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Statistical postprocessing of ensemble forecasts /
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 applicatio...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam, Netherlands :
Elsevier,
[2018]
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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.