Atmospheric Modeling, Data Assimilation and PredictabilityCambridge University Press, 2003 - 341 pages This comprehensive text and reference work on numerical weather prediction covers for the first time, not only methods for numerical modeling, but also the important related areas of data assimilation and predictability. It incorporates all aspects of environmental computer modeling including an historical overview of the subject, equations of motion and their approximations, a modern and clear description of numerical methods, and the determination of initial conditions using weather observations (an important new science known as data assimilation). |
Table des matières
Historical overview of numerical weather prediction | 1 |
12 Early developments | 4 |
13 Primitive equations global and regional models and nonhydrostatic models | 10 |
determination of the initial conditions for the computer forecasts | 12 |
15 Operational NWP and the evolution of forecast skill | 17 |
16 Nonhydrostatic mesoscale models | 24 |
17 Weather predictability ensemble forecasting and seasonal to interannual prediction | 25 |
18 The future | 30 |
Data assimilation | 136 |
52 Empirical analysis schemes | 140 |
53 Introduction to least squares methods | 142 |
54 Multivariate statistical data assimilation methods | 149 |
55 3DVar the physical space analysis scheme PSAS and their relationship to OI | 168 |
56 Advanced data assimilation methods with evolving forecast error covariance | 175 |
57 Dynamical and physical balance in the initial conditions | 185 |
58 Quality control of observations | 198 |
The continuous equations | 32 |
22 Atmospheric equations of motion on spherical coordinates | 36 |
23 Basic wave oscillations in the atmosphere | 37 |
24 Filtering approximations | 47 |
25 Shallow water equations quasigeostrophic filtering and filtering of inertiagravity waves | 53 |
26 Primitive equations and vertical coordinates | 60 |
Numerical discretization of the equations of motion | 68 |
numerical solution | 72 |
33 Space discretization methods | 91 |
34 Boundary value problems | 114 |
35 Lateral boundary conditions for regional models | 120 |
Introduction to the parameterization of subgridscale physical processes | 127 |
42 Subgridscale processes and Reynolds averaging | 129 |
43 Overview of model parameterizations | 132 |
Atmospheric predictability and ensemble forecasting | 205 |
62 Brief review of fundamental concepts about chaotic systems | 208 |
63 Tangent linear model adjoint model singular vectors and Lyapunov vectors | 212 |
early studies | 227 |
65 Operational ensemble forecasting methods | 234 |
66 Growth rate of errors and the limit of predictability in midlatitudes and in the tropics | 249 |
67 The role of the oceans and land in monthly seasonal and interannual predictability | 254 |
68 Decadal variability and climate change | 258 |
The early history of NWP | 261 |
Coding and checking the tangent linear and the adjoint models | 264 |
Postprocessing of numerical model output to obtain station weather forecasts | 276 |
| 283 | |
| 328 | |
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Expressions et termes fréquents
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