Statistical Methods in the Atmospheric Sciences, Volume 91, Second Edition (International Geophysics)
- ISBN 10: 0127519661
- ISBN 13: 9780127519661
- Author: Daniel S. Wilks
- Edition: 2
- Release: 2005-11-21
- Format: Hardcover (648 pages)
- List Price: $81.95
- Tags: Science, Earth Sciences, Geophysics, Outdoors & Nature, Environment, Weather
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Book Description: Praise for the First Edition:"I recommend this book, without hesitation, as either a reference or course text...Wilks' excellent book provides a thorough base in applied statistical methods for atmospheric sciences."--BAMS (Bulletin of the American Meteorological Society)Fundamentally, statistics is concerned with managing data and making inferences and forecasts in the face of uncertainty. It should not be surprising, therefore, that statistical methods have a key role to play in the atmospheric sciences. It is the uncertainty in atmospheric behavior that continues to move research forward and drive innovations in atmospheric modeling and prediction.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. Statistical Methods in the Atmospheric Sciences, Second Edition 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. * Presents and explains techniques used in atmospheric data summarization, analysis, testing, and forecasting* Chapters feature numerous worked examples and exercises* Model Output Statistic (MOS) includes an introduction to the Kalman filter, an approach that tolerates frequent model changes* Detailed section on forecast verification, including statistical inference, diagrams, and other methods New in this Edition:* Expanded treatment of resampling tests within nonparametric tests* Updated treatment of ensemble forecasting* Expanded coverage of key analysis techniques, such as principle component analysis, canonical correlation analysis, discriminant analysis, and cluster analysis* Careful updates and edits throughout, based on users' feedback
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