Atmospheric Modeling, Data Assimilation and Predictability

Couverture
Cambridge 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).
 

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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
References
283
Index
328
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À propos de l'auteur (2003)

Eugenia Kalnay was awarded a Ph.D in Meteorology from the Massachusetts Institute of Technology in 1971. Following a position as Associate Professor in the same department, she became Chief of the Global Modeling and Simulation Branch at the NASA Goddard Space Flight Center (1983-1987). From 1987 to 1997 she was Director of the Environmental Modeling Center (US National Weather Service) and in 1998 was awarded the Robert E. Lowry endowed chair at the University of Oklahoma. In 1999 she became the Chair of the Department of Meteorology at the University of Maryland. Professor Kalnay is a member of the US National Academy of Engineering, is the recipient of two gold medals from the US Department of Commerce and the NASA Medal for Exceptional Scientific Achievement, and has received the Jule Charney Award from the American Meteorological Society. The author of more than 100 peer reviewed papers on numerical weather prediction, data assimilation and predictability, Professor Kalnay is a key figure in this field and has pioneered many of the essential techniques.

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