Atmospheric Modeling, Data Assimilation and Predictability

by Eugenia Kalnay

• ISBN13: 9780521791793
• ISBN10: 0521791790
• Format: Hardcover
• Copyright: 2002-12-16
• Publisher: Cambridge University Press

Summary

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 of Contents

Foreword	xi
Acknowledgements	xv
List of abbreviations	xvii
List of variables	xxi
Historical overview of numerical weather prediction	1	(31)
Introduction	1	(3)
Early developments	4	(6)
Primitive equations, global and regional models, and nonhydrostatic models	10	(2)
Data assimilation: determination of the initial conditions for the computer forecasts	12	(5)
Operational NWP and the evolution of forecast skill	17	(7)
Nonhydrostatic mesoscale models	24	(1)
Weather predictability, ensemble forecasting, and seasonal to interannual prediction	25	(5)
The future	30	(2)
The continuous equations	32	(36)
Governing equations	32	(4)
Atmospheric equations of motion on spherical coordinates	36	(1)
Basic wave oscillations in the atmosphere	37	(10)
Filtering approximations	47	(6)
Shallow water equations, quasi-geostrophic filtering, and filtering of inertia-gravity waves	53	(7)
Primitive equations and vertical coordinates	60	(8)
Numerical discretization of the equations of motion	68	(59)
Classification of partial differential equations (PDEs)	68	(4)
Initial value problems: numerical solutions	72	(19)
Space discretization methods	91	(23)
Boundary value problems	114	(6)
Lateral boundary conditions for regional models	120	(7)
Introduction to the parameterization of subgrid-scale physical processes	127	(9)
Introduction	127	(2)
Subgrid-scale processes and Reynolds averaging	129	(3)
Overview of model parameterizations	132	(4)
Data assimilation	136	(69)
Introduction	136	(4)
Empirical analysis schemes	140	(2)
Introduction to least squares methods	142	(7)
Multivariate statistical data assimilation methods	149	(19)
3D-Var, the physical space analysis scheme (PSAS), and their relation to OI	168	(7)
Advanced data assimilation methods with evolving forecast error covariance	175	(10)
Dynamical and physical balance in the initial conditions	185	(13)
Quality control of observations	198	(7)
Atmospheric predictability and ensemble forecasting	205	(56)
Introduction to atmospheric predictability	205	(3)
Brief review of fundamental concepts about chaotic systems	208	(4)
Tangent linear model, adjoint model, singular vectors, and Lyapunov vectors	212	(15)
Ensemble forecasting: early studies	227	(7)
Operational ensemble forecasting methods	234	(15)
Growth rate errors and the limit of predictability in mid-latitudes and in the tropics	249	(5)
The role of the oceans and land in monthly, seasonal, and interannual predictability	254	(4)
Decadal variability and climate change	258	(3)
Appendix A The early history of NWP	261	(3)
Appendix B Coding and checking the tangent linear and the adjoint models	264	(12)
Appendix C Post-processing of numerical model output to obtain station weather forecasts	276	(7)
References	283	(45)
Index	328