Statistical Methods for Climate Scientists
Statistical Methods for Climate Scientists
复制标题
气候科学家的统计方法
DOI：
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
M. Tippett
中科院分区：
文献类型：
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作者：
T. DelSole;M. Tippett
全面介绍大气、海洋和气候科学中最常用的统计方法。每一种方法都用简单的语言一步一步地描述，并用具体的例子说明，并根据需要解释相关的统计和科学概念。特别注意的是细微差别和陷阱，有足够的细节，使读者能够编写相关的代码。涵盖的主题包括假设检验，时间序列分析，线性回归，数据同化，极值分析，主成分分析，典型相关分析，可预测成分分析和协方差判别分析。还讨论了气候应用中出现的具体统计挑战，包括与典型相关分析，可预测成分分析和协方差判别分析相关的模型选择问题。不需要以前的统计学背景，这是一个高度可访问的教科书和参考，为学生和早期职业研究人员在气候科学。
A comprehensive introduction to the most commonly used statistical methods relevant in atmospheric, oceanic and climate sciences. Each method is described step-by-step using plain language, and illustrated with concrete examples, with relevant statistical and scientific concepts explained as needed. Particular attention is paid to nuances and pitfalls, with sufficient detail to enable the reader to write relevant code. Topics covered include hypothesis testing, time series analysis, linear regression, data assimilation, extreme value analysis, Principal Component Analysis, Canonical Correlation Analysis, Predictable Component Analysis, and Covariance Discriminant Analysis. The specific statistical challenges that arise in climate applications are also discussed, including model selection problems associated with Canonical Correlation Analysis, Predictable Component Analysis, and Covariance Discriminant Analysis. Requiring no previous background in statistics, this is a highly accessible textbook and reference for students and early-career researchers in the climate sciences.