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Statistical Methods in the Atmospheric Sciences
Abstract
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. This book 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. It includes a detailed section on forecast verification, including statistical inference, diagrams, and other methods. It provides an expanded treatment of resampling tests within nonparametric tests. It offers an updated treatment of ensemble forecasting. It provides expanded coverage of key analysis techniques, such as principle component analysis, canonical correlation analysis, discriminant analysis, and cluster analysis. It includes careful updates and edits throughout, based on users' feedback.
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Citations
Cited by
Strictly Proper Scoring Rules, Prediction, and Estimation
Gneiting, T., & Raftery, A. E. (2007) · Journal of the American Statistical Association · doi:10.1198/016214506000001437 · Cited by 5,793
Atmospheric Modeling, Data Assimilation and Predictability
Kalnay, E. (2002) · doi:10.1017/cbo9780511802270 · Cited by 3,391
El Niño Modoki and its possible teleconnection
Ashok, K., Behera, S. K., Rao, S. A., Weng, H., & Yamagata, T. (2007) · Journal of Geophysical Research: Oceans · doi:10.1029/2006jc003798 · Cited by 2,721
Using Bayesian model averaging to calibrate forecast ensembles.
Raftery, A. E., Gneiting, T., Balabdaoui, F., & Polakowski, M. (2005) · Monthly Weather Review · doi:10.1175/mwr2906.1 · Cited by 1,915
Gneiting, T., Raftery, A. E., Westveld, A. H., & Goldman, T. (2005) · Monthly Weather Review · doi:10.1175/mwr2904.1 · Cited by 1,142
Metadata
- Authors
- Ramsey, P. H.
- Venue
- Technometrics
- Publisher
- Informa UK Limited
- Published
- 1996-11
- Volume
- 38
- Issue
- 4
- Pages
- 402-402
- ISSN
- 0040-1706 (ISSN-L)
- 1537-2723
- OpenAlex
- W2153529786
- Cited by
- 7,601
- References
- 0
- FWCI
- 32.20
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Statistical Methods in the Atmospheric Sciences. https://app.cochat.ai/papers/10.1080/00401706.1996.10484553
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- Ramsey, P. H. (1996). Statistical Methods in the Atmospheric Sciences. Technometrics. https://doi.org/10.1080/00401706.1996.10484553
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.