| 000 | 03508cam a2200277zu 4500 | ||
|---|---|---|---|
| 001 | 88864260 | ||
| 003 | FRCYB88864260 | ||
| 005 | 20250107231058.0 | ||
| 006 | m o d | ||
| 007 | cr un | ||
| 008 | 250108s2018 fr | o|||||0|0|||eng d | ||
| 020 | _a9780128123720 | ||
| 035 | _aFRCYB88864260 | ||
| 040 |
_aFR-PaCSA _ben _c _erda |
||
| 100 | 1 | _aVannitsem, Stéphane | |
| 245 | 0 | 1 |
_aStatistical Postprocessing of Ensemble Forecasts _c['Vannitsem, Stéphane'] |
| 264 | 1 |
_bElsevier Science _c2018 |
|
| 300 | _a p. | ||
| 336 |
_btxt _2rdacontent |
||
| 337 |
_bc _2rdamdedia |
||
| 338 |
_bc _2rdacarrier |
||
| 650 | 0 | _a | |
| 700 | 0 | _aVannitsem, Stéphane | |
| 856 | 4 | 0 |
_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88864260 _qtext/html _a |
| 520 | _aStatistical 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 placeProvides real-world examples of methods used to formulate forecastsPresents the tools needed to make the best use of multiple model forecasts in a timely and efficient manner | ||
| 999 |
_c71136 _d71136 |
||