| Description |
1 online resource : illustrations |
| Contents |
Front Cover; Statistical Postprocessing of Ensemble Forecasts; Copyright; Contents; Contributors; Preface; Chapter 1: Uncertain Forecasts From Deterministic Dynamics; 1.1. Sensitivity to Initial Conditions, or ̀̀Chaos;́́ 1.2. Uncertainty and Probability in ̀̀Deterministic ́́Predictions; 1.3. Ensemble Forecasting; 1.4. Postprocessing Individual Dynamical Forecasts; 1.5. Postprocessing Ensemble Forecasts: Overview of This Book; References; Chapter 2: Ensemble Forecasting and the Need for Calibration; 2.1. The Dynamical Weather Prediction Problem; 2.1.1. Historical Background |
|
2.1.2. Observations2.1.3. The Equations of Motion for the Atmosphere; 2.1.4. Computation of the Initial Conditions (Analysis); 2.2. The Chaotic Nature of the Atmosphere; 2.3. From Single to Ensemble Forecasts; 2.3.1. Forecast Reliability and Accuracy; 2.3.2. Are Ensemble Forecasts More Valuable than a Single Forecast?; 2.4. Sources of Forecast Errors; 2.5. Characteristics of the Operational Global Ensemble Systems; 2.6. The Value of a Reforecast Suite; 2.7. A Look Into the Future; 2.8. Summary: The Key Messages of This Chapter; References; Chapter 3: Univariate Ensemble Postprocessing |
|
3.1. Introduction3.2. Nonhomogeneous Regressions, and Other Regression Methods; 3.2.1. Nonhomogeneous Gaussian Regression (NGR); 3.2.2. Nonhomogeneous Regressions With More Flexible Predictive Distributions; 3.2.3. Truncated Nonhomogeneous Regressions; 3.2.4. Censored Nonhomogeneous Regressions; 3.2.5. Logistic Regression; 3.3. Bayesian Model Averaging, and Other ̀̀Ensemble Dressing ́́Methods; 3.3.1. Bayesian Model Averaging (BMA); 3.3.2. Other Ensemble Dressing Methods; 3.4. Fully Bayesian Ensemble Postprocessing Approaches; 3.5. Nonparametric Ensemble Postprocessing Methods |
|
3.5.1. Rank Histogram Recalibration3.5.2. Quantile Regression; 3.5.3. Ensemble Dressing; 3.5.4. Individual Ensemble-Member Adjustments; 3.5.5. ̀̀Statistical Learning ́́Methods for Ensemble Postprocessing; 3.6. Comparisons Among Methods; References; Chapter 4: Ensemble Postprocessing Methods Incorporating Dependence Structures; 4.1. Introduction; 4.2. Dependence Modeling Via Copulas; 4.2.1. Copulas and Sklar's Theorem; 4.2.2. Parametric, in Particular Gaussian, Copulas; 4.2.3. Empirical Copulas; 4.3. Parametric Multivariate Approaches; 4.3.1. Intervariable Dependencies |
|
4.3.2. Spatial Dependencies4.3.3. Temporal Dependencies; 4.4. Nonparametric Multivariate Approaches; 4.4.1. Empirical Copula-Based Ensemble Postprocessing; 4.4.2. Ensemble Copula Coupling (ECC); 4.4.3. Schaake Shuffle-Based Approaches; 4.5. Univariate Approaches Accounting for Dependencies; 4.5.1. Spatial Dependencies; 4.5.2. Temporal Dependencies; 4.6. Discussion; References; Chapter 5: Postprocessing for Extreme Events; 5.1. Introduction; 5.2. Extreme-Value Theory; 5.2.1. Generalized Extreme-Value Distribution; 5.2.2. Peak-Over-Threshold Approach; 5.2.3. Nonstationary Extremes |
| Summary |
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 MïŽller (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 |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Vendor-supplied metadata |
| Subject |
Weather forecasting.
|
|
SCIENCE -- Earth Sciences -- Geography.
|
|
SCIENCE -- Earth Sciences -- Geology.
|
|
Weather forecasting
|
| Form |
Electronic book
|
| Author |
Vannitsem, Stéphane, editor.
|
|
Wilks, Daniel S., editor.
|
|
Messner, Jakob, editor
|
| ISBN |
9780128122488 |
|
012812248X |
|
