Statistics and Data Visualization in Climate Science with R and Python

Samuel S. P. Shen is Distinguished Professor of Mathematics and Statistics at San Diego State University, and Visiting Research Mathematician at Scripps Institution of Oceanography, University of California, San Diego. Formerly, he was McCalla Professor of Mathematical and Statistical Sciences at the University of Alberta, Canada, and President of the Canadian Applied and Industrial Mathematics Society. He has held visiting positions at the NASA Goddard Space Flight Center, the NOAA Climate Prediction Center, and the University of Tokyo. Shen holds a B.Sc. degree in Engineering Mechanics and a Ph.D. degree in Applied Mathematics. Gerald R. North is University Distinguished Professor Emeritus and former Head of the Department of Atmospheric Science at Texas A&M University. His research focuses on modern and paleo-climate analysis, satellite remote sensing, climate and hydrology modeling, and statistical methods in atmospheric science. He is an elected Fellow of the American Geophysical Union and the American Meteorological Society. He has received several awards including the Harold J. Haynes Endowed Chair in Geosciences of Texas A&M University, the Jules G. Charney medal from the American Meteorological Society, and the Scientific Achievement medal from NASA. North holds both B.Sc. and Ph.D. degrees in Physics.

'Statistics and Data Visualization in Climate Science with R and Python by Sam Shen and Jerry North is a fabulous addition to the set of tools for scientists, educators and students who are interested in working with data relevant to climate variability and change … I can testify that this book is an enormous help to someone like me. I no longer can simply ask my grad students and postdocs to download and analyze datasets, but I still want to ask questions and find data-based answers. This book perfectly fills the 40-year gap since I last had to do all these things myself, and I can't wait to begin to use it … I am certain that teachers will find the book and supporting materials extremely beneficial as well. Professors Shen and North have created a resource of enormous benefit to climate scientists.' Phillip A. Arkin, University of Maryland

• 1. Basics of Climate Data Arrays, Statistics, and Visualization; 2. Elementary Probability and Statistics; 3. Estimation and Decision Making; 4. Regression Models and Methods; 5. Matrices for Climate Data; 6. Covariance Matrices, EOFs, and PCs; 7. Introduction to Time Series; 8. Spectral Analysis of Time Series; 9. Introduction to Machine Learning; References and Further Reading; Exercises; Index.