Probability, Statistics, And Decision Making In The Atmospheric Sciences

This book presents surveys of relatively new developments in probability and statistics of potential interest to atmospheric scientists. It introduces decision analysis, a formal method for analyzing and modeling decision-making problems, and examines certain aspects of weather forecasting.

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.

Autorentext

Allan H. Murphy and Richard W. Katz

Inhalt

Preface. 1. Exploratory Analysis of Atmospheric Data 2. Developing Empirical Models with Multiple Regression: Biased Estimation Techniques 3. Exploratory Multivariate Analysis of a Single Batch of Data 4. Multivariate Comparisons of Data from Several Batches 5. Time Series Analysis -Time Domain 6. Probabalistic Models 7. Statistical Weather Forecasting 8. Probabalistic Weather Forecasting 10. Forecast Evalution 11. Design and Evaluation of Weather Modification Experiments 12. Bayesian Inference 13. Decision Analysis. Index.