Statistical Postprocessing of Ensemble Forecasts

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, 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

1. Uncertain Forecasts From Deterministic Dynamics 2. Ensemble Forecasting and the Need for Calibration 3. Univariate Ensemble Postprocessing 4. Ensemble Postprocessing Methods Incorporating Dependence Structures 5. Postprocessing for Extreme Events 6. Verification: Assessment of Calibration and Accuracy 7. Practical Aspects of Statistical Postprocessing 8. Applications of Postprocessing for Hydrological Forecasts 9. Application of Postprocessing for Renewable Energy 10. Postprocessing of Long-Range Forecasts 11. Ensemble Postprocessing With R