Uncertainties in numerical weather prediction /

Uncertainties in Numerical Weather Prediction is a comprehensive work on the most current understandings of uncertainties and predictability in numerical simulations of the atmosphere. It provides general knowledge on all aspects of uncertainties in the weather prediction models in a single, easy to...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Haraldur Olafsson (Editor ), Bao, J. -W. (Jian Wen), 1962- (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam, Netherlands ; Cambridge, MA : Elsevier, [2021]
Temas:
Numerical weather forecasting.
Temps (M�et�eorologie) > Pr�evision num�erique.
Numerical weather forecasting
Acceso en línea:Texto completo

MARC

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245 0 0 |a Uncertainties in numerical weather prediction /  |c edited by Haraldur Olafsson, Jian-Wen Bao. 
264 1 |a Amsterdam, Netherlands ;  |a Cambridge, MA :  |b Elsevier,  |c [2021] 
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505 0 |a Intro -- Uncertainties in Numerical Weather Prediction -- Copyright -- Contents -- Contributors -- Preface -- Chapter 1: Dynamical cores for NWP: An uncertain landscape -- 1. Introduction -- 2. Governing equations -- 3. Some physical properties -- 4. Discretizing in time -- 4.1. Some different approaches -- 4.1.1. Single-stage, single-step schemes -- 4.1.2. Single-stage, multistep schemes -- 4.1.3. Multistage single-step schemes -- 4.2. Some numerical properties -- 4.2.1. Single-stage, single-step schemes -- 4.2.2. Single-stage, multistep schemes -- 4.2.3. Multistage single-step schemes 
505 8 |a 5. Discretizing in space -- 5.1. Some different approaches -- 5.1.1. Finite-difference method -- 5.1.2. Finite-volume method -- 5.1.3. Finite-element method -- 5.2. Some numerical properties -- 5.2.1. Finite difference -- 5.2.2. Finite volume -- 5.2.3. Finite element -- 6. (Semi- )Lagrangian approach -- 7. Multidimensional aspects -- 7.1. Extending the discretization to two dimensions -- 7.2. Grids -- 8. An outlook -- Acknowledgments -- References -- Chapter 2: Numerical uncertainties in discretization of the shallow-water equations for weather predication models -- 1. Introduction 
505 8 |a 2. Discretization of the governing equation I -- 2.1. Temporal differential operator -- 2.1.1. Euler and Crank-Nicholson methods -- 2.1.2. Multistep methods -- 2.1.3. Multistage methods -- 2.2. Spatial differential operators -- 2.2.1. Grid staggering and operator discretization -- 2.2.2. Nonzero null space of discrete operators -- 2.3. Extension to two dimensions -- 3. Discretization of the governing equation II -- 3.1. Flux-corrected transport (FCT) schemes -- 3.2. Flux limiter transport schemes -- 3.3. Multidimensional positive definite advection transport algorithm (MPDATA) 
505 8 |a 4. Filtering, damping, and limiting techniques -- 4.1. Horizontal diffusion -- 4.2. Divergence damping (2D) -- 4.3. Smagorinsky horizontal diffusion -- 4.4. Shapiro filters -- 4.5. Polar spectral filtering -- 4.6. Robert-Asselin time filtering -- 4.7. Lax-Wendroff advection method -- 4.8. Shape preserving advection methods -- 5. Global models on unstructured grids -- 5.1. Icosahedral-hexagonal grid -- 5.1.1. MPAS: A community global model -- 5.2. Cubed-sphere grid -- 5.2.1. FV3: NGGPS model at NCEP -- 5.3. Some remarks on unstructured grid -- 6. Summary -- Acknowledgments -- References 
505 8 |a Chapter 3: Probabilistic view of numerical weather prediction and ensemble prediction -- 1. The numerical weather prediction problem -- 1.1. What are the key processes of numerical weather prediction? -- 1.2. An integrated suite of analysis and forecasts -- 1.3. Observations -- 1.4. The model equations -- 1.5. The definition of the initial conditions -- 2. Sources of forecast errors and the chaotic nature of the atmospheric flow -- 3. Ensemble-based probabilistic prediction -- 3.1. How can a forecast PDF be generated? -- 3.2. Ensemble methods: How should one design an ensemble? 
520 |a Uncertainties in Numerical Weather Prediction is a comprehensive work on the most current understandings of uncertainties and predictability in numerical simulations of the atmosphere. It provides general knowledge on all aspects of uncertainties in the weather prediction models in a single, easy to use reference. The book illustrates particular uncertainties in observations and data assimilation, as well as the errors associated with numerical integration methods. Stochastic methods in parameterization of subgrid processes are also assessed, as are uncertainties associated with surface-atmosphere exchange, orographic flows and processes in the atmospheric boundary layer. Through a better understanding of the uncertainties to watch for, readers will be able to produce more precise and accurate forecasts. This is an essential work for anyone who wants to improve the accuracy of weather and climate forecasting and interested parties developing tools to enhance the quality of such forecasts. 
588 0 |a Online resource; title from digital title page (viewed on February 24, 2021). 
650 0 |a Numerical weather forecasting. 
650 6 |a Temps (M�et�eorologie)  |x Pr�evision num�erique.  |0 (CaQQLa)201-0042781 
650 7 |a Numerical weather forecasting  |2 fast  |0 (OCoLC)fst01041307 
700 0 |a Haraldur Olafsson,  |e editor. 
700 1 |a Bao, J. -W.  |q (Jian Wen),  |d 1962-  |e editor. 
776 0 8 |i Print version:  |z 9780128154915 
856 4 0 |u https://sciencedirect.uam.elogim.com/science/book/9780128154915  |z Texto completo 

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