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.
Happy New Almanac Year! It’s time to celebrate the 230th edition of The Old Farmer’s Almanac! Long recognized as North America’s most-beloved and best-selling annual, this handy yellow book fulfills every need and expectation as a calendar of the heavens, a time capsule of the year, an essential reference that reads like a magazine. Always timely, topical, and distinctively “useful, with a pleasant degree of humor,” the Almanac is consulted daily throughout the year by users from all walks of life. The 2022 edition contains the fun facts, predictions, and feature items that have made it a cultural icon: traditionally 80 percent–accurate weather forecasts; notable astronomical events and time-honored astrological dates; horticultural, culinary, fashion, and other trends; historical hallmarks; best fishing days; time- and money-saving garden advice; recipes for delicious dishes; facts on folklore, farmers, home remedies, and husbandry; amusements and contests; plus too much more to mention—all in the inimitable Almanac style that has charmed and educated readers since 1792.
Forecasting the weather for the long and medium range is a difficult and scientifically challenging problem. Since the first operational weather prediction by numerical methods was carried out (on the BESK computer in Stockholm, Sweden, 1954) . there has been an ever accelerating development in computer technology. Hand in hand has followed a tremendous increase in the complexity of the atmospheric models used for weather prediction. The ability of these models to predict future states of the atmosphere has also increased rapidly, both due to model development and due to more accurate and plentiful observations of the atmosphere to define the initial . state for model integrations. It may however be argued on theoretical grounds that even if we have an almost perfect model with almost perfect initial data, we will never be able to make an accurate weather prediction more than a few weeks ahead. This is due to the inherent instability of the atmosphere and work in this field was pioneered by E. Lorenz. It is generally referred to as atmospheric predict ability and in the opening chapter of this book Professor Lorenz gives us an overview of the problem of atmospheric predictability. The contributions to this book were originally presented at the 1981 ECMWF Seminar (ECMWF - European Centre for Medium Range Weather Forecasts) which was held at ECMWF in Reading, England, in September 1981.
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
