Text-Book of Long Range Weather Forecasting
Text-Book of Long Range Weather Forecasting

Author: George J. McCormack

Publisher: Astrology Classics

Published: 2012-04

Total Pages: 164

ISBN-13: 193330345X

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George J. McCormack, (1887-1974) had a life-long interest in astrology and the weather. Inspired by the astrometeorological work of A.J. Pearce (1840-1923), McCormack meticulously tracked and recorded the weather, from before World War I, until his death more than half a century later. In 1947, after 23 years of research, he published his "key" to long-range weather forecasting, being this book. Confident of his ability, in the spring of 1947 McCormack predicted one of the most severe winters in decades, specifically forecasting the infamous snows of December 26, 1947. He was nationally famous overnight. The techniques he used are in this amazing book. With study, they will become yours. The weather bureau predicts the weather, day by day, by careful observation of current conditions. You can learn to predict based on underlying celestial factors, which can be known months, even years, in advance. In 1963, before the US Weather Bureau, and again in 1964, before the American Meteorological Society, McCormack presented his life's work. Both groups ignored him, to our great loss. Use this book, make a better choice.

Statistical Postprocessing of Ensemble Forecasts
Statistical Postprocessing of Ensemble Forecasts

Author: Stéphane Vannitsem

Publisher: Elsevier

Published: 2018-05-17

Total Pages: 364

ISBN-13: 012812248X

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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 forecasts: 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. - Consolidates, for the first time, the methodologies and applications of ensemble forecasts in one succinct place - Provides real-world examples of methods used to formulate forecasts - Presents the tools needed to make the best use of multiple model forecasts in a timely and efficient manner