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Statistical Postprocessing of Ensemble Forecasts de Daniel S. Wilks
Descripción - Reseña del 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 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 Biografía del autor Stéphane Vannitsem is a member of the Research Division of the Royal Meteorological Institute of Belgium since 1994, and has been co-editor of three special issues, two in nonlinear processes in Geophysics, and one in International Journal of Bifurcation and Chaos. His fields of expertise include dynamical chaos, predictability and data assimilation, and statistical postprocessing.Daniel S. Wilks has been a member of the Atmospheric Sciences faculty at Cornell University since 1987, and is the author of Statistical Methods in the Atmospheric Sciences (2011, Academic Press), which is in its third edition and has been continuously in print since 1995. Research areas include statistical forecasting, forecast postprocessing, and forecast evaluation.Jakob W. Messner is a post-doctoral fellow at the Electrical Engineering department of the Technical University of Denmark. He holds a Ph.D. in Atmospheric Sciences from the University of Innsbruck and his main research interests include statistical forecasting for weather and energy applications, ensemble postprocessing, and implementation of statistical methods in open-source software.
Detalles del Libro
- Name: Statistical Postprocessing of Ensemble Forecasts
- Autor: Daniel S. Wilks
- Categoria: Libros,Ciencias, tecnología y medicina,Ciencias de la Tierra
- Tamaño del archivo: 14 MB
- Tipos de archivo: PDF Document
- Descargada: 264 times
- Idioma: Español
- Archivos de estado: AVAILABLE
Descargar Statistical Postprocessing of Ensemble Forecasts de Daniel S. Wilks libros ebooks
Statistical postprocessing of ensemble forecasts 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
Statistical postprocessing of ensemble forecasts 1st edition statistical postprocessing of ensemble forecasts brings together chapters contributed by international subjectmatter 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
Statistical postprocessing of ensemble forecasts statistical postprocessing of ensemble forecasts brings together chapters contributed by international subjectmatter 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
Postprocessing of ensemble weather forecasts using a this study proposes a new statistical method for postprocessing ensemble weather forecasts using a stochastic weather generator key parameters of the weather generator were linked to the ensemble forecast means for both precipitation and temperature allowing the generation of an infinite number of daily times
Statistical postprocessing of ensemble forecasts engels statistical postprocessing of ensemble forecasts engels door stephane vannitsem daniel wilks jacob wessner onze prijs 13802 verwachte levertijd ongeveer 8 werkdagen
Postprocessing of ensemble weather forecasts using a this study proposes a new statistical method for postprocessing ensemble weather forecasts using a stochastic weather generator key parameters of the weather generator were linked to the ensemble forecast means for both precipitation and temperature allowing the generation of an infinite number of daily times series that are fully coherent with the ensemble weather forecast
New approaches to postprocessing of multimodel ensemble ensemble weather forecasts often underrepresent uncertainty leading to overconfidence in their predictions multimodel forecasts combining several individual ensembles have been shown to display greater skill than singleensemble forecasts in predicting temperatures but tend to retain some bias in their joint predictions
Probabilistic forecasts of wind speed ensemble model pierre pinson jakob w messner application of postprocessing for renewable energy statistical postprocessing of ensemble forecasts 101016b9780128123720000091 241266 2018 crossref sai ganesh nagarajan gareth peters ido nevat spatial field reconstruction of nongaussian random fields the tukey gandh random process ssrn electronic journal 102139ssrn3159687 2018
Ensemble forecasting an overview sciencedirect topics thordis l thorarinsdottir nina schuhen in statistical postprocessing of ensemble forecasts 2018 abstract in ensemble forecasting forecast verification methods are needed to diagnose both the need for statistical postprocessing and the effectiveness of the postprocessing methods in producing calibrated and accurate forecaststhis chapter discusses an array of techniques that can be used
Weather forecasting with ensemble methods science the ability of ensemble systems in concert with statistical postprocessing to improve deterministic forecastsin that the ensemble mean forecast outperforms the individual ensemble membersand to produce probabilistic and uncertainty information to the benefit of weathersensitive public commercial and humanitarian sectors has been convincingly established