Statistics and Data Visualization in Climate Science with R and Python

‘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

‘This book is a gem. It is the proverbial fishing rod to those interested in statistical analysis of climate data and visualization that facilitates insightful interpretation. By providing a plethora of actual examples and R and Python scripts, it lays out the “learning by doing” foundation upon which students and professionals alike can build their own applications to explore climate data. This book will become an invaluable desktop reference in Climate Statistics.’ Ana P. Barros, University of Illinois Urbana-Champain

‘A valuable toolkit of practical statistical methods and skills for using computers to analyze and visualize large data sets, this unique book empowers readers to gain physical understanding from climate data. The authors have carried out fundamental research in this field, and they are master teachers who have taught the material often. Their expertise is evident throughout the book.’ Richard C. J. Somerville, University of California, San Diego

‘This book is written by experts in the field, working on the frontiers of climate science. It enables instructors to ‘flip the classroom’, and highly motivated students to visualize and analyze their own data sets. The book clearly and succinctly summarizes the applicable statistical principles and formalisms and goes on to provide detailed tutorials on how to apply them, starting with very simple tasks and moving on to illustrate more advanced, state-of-the-art techniques. Having this book readily available should reduce the time required for advanced undergraduate and graduate students to achieve sufficient proficiency in research methodology to become productive scientists in their own right.’ John M. Wallace, University of Washington