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Atmospheric Modeling, Data Assimilation and Predictability [Hardback]

4.09/5 (11 ratings by Goodreads)
(University of Maryland, College Park)
  • Formāts: Hardback, 364 pages, height x width x depth: 255x180x24 mm, weight: 926 g, 4 Tables, unspecified; 12 Halftones, unspecified; 74 Line drawings, unspecified
  • Izdošanas datums: 07-Nov-2002
  • Izdevniecība: Cambridge University Press
  • ISBN-10: 0521791790
  • ISBN-13: 9780521791793
Citas grāmatas par šo tēmu:
  • Hardback
  • Cena: 106,73 €
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  • Formāts: Hardback, 364 pages, height x width x depth: 255x180x24 mm, weight: 926 g, 4 Tables, unspecified; 12 Halftones, unspecified; 74 Line drawings, unspecified
  • Izdošanas datums: 07-Nov-2002
  • Izdevniecība: Cambridge University Press
  • ISBN-10: 0521791790
  • ISBN-13: 9780521791793
Citas grāmatas par šo tēmu:
This comprehensive text and reference work on numerical weather prediction, first published in 2002, covers 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 science known as data assimilation). Finally, this book provides a clear discussion of the problems of predictability and chaos in dynamical systems and how they can be applied to atmospheric and oceanic systems. Professors and students in meteorology, atmospheric science, oceanography, hydrology and environmental science will find much to interest them in this book, which can also form the basis of one or more graduate-level courses.

Recenzijas

' a frisson of excitement accompanied the rumour that Eugenia Kalnay was writing a new book. Expectations were high, since she is a renowned expert in the field. She has not disappointed us.' Science and Technology ' quite wonderful, achieving a tremendous balance between comprehensiveness and readability. I am especially pleased with the numerical analysis part, which is crystal clear and shows the benefits of classroom testing. I also like the tiny little touches, like the stepped-on butterfly story and the mention that Poincaré knew about chaos in celestial mechanics. Your book fills an enormous hole in the literature of NWP [ numerical weather prediction].' Richard C. J. Somerville, Scripps Institution of Oceanography, San Diego 'Fantastic in content, format and practicability.' Kelvin K. Droegemeier, Regents' Professor of Meteorology, and Director, Center for Analysis and Prediction of Storms, University of Oklahoma '[ I] admire the clarity and pedagogic superiority of [ this] presentation.' Anders Persson, Swedish Meteorological and Hydrological Institute (SMHI) ' much better for learning about data assimilation than anything else currently available.' Richard Swinbank, United Kingdom Meteorological Office ' [ the] presentation is impeccable and is very accessible to non-meteorologists like me.' Eric Kostelich, University of Arizona ' what a great wealth of historical information.' Lawrence Takacs, NASA, Data Assimilation Office ' a delight to read It will be of great assistance to our community and should greatly encourage young scientists who may be thinking of entering the field the book will be of considerable value to people who are unable or unwilling to cope with mathematical technicalities. They can gain much by studying the expository sections of the text.' Peter Lynch, Assistant Director, Irish Weather Service ' [ the] method in the [ data] assimilation section of starting with 'baby' examples, and then working up through the full analysis, is great for understanding. On the predictability part, the history, and the explanations of how the unstable perturbations grow is the best I've seen.' Alexander E. MacDonald, Director, NOAA Forecast Systems Lab ' this book is extremely useful, informative, and well-written there are many instances where items that were only marginally familiar beforehand have now become very clear.' Brian O. Blanton, Senior Scientist/Oceanographer, University of North Carolina, Chapel Hill

Papildus informācija

This book, first published in 2002, is a graduate-level text on numerical weather prediction, including atmospheric modeling, data assimilation and predictability.
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


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.