This revised and expanded text explains the latest statistical methods that are being used to describe, analyze, test, and forecast atmospheric data. It features numerous worked examples, illustrations, equations, and exercises with separate solutions. The book will help advanced students and professionals understand and communicate what their data sets have to say, and make sense of the scientific literature in meteorology, climatology, and related disciplines.
This book introduces and explains the statistical methods used to describe, analyze, test, and forecast atmospheric data. It will be useful to students, scientists, and other professionals who seek to make sense of the scientific literature in meteorology, climatology, or other geophysical disciplines, or to understand and communicate what their atmospheric data sets have to say. The book includes chapters on exploratory data analysis, probability distributions, hypothesis testing, statistical weather forecasting, forecast verification, time(series analysis, and multivariate data analysis. Worked examples, exercises, and illustrations facilitate understanding of the material; an extensive and up-to-date list of references allows the reader to pursue selected topics in greater depth. Key Features * Presents and explains techniques used in atmospheric data summarization, analysis, testing, and forecasting * Includes extensive and up-to-date references * Features numerous worked examples and exercises * Contains over 130 illustrations
Statistical Methods in the Atmospheric Sciences, Second Edition, explains the latest statistical methods used to describe, analyze, test, and forecast atmospheric data. This revised and expanded text is intended to help students understand and communicate what their data sets have to say, or to make sense of the scientific literature in meteorology, climatology, and related disciplines. In this new edition, what was a single chapter on multivariate statistics has been expanded to a full six chapters on this important topic. Other chapters have also been revised and cover exploratory data analysis, probability distributions, hypothesis testing, statistical weather forecasting, forecast verification, and time series analysis. There is now an expanded treatment of resampling tests and key analysis techniques, an updated discussion on ensemble forecasting, and a detailed chapter on forecast verification. In addition, the book includes new sections on maximum likelihood and on statistical simulation and contains current references to original research. Students will benefit from pedagogical features including worked examples, end-of-chapter exercises with separate solutions, and numerous illustrations and equations. This book will be of interest to researchers and students in the atmospheric sciences, including meteorology, climatology, and other geophysical disciplines.
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Methodology drawn from the fields of probability. statistics and decision making plays an increasingly important role in the atmosphericsciences. both in basic and applied research and in experimental and operational studies. Applications of such methodology can be found in almost every facet of the discipline. from the most theoretical and global (e.g., atmospheric predictability. global climate modeling) to the most practical and local (e.g., crop-weather modeling forecast evaluation). Almost every issue of the multitude of journals published by the atmospheric sciences community now contain some or more papers involving applications of concepts and/or methodology from the fields of probability and statistics. Despite the increasingly pervasive nature of such applications. very few book length treatments of probabilistic and statistical topics of particular interest to atmospheric scientists have appeared (especially inEnglish) since the publication of the pioneering works of Brooks andCarruthers (Handbook of Statistical Methods in Meteorology) in 1953 and Panofsky and Brier-(some Applications of)statistics to Meteor) in 1958. As a result. many relatively recent developments in probability and statistics are not well known to atmospheric scientists and recent work in active areas of meteorological research involving significant applications of probabilistic and statistical methods are not familiar to the meteorological community as a whole.
Studies of local and global phenomena generate descriptions which require statistical analysis. In this text, H. Jean Thiebaux presents a succinct yet comprehensive review of the fundamentals of statistics as they pertain to studies in oceanic and atmospheric sciences. The text includes an accompanying disk with compatible Minitab sample data. Together, this volume and the included data provide insights into the basics of statistical inference, data analysis, and distributional models of variability. Oceanographers, meteorologists, marine biologists, and other environmental scientists will find this book of great value as a statistical tool for their continuing studies. Key Features * Specifically designed for students of the ocean and atmospheric sciences * Contains a disk containing files of real ocean and atmospheric data, in universal ASCII format, on which many of the exercises are based * Provides succinct yet comprehensive coverage * Designed to teach students statistical methods with the scientific realism of computer analysis and statistical inference
The need to understand and quantify change is fundamental throughout the environmental sciences. This might involve describing past variation, understanding the mechanisms underlying observed changes, making projections of possible future change, or monitoring the effect of intervening in some environmental system. This book provides an overview of modern statistical techniques that may be relevant in problems of this nature. Practitioners studying environmental change will be familiar with many classical statistical procedures for the detection and estimation of trends. However, the ever increasing capacity to collect and process vast amounts of environmental information has led to growing awareness that such procedures are limited in the insights that they can deliver. At the same time, significant developments in statistical methodology have often been widely dispersed in the statistical literature and have therefore received limited exposure in the environmental science community. This book aims to provide a thorough but accessible review of these developments. It is split into two parts: the first provides an introduction to this area and the second part presents a collection of case studies illustrating the practical application of modern statistical approaches to the analysis of trends in real studies. Key Features: Presents a thorough introduction to the practical application and methodology of trend analysis in environmental science. Explores non-parametric estimation and testing as well as parametric techniques. Methods are illustrated using case studies from a variety of environmental application areas. Looks at trends in all aspects of a process including mean, percentiles and extremes. Supported by an accompanying website featuring datasets and R code. The book is designed to be accessible to readers with some basic statistical training, but also contains sufficient detail to serve as a reference for practising statisticians. It will therefore be of use to postgraduate students and researchers both in the environmental sciences and in statistics.
