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E-raamat: Numerical Weather and Climate Prediction

(National Center for Atmospheric Research, Boulder, Colorado)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 02-Dec-2010
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9780511922770
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  • Formaat: PDF+DRM
  • Ilmumisaeg: 02-Dec-2010
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9780511922770
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"This textbook provides a comprehensive yet accessible treatment of weather and climate prediction, for graduate students, researchers, and professionals. It teaches the strengths, weaknesses, and best practices for the use of atmospheric models. It is ideal for the many scientists who use such models across a wide variety of applications. The book describes the different numerical methods, data assimilation, ensemble methods, predictability, land surface modeling, climate modeling and downscaling, computational fluid-dynamics models, experimental designs in model-based research, verification methods, operational prediction, and special applications such as air-quality modeling and flood prediction. The book is based on a course that the author has taught for over 30 years at the Pennsylvania State University and the University of Colorado at Boulder, and also benefits from his wide practical modeling experience at the US National Center for Atmospheric Research. This volume will satisfy everyone who needs to know about atmospheric modeling for use in research or operations. It is ideal both as a textbook for a course on weather and climate prediction and as a reference text for researchers and professionals from a range of backgrounds: atmospheric science, meteorology, climatology, environmental science, geography, and geophysical fluid mechanics/dynamics"--

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Arvustused

'Numerical Weather and Climate Prediction is an excellent book for those who want a comprehensive introduction to numerical modeling of the atmosphere and Earth system, whether their interest is in weather forecasting, climate modeling, or many other applications of numerical models. The book is comprehensive, well written, and contains clear and informative illustrations.' Dr Richard A. Anthes, President, University Corporation for Atmospheric Research, Boulder 'Tom Warner's book is a rich, effectively written and comprehensive detailed summary of the field of atmospheric modeling from local to global scales. It should be in the library of all meteorologists, climate researchers, and other scientists who are interested in the capabilities, strengths and weaknesses of modeling.' Professor Roger A. Pielke, Sr, Colorado State University, Fort Collins '[ This book] covers all aspects of modeling one might expect, such as numerical techniques, but also some that might be unexpected such as ensemble modeling, initialization, and error growth. Today most students have become model users instead of model developers. Fewer and fewer peer into the models they use beyond the narrow regions that may directly interest them. With hundreds of thousands of lines of code, and groups of developers working on individual parts of the code, very few can say they truly understand all the parts of a model. Professor Warner's textbook should help both the student and the more advanced user of codes better appreciate and understand the numerical models that have come to dominate atmospheric science.' Professor Brian Toon, University of Colorado, Boulder 'Tom [ Warner]'s new book covers an impressive range of need-to-know material spanning traditional and cutting-edge atmospheric modeling topics. It should be required reading for all model users and aspiring model developers, and it will be a required text for my NWP students.' Professor David R. Stauffer, Pennsylvania State University 'The book addresses many practical issues in modern numerical weather prediction. It is particularly suitable for the students and scientists who use numerical models for their research and applications. While there have already been a few excellent textbooks that provide fundamental theory of NWP, this book offers complementary materials, which is useful for [ the] understanding of key components of operational numerical weather forecasting.' Professor Zhaoxia Pu, University of Utah ' gives a bird's eye view of the phenomenon of numerical weather prediction and that view is superb The illustrations are brilliant A more accessible, yet unpatronising treatment you will probably struggle to find It is such a nice read that it may even be worth a purchase for the more experienced of you. If not, [ it] is nonetheless an excellent introduction to the topic ' Meteorologische Zeitschrift

Muu info

Winner of Outstanding Publication Award, University Corporation for Atmospheric Research (UCAR) 2011.Provides a comprehensive yet accessible treatment of computer-based weather and climate prediction, for graduate students, researchers and professionals.
Preface xi
Acronyms and abbreviations xiii
Principal symbols xviii
1 Introduction
1(5)
2 The governing systems of equations
6(11)
2.1 The basic equations
6(1)
2.2 Reynolds' equations: separating unresolved turbulence effects
7(3)
2.3 Approximations to the equations
10(7)
3 Numerical solutions to the equations
17(102)
3.1 Overview of basic concepts
17(6)
3.2 Numerical frameworks
23(28)
3.3 Finite-difference methods
51(7)
3.4 Effects of the numerical approximations
58(38)
3.5 Lateral-boundary conditions
96(18)
3.6 Upper-boundary conditions
114(2)
3.7 Conservation issues
116(1)
3.8 Practical summary of the process for setting up a model
116(3)
4 Physical-process parameterizations
119(52)
4.1 Background
119(2)
4.2 Cloud microphysics parameterizations
121(8)
4.3 Convective parameterizations
129(11)
4.4 Turbulence, or boundary-layer, parameterizations
140(15)
4.5 Radiation parameterizations
155(11)
4.6 Stochastic parameterizations
166(1)
4.7 Cloud-cover, or cloudiness, parameterizations
166(5)
5 Modeling surface processes
171(27)
5.1 Background
171(1)
5.2 Land-surface processes that must be modeled
172(13)
5.3 Ocean or lake processes that must be modeled
185(2)
5.4 Modeling surface and subsurface processes over land
187(5)
5.5 Modeling surface and subsurface processes over water
192(1)
5.6 Orographic forcing
192(2)
5.7 Urban-canopy modeling
194(2)
5.8 Data sets for the specification of surface properties
196(2)
6 Model initialization
198(54)
6.1 Background
198(1)
6.2 Observations used for model initialization
199(11)
6.3 Continuous versus intermittent data-assimilation methods
210(5)
6.4 Model spinup
215(1)
6.5 The statistical framework for data assimilation
216(11)
6.6 Successive-correction methods
227(3)
6.7 Statistical interpolation (optimal interpolation)
230(1)
6.8 Three-dimensional variational analysis
231(2)
6.9 Diabatic-initialization methods
233(3)
6.10 Dynamical balance in the initial conditions
236(6)
6.11 Advanced data-assimilation methods
242(6)
6.12 Hybrid data-assimilation methods
248(1)
6.13 Initialization with idealized conditions
249(3)
7 Ensemble methods
252(32)
7.1 Background
252(2)
7.2 The ensemble mean and ensemble dispersion
254(3)
7.3 Sources of uncertainty, and the definition of ensemble members
257(4)
7.4 Interpretation and verification of ensemble forecasts
261(8)
7.5 Calibration of ensembles
269(2)
7.6 Time-lagged ensembles
271(1)
7.7 Limited-area, short-range ensemble forecasting
272(1)
7.8 Graphically displaying ensemble-model products
273(7)
7.9 Economic benefits of ensemble predictions
280(4)
8 Predictability
284(10)
8.1 Background
284(1)
8.2 Model error and initial-condition error
284(3)
8.3 Land-surface forcing's impact on predictability
287(1)
8.4 Causes of predictability variations
288(2)
8.5 Special predictability considerations for limited-area and mesoscale models
290(2)
8.6 Predictability and model improvements
292(1)
8.7 The impact of post processing on predictability
293(1)
9 Verification methods
294(27)
9.1 Background
294(1)
9.2 Some standard metrics used for model verification
295(4)
9.3 More about reference forecasts and their use
299(1)
9.4 Truth data sets: observations versus analyses of observations
300(1)
9.5 Special considerations
301(5)
9.6 Verification in terms of probability distribution functions
306(1)
9.7 Verification stratified by weather regime, time of day, and season
307(2)
9.8 Feature-based, event-based, or object-based verification
309(3)
9.9 Verification in terms of the scales of atmospheric features
312(5)
9.10 The use of reforecasts for model verification
317(1)
9.11 Forecast-value-based verification
317(1)
9.12 Choosing appropriate verification metrics
317(1)
9.13 Model-verification toolkits
318(1)
9.14 Observations for model verification
318(3)
10 Experimental design in model-based research
321(22)
10.1 Case studies for physical-process analysis
321(2)
10.2 Observing-system simulation experiments
323(5)
10.3 Observing-system experiments
328(1)
10.4 Big-Brother-Little-Brother experiments
329(1)
10.5 Reforecasts
330(1)
10.6 Sensitivity studies
331(7)
10.7 Predictive-skill studies
338(1)
10.8 Simulations with synthetic initial conditions
339(1)
10.9 The use of reduced-dimension and reduced-physics models
339(1)
10.10 Sources of meteorological observational data
340(3)
11 Techniques for analyzing model output
343(15)
11.1 Background
343(1)
11.2 Graphical methