## 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. Accessible presentation and explanation of techniques for atmospheric data summarization, analysis, testing and forecasting Many worked examples End-of-chapter exercises, with answers provided |

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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

Univariate Statistics | 21 |

Multivariate Statistics | 457 |

Example Data Sets | 617 |

Probability Tables | 619 |

Answers to Exercises | 627 |

References | 635 |

### Common terms and phrases

Â Ã algorithm analysis approximately atmospheric autocorrelation autoregressive average beta distribution binomial bootstrap calculated Canandaigua canonical climatological clustering coefficient computed confidence intervals contingency table correlation corresponding covariance matrix cumulative probability curve data in Table data series data set data values data vectors defined density diagram distance eigenvalues eigenvectors elements ensemble forecasts ensemble members Equa Equation errors estimated evaluated event example exhibit Figure forecasts and observations function gamma distribution Gaussian distribution gridpoints groups histogram independent indicates Ithaca joint distribution likelihood locations Mahalanobis distance Markov chain maximum methods minimum temperature multivariate null hypothesis pairs parameters plot points posterior distribution precipitation predictand prediction predictors principal components prior distribution probability distribution probability forecasts quantiles random regression relative frequency represent residuals result sample mean sampling distribution scalar scatterplot Section simulated skill score squared standard deviation test statistic tion transformation uncertainty variables variance variations verification yields zero