Statistical Analysis of Climate Series

Analyzing, Plotting, Modeling, and Predicting with R

  • Helmut Pruscha

Table of contents

  1. Front Matter
    Pages i-viii
  2. Helmut Pruscha
    Pages 1-10
  3. Helmut Pruscha
    Pages 11-28
  4. Helmut Pruscha
    Pages 29-48
  5. Helmut Pruscha
    Pages 49-66
  6. Helmut Pruscha
    Pages 67-76
  7. Helmut Pruscha
    Pages 77-101
  8. Helmut Pruscha
    Pages 103-120
  9. Helmut Pruscha
    Pages 121-139
  10. Back Matter
    Pages 141-175

About this book

Introduction

The book presents the application of statistical methods to climatological data on temperature and precipitation. It provides specific techniques for treating series of yearly, monthly and daily records. The results’ potential relevance in the climate context is discussed.

The methodical tools are taken from time series analysis, from periodogram and wavelet analysis, from correlation and principal component analysis, and from categorical data and event-time analysis.

The applied models are - among others - the ARIMA and GARCH model, and inhomogeneous Poisson processes.

Further, we deal with a number of special statistical topics, e.g. the problem of trend-, season- and autocorrelation-adjustment, and with simultaneous statistical inference.

Programs in R and data sets on climate series, provided at the author’s homepage, enable readers (statisticians, meteorologists, other natural scientists) to perform their own exercises and discover their own applications.

Keywords

Climate Series Computing in R Statistical Analysis

Authors and affiliations

  • Helmut Pruscha
    • 1
  1. 1.MünchenGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-32084-2
  • Copyright Information Springer-Verlag Berlin Heidelberg 2013
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-642-32083-5
  • Online ISBN 978-3-642-32084-2
  • About this book