Statistical Postprocessing of Ensemble Forecasts brings together contributed chapters by international subject-matter experts describing the current state of the art in statistical post-processing of ensemble forecasts, and illustrating the use of these methods in several important applications including weather, hydrological and climate forecasts, and renewable energy forecasting.
Edited by three experts with strong and complementary expertise in statistical post-processing of ensemble forecasts, this book assesses the new and rapidly developing field of ensemble forecasts as an extension of the use of statistical corrections and their use for post-processing traditional deterministic forecasts.
- Consolidates, for the first time, 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
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
1. Introduction and overview
2. Review of ensemble forecasting and the need for calibration
3. Univariate ensemble post-processing
4. Multivariate ensemble post-processing
5. Post-processing for extreme events
6. Verification: assessment of calibration and accuracy
7. Application of post-processing to Weather forecasts
8. Application of post-processing for hydrological forecasts
9. Application of post-processing for renewable energies
10. Application of post-processing for seasonal, decadal, and climate projections
11. Algorithms for computing the most frequently needed methods for ensemble post-processing and verification
- No. of pages:
- © Elsevier 2019
- 1st September 2018
- Paperback ISBN:
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 post-processing.
Royal Meteorological Institute of Belgium, Brussels
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.
Department of Earth and Atmospheric Sciences, Cornell University, USA
Is a postdoc at the Institute of Statistics and the Institute of Atmospheric and Cryospheric Sciences since 2013. His research areas include statistical models to post-process ensemble forecasts and their software implementation.
Institute of Atmospheric and Cryospheric Sciences, University of Innsbruck, Austria