Statistical Postprocessing of Ensemble Forecasts

by Stephane Vannitsem (Editor), Daniel S Wilks (Editor), Jakob Messner (Editor)

Statistical Postprocessing of Ensemble Forecasts brings together chapters contributed by international subject-matter experts describing the current state of the art in the statistical postprocessing of ensemble forecasts. The book illustrates the use of these methods in several important applications including weather, hydrological and climate forecasts, and renewable energy forecasting. After an introductory section on ensemble forecasts and prediction systems, the second section of the book is devoted to exposition of the methods available for statistical postprocessing of ensemble univariate and multivariate ensemble postprocessing are first reviewed by Wilks (Chapters 3), then Schefzik and Möller (Chapter 4), and the more specialized perspective necessary for postprocessing forecasts for extremes is presented by Friederichs, Wahl, and Buschow (Chapter 5). The second section concludes with a discussion of forecast verification methods devised specifically for evaluation of ensemble forecasts (Chapter 6 by Thorarinsdottir and Schuhen). The third section of this book is devoted to applications of ensemble postprocessing. Practical aspects of ensemble postprocessing are first detailed in Chapter 7 (Hamill), including an extended and illustrative case study. Chapters 8 (Hemri), 9 (Pinson and Messner), and 10 (Van Schaeybroeck and Vannitsem) discuss ensemble postprocessing specifically for hydrological applications, postprocessing in support of renewable energy applications, and postprocessing of long-range forecasts from months to decades. Finally, Chapter 11 (Messner) provides a guide to the ensemble-postprocessing software available in the R programming language, which should greatly help readers implement many of the ideas presented in this book. Edited by three experts with strong and complementary expertise in statistical postprocessing of ensemble forecasts, this book assesses the new and rapidly developing field of ensemble forecast postprocessing as an extension of the use of statistical corrections to traditional deterministic forecasts. Statistical Postprocessing of Ensemble Forecasts is an essential resource for researchers, operational practitioners, and students in weather, seasonal, and climate forecasting, as well as users of such forecasts in fields involving renewable energy, conventional energy, hydrology, environmental engineering, and agriculture.

362 pages, Paperback

Published June 5, 2018

Reviews

[David · Rating 3 out of 5]

An essential book if you are working in statistical postprocessing (calibration) in meteorology or climatology. However, I have mixed feelings because the book is a collection of chapters by different authors in their area of expertise. Chapters offer a lot of information, but not so much _formation_ and I clearly prefer the other way around. In this sense, I think the book is not very good at the pedagogical level. Besides, when you give so much information, you stun the reader and the readability diminishes. In some chapters you appreciate this more than in others.

But as I said, it's an essential book if you work in this area due to the last chapter, an application of a lot of the ideas of the book in R code. You can learn a lot from it and begin to do your own calibrations and their verifications.