## Statistical Methods in the Atmospheric SciencesPraise 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 * Features numerous worked examples and exercises * Covers Model Output Statistic (MOS) with an introduction to the Kalman filter, an approach that tolerates frequent model changes * Includes a detailed section on forecast verification New in this Edition:* Expanded treatment of resampling tests and coverage of key analysis techniques * Updated treatment of ensemble forecasting * Edits and revisions throughout the text plus updated references |

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#### LibraryThing Review

User Review - co_coyote - LibraryThingOne of those rare books, especially among statistics books, in which nearly everything is clearly explained and relevant to your work. This is one book on my shelf that I could not possibly do without. Read full review

### Contents

21 | |

Multivariate Statistics | 401 |

APPENDIX A Example Data Sets | 565 |

APPENDIX B Probability Tables | 569 |

APPENDIX C Answers to Exercises | 579 |

587 | |

611 | |

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algorithm analysis anomalies approximately atmospheric autocorrelation autoregressive average bivariate calculated Canandaigua canonical climatological clustering coefficient computed conditional probabilities confidence intervals contingency table corresponding covariance matrix cumulative probabilities curve data in Table data series data set data values data vectors defined density diagonal dimensions discriminant distance eigenvalues eigenvectors elements ensemble forecasts ensemble members estimated event example exhibit Figure forecasts and observations function gamma distribution Gaussian distribution gridpoints groups histogram illustrates independent indicates Ithaca January joint distribution linear combinations linear regression locations Mahalanobis distance Markov chain maximum minimum temperature multivariate nonprobabilistic null distribution null hypothesis NWP model occur pairs parameters plot precipitation predictand principal components probability distribution probability forecasts Q-Q plot quantiles ratio relative frequency represent resampling residuals result sample mean sampling distribution scalar scatterplot Section skill score spectrum squared standard deviation Table A.1 temperature data test statistic transformation variance variations verification yields zero