Statistical Mining and Data Visualization in Atmospheric Sciences brings together in one place important contributions and up-to-date research results in this fast moving area. Statistical Mining and Data Visualization in Atmospheric Sciences serves as an excellent reference, providing insight into some of the most challenging research issues in the field.
Climatology is, to a large degree, the study of the statistics of our climate. The powerful tools of mathematical statistics therefore find wide application in climatological research. The purpose of this book is to help the climatologist understand the basic precepts of the statistician's art and to provide some of the background needed to apply statistical methodology correctly and usefully. The book is self contained: introductory material, standard advanced techniques, and the specialised techniques used specifically by climatologists are all contained within this one source. There are a wealth of real-world examples drawn from the climate literature to demonstrate the need, power and pitfalls of statistical analysis in climate research. Suitable for graduate courses on statistics for climatic, atmospheric and oceanic science, this book will also be valuable as a reference source for researchers in climatology, meteorology, atmospheric science, and oceanography.
"Presenting a concise, well-rounded focus on the statistical concepts and applications that are essential for understanding gathered data in the earth and atmospheric sciences. Containing contributions from over 100 leading academics, researchers, and practitioners in these fields, this comprehensive book unveils modern, approaches to the acquisition and analysis of data across diverse subject areas like geology, agriculture, animal science, and geophysics. It discusses techniques related to survey methodology, computational statistics, and operations research and covers new areas of interest like non-linear weather forecasting, construction geological surveys, and water pollution assessment"--
Methodology drawn from the fields of probability. statistics and decision making plays an increasingly important role in the atmosphericsciences. both in basic and applied research and in experimental and operational studies. Applications of such methodology can be found in almost every facet of the discipline. from the most theoretical and global (e.g., atmospheric predictability. global climate modeling) to the most practical and local (e.g., crop-weather modeling forecast evaluation). Almost every issue of the multitude of journals published by the atmospheric sciences community now contain some or more papers involving applications of concepts and/or methodology from the fields of probability and statistics. Despite the increasingly pervasive nature of such applications. very few book length treatments of probabilistic and statistical topics of particular interest to atmospheric scientists have appeared (especially inEnglish) since the publication of the pioneering works of Brooks andCarruthers (Handbook of Statistical Methods in Meteorology) in 1953 and Panofsky and Brier-(some Applications of)statistics to Meteor) in 1958. As a result. many relatively recent developments in probability and statistics are not well known to atmospheric scientists and recent work in active areas of meteorological research involving significant applications of probabilistic and statistical methods are not familiar to the meteorological community as a whole.
Statistical Postprocessing of Ensemble Forecasts brings together chapters contributed by international subject-matter experts describing the current state of the art in the statistical postprocessing of ensemble forecasts. The book illustrates the use of these methods in several important applications including weather, hydrological and climate forecasts, and renewable energy forecasting. After an introductory section on ensemble forecasts and prediction systems, the second section of the book is devoted to exposition of the methods available for statistical postprocessing of ensemble forecasts: univariate and multivariate ensemble postprocessing are first reviewed by Wilks (Chapters 3), then Schefzik and Möller (Chapter 4), and the more specialized perspective necessary for postprocessing forecasts for extremes is presented by Friederichs, Wahl, and Buschow (Chapter 5). The second section concludes with a discussion of forecast verification methods devised specifically for evaluation of ensemble forecasts (Chapter 6 by Thorarinsdottir and Schuhen). The third section of this book is devoted to applications of ensemble postprocessing. Practical aspects of ensemble postprocessing are first detailed in Chapter 7 (Hamill), including an extended and illustrative case study. Chapters 8 (Hemri), 9 (Pinson and Messner), and 10 (Van Schaeybroeck and Vannitsem) discuss ensemble postprocessing specifically for hydrological applications, postprocessing in support of renewable energy applications, and postprocessing of long-range forecasts from months to decades. Finally, Chapter 11 (Messner) provides a guide to the ensemble-postprocessing software available in the R programming language, which should greatly help readers implement many of the ideas presented in this book. Edited by three experts with strong and complementary expertise in statistical postprocessing of ensemble forecasts, this book assesses the new and rapidly developing field of ensemble forecast postprocessing as an extension of the use of statistical corrections to traditional deterministic forecasts. Statistical Postprocessing of Ensemble Forecasts is an essential resource for researchers, operational practitioners, and students in weather, seasonal, and climate forecasting, as well as users of such forecasts in fields involving renewable energy, conventional energy, hydrology, environmental engineering, and agriculture. Consolidates, for the first time, the methodologies and applications of ensemble forecasts in one succinct place Provides real-world examples of methods used to formulate forecasts Presents the tools needed to make the best use of multiple model forecasts in a timely and efficient manner