Forecast Verification
Forecast Verification

Author: Ian T. Jolliffe

Publisher: John Wiley & Sons

Published: 2003-08-01

Total Pages: 257

ISBN-13: 0470864419

DOWNLOAD EBOOK

This handy reference introduces the subject of forecastverification and provides a review of the basic concepts,discussing different types of data that may be forecast. Each chapter covers a different type of predicted quantity(predictand), then looks at some of the relationships betweeneconomic value and skill scores, before moving on to review the keyconcepts and summarise aspects of forecast verification thatreceive the most attention in other disciplines. The book concludes with a discussion on the most importanttopics in the field that are the subject of current research orthat would benefit from future research. An easy to read guide of current techniques with real life casestudies An up-to-date and practical introduction to the differenttechniques and an examination of their strengths andweaknesses Practical advice given by some of the world?s leadingforecasting experts Case studies and illustrations of actual verification and itsinterpretation Comprehensive glossary and consistent statistical andmathematical definition of commonly used terms

Short-range Forecasting of Cloudiness and Precipitation Through Extrapolation of GOES Imagery
Short-range Forecasting of Cloudiness and Precipitation Through Extrapolation of GOES Imagery

Author: H. Stuart Muench

Publisher:

Published: 1981

Total Pages: 50

ISBN-13:

DOWNLOAD EBOOK

This report describes the development and testing of an objective technique to forecast cloudiness and precipitation through extrapolation of satellite imagery. By utilizing on objectively determined cloud-motion vector, the technique makes local forecasts of satellite parameters (brightness and IR temperature), with high temporal resolution, using simple linear extrapolation. Algorithms are then used to convert the satellite parameters to total cloud cover, probability of 1-hour precipitation, and presence of low, middle, and high clouds. The test program computed motion vectors and made forecasts out to 7 hours, in half-hour steps, at 30 locations. The program was tested on 12 spring and fall cases, using half-hourly GOES imagery. For periods beyond 2 hours, forecasts of cloud cover and precipitation were markedly better than persistence, which deficiencies in specification hindered short-period performance. Forecasts of cloud layers were worse than persistence due to inadequate specification algorithms. The net results were quite encouraging, and further refinements and developments are planned.

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

DOWNLOAD EBOOK

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

Sub-seasonal to Seasonal Prediction
Sub-seasonal to Seasonal Prediction

Author: Andrew Robertson

Publisher: Elsevier

Published: 2018-10-19

Total Pages: 588

ISBN-13: 012811715X

DOWNLOAD EBOOK

The Gap Between Weather and Climate Forecasting: Sub-seasonal to Seasonal Prediction is an ideal reference for researchers and practitioners across the range of disciplines involved in the science, modeling, forecasting and application of this new frontier in sub-seasonal to seasonal (S2S) prediction. It provides an accessible, yet rigorous, introduction to the scientific principles and sources of predictability through the unique challenges of numerical simulation and forecasting with state-of-science modeling codes and supercomputers. Additional coverage includes the prospects for developing applications to trigger early action decisions to lessen weather catastrophes, minimize costly damage, and optimize operator decisions. The book consists of a set of contributed chapters solicited from experts and leaders in the fields of S2S predictability science, numerical modeling, operational forecasting, and developing application sectors. The introduction and conclusion, written by the co-editors, provides historical perspective, unique synthesis and prospects, and emerging opportunities in this exciting, complex and interdisciplinary field. - Contains contributed chapters from leaders and experts in sub-seasonal to seasonal science, forecasting and applications - Provides a one-stop shop for graduate students, academic and applied researchers, and practitioners in an emerging and interdisciplinary field - Offers a synthesis of the state of S2S science through the use of concrete examples, enabling potential users of S2S forecasts to quickly grasp the potential for application in their own decision-making - Includes a broad set of topics, illustrated with graphic examples, that highlight interdisciplinary linkages