Statistical Methods in the Atmospheric Sciences

Elsevier (Verlag)
  • 4. Auflage
  • |
  • erschienen am 11. Juni 2019
  • Buch
  • |
  • Softcover
  • |
  • 840 Seiten
978-0-12-815823-4 (ISBN)
Statistical Methods in the Atmospheric Sciences, Fourth Edition, continues the tradition of trying to meet the needs of students, researchers and operational practitioners. This updated edition not only includes expanded sections built upon the strengths of the prior edition, but also provides new content where there have been advances in the field, including Bayesian analysis, forecast verification and a new chapter dedicated to ensemble forecasting.

- Provides a strong, yet concise, introduction to applied statistics that is specific to atmospheric science
- Contains revised and expanded sections on nonparametric tests, test multiplicity and quality uncertainty descriptors
- Includes new sections on ANOVA, quantile regression, the lasso and other regularization methods, regression trees, changepoint detection, ensemble forecasting and exponential smoothing
  • Englisch
  • USA
  • Für höhere Schule und Studium
  • |
  • Researchers and students in the atmospheric sciences, including meteorology, climatology, and other allied disciplines involving atmospheric data

  • Höhe: 235 mm
  • |
  • Breite: 191 mm
  • 1720 gr
978-0-12-815823-4 (9780128158234)
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Daniel S. Wilks has been a member of the Atmospheric Sciences faculty at Cornell University since 1987, and is the author of Statistical Methods in the Atmospheric Sciences (2011, Academic Press), which is in its third edition and has been continuously in print since 1995. Research areas include statistical forecasting, forecast postprocessing, and forecast evaluation.
1. Introduction 2. Review of Probability 3. Empirical Distributions and Exploratory Data Analysis 4. Parametric Probability Distributions 5. Frequentist Statistical Inference 6. Bayesian Inference 7. Statistical Forecasting 8. Ensemble Forecasting 9. Forecast Verification 10. Time Series 11. Matrix Algebra and Random Matrices 12. Multivariate Normal Distribution 13. Principal Component (EOF) Analysis 14. Linear multivariate analysis of vector pairs: CCA, MCA, and RA 15. Discrimination and Classification 16. Cluster Analysis

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