methods in an accessible and transparent way using small datasets