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Statistical Methods in the Atmospheric Sciences,9780123850225
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Statistical Methods in the Atmospheric Sciences

by
Edition:
3rd
ISBN13:

9780123850225

ISBN10:
0123850223
Format:
Hardcover
Pub. Date:
5/20/2011
Publisher(s):
Academic Pr
List Price: $105.00

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Summary

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

Table of Contents

Preliminaries
Introduction
Review of Probability
Univariate Statistics
Empirical Distributions and Exploratory Data Analysis
Parametric Probability Distributions
Frequentist Statistical Inference
Bayesian Inference
Statistical Forecasting
Forecast Verification
Time Series
Multivariate Statistics
Matrix Algebra and Random Matrices
The Multivariate Normal (MVN) Distribution
Principal Component (EOF) Analysis
Canonical Correlation Analysis (CCA)
Discrimination and Classification
Cluster Analysis
Example Data Sets
Probability Tables
Answers to Exercises
References
Index
Table of Contents provided by Publisher. All Rights Reserved.


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