Statistical Methods in the Atmospheric Sciences
Statistical Methods in the Atmospheric Sciences
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DOI：
10.1080/00401706.1996.10484553
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发表时间：
2005-12
期刊：
影响因子：
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通讯作者：
Philip H. Ramsey
中科院分区：
文献类型：
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作者：
Philip H. Ramsey
对第一版的赞扬：“我毫不犹豫地推荐这本书，作为参考书或教材......威尔克斯的优秀著作为大气科学的应用统计方法提供了全面的基础”——“BAMS”（“美国气象学会通报”）。从根本上说，统计学涉及管理数据并在面对不确定性时做出推论和预测。因此，统计方法在大气科学中发挥关键作用也就不足为奇了。大气行为的不确定性不断推动研究前进并推动大气建模和预测的创新。这份修订和扩展的文本解释了用于描述、分析、测试和预测大气数据的最新统计方法。它包含大量的示例、插图、方程和带有单独解决方案的练习。 《大气科学统计方法，第二版》将帮助高级学生和专业人士理解和交流他们的数据集的内容，并理解气象学、气候学和相关学科的科学文献。本书介绍并解释了大气数据汇总、分析、测试和预测中使用的技术。各章包含大量实例和练习。模型输出统计 (MOS) 包括对卡尔曼滤波器的介绍，这是一种可以容忍频繁模型更改的方法。它包括有关预测验证的详细部分，包括统计推断、图表和其他方法。它提供了非参数检验中重采样检验的扩展处理。它提供了集合预报的更新处理方法。它扩展了关键分析技术的覆盖范围，例如主成分分析、典型相关分析、判别分析和聚类分析。它包括根据用户反馈进行的仔细更新和编辑。
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.