Atmospheric modeling, data assimilation, and predictability / Eugenia Kalnay.

By: Kalnay, Eugenia, 1942-.
Material type: materialTypeLabelBookPublisher: New York : Cambridge University Press, c2003Description: xxii, 341 p. : ill. (some col.), map ; 25 cm.ISBN: 0521791790 (hardback); 0521796296 (pbk.).Subject(s): NUMERICAL WEATHER PREDICTION | DATA ASSIMILATION | CHAOS | DYNAMICAL SYSTEMS | PREDICTION | METEOROLOGY | ATMOSPHERIC MODELS | NUMERICAL MODELLING | MATHEMATICAL MODELS | CLIMATIC CHANGESHoldings: GRETA POINT: 551.509 ATM Summary: Historical overview of numerical weather prediction.
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551.509 ATM 1 Available B01581

Reprinted 2004.

Includes bibliographical references (p. 283-327) and index.

Historical overview of numerical weather prediction. Early developments. Primitive equations, global and regional models, and nonhydrostatic models. Data assimilation: determination of the initial conditions for the computer forecasts. Operational NWP and the evolution of forecast skill. Nonhydrostatic mesoscale models. Weather predictability, ensemble forecasting, and seasonal to interannual prediction. The future -- 2. The continuous equations. Governing equations. Atmospheric equations of motion on spherical coordinates. Basic wave oscillations in the atmosphere. Filtering approximations. Shallow water equations, quasi-geostrophic filtering, and filtering of inertia-gravity waves. Primitive equations and vertical coordinates -- 3. Numerical discretization of the equations of motion. Classification of partial differential equations (PDEs). Initial value problems: numerical solution. Space discretization methods. Boundary value problems. Lateral boundary conditions for regional models -- 4. Introduction to the parameterization of subgrid-scale physical processes. Subgrid-scale processes and Reynolds averaging. Overview of model parameterizations -- 5. Data assimilation. Empirical analysis schemes. Introduction to least squares methods. Multivariate statistical data assimilation methods. 3D-Var. the physical space analysis scheme (PSAS), and their relation to OI. Advanced data assimilation methods with evolving forecast error covariance. Dynamical and physical balance in the initial conditions. Quality control of observations -- 6. Atmospheric predictability and ensemble forecasting. Introduction to atmospheric predictability. Brief review of fundamental concepts about chaotic systems. Tangent linear model, adjoint model, singular vectors, and Lyapunov vectors. Ensemble forecasting: early studies. Operational ensemble forecasting methods. Growth rate errors and the limit of predictability in mid-latitudes and in the tropics. The role of the oceans and land in monthly, seasonal, and interannual predictability. Decadal variability and climate change. App. A. The early history of NWP -- App. B. Coding and checking the tangent linear and the adjoint models -- App. C. Post-processing of numerical model output to obtain station weather forecasts


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