
Statistical Postprocessing of Ensemble Forecasts
Description
Key Features
- 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
Readership
Researchers and operational practitioners in weather, seasonal, and climate forecasting; users of such forecasts in such fields of application as renewable energy, conventional energy, hydrology, environmental engineering, and agriculture; and students in these and related fields
Table of Contents
1 Uncertain Forecasts From Deterministic Dynamics
Daniel S. Wilks, Stéphane Vannitsem
2 Ensemble Forecasting and the Need for Calibration
Roberto Buizza
3 Univariate Ensemble Postprocessing
Daniel S. Wilks
4 Ensemble Postprocessing Methods Incorporating Dependence Structures
Roman Schefzik, Annette Möller
5 Postprocessing for Extreme Events
Petra Friederichs, Sabrina Wahl, Sebastian Buschow
6 Verification: Assessment of Calibration and Accuracy
Thordis L. Thorarinsdottir, Nina Schuhen
7 Practical Aspects of Statistical Postprocessing
Thomas M. Hamill
8 Applications of Postprocessing for Hydrological Forecasts
Stephan Hemri
9 Application of Postprocessing for Renewable Energy
Pierre Pinson, Jakob W. Messner
10 Postprocessing of Long-Range Forecasts
Bert Van Schaeybroeck, Stéphane Vannitsem
11 Ensemble Postprocessing With R
Jakob W. Messner
Product details
- No. of pages: 362
- Language: English
- Copyright: © Elsevier 2018
- Published: May 17, 2018
- Imprint: Elsevier
- Paperback ISBN: 9780128123720
- eBook ISBN: 9780128122488
About the Editors
Stéphane Vannitsem
Affiliations and Expertise
Daniel Wilks
Affiliations and Expertise
Jakob Messner
Affiliations and Expertise
Ratings and Reviews
There are currently no reviews for "Statistical Postprocessing of Ensemble Forecasts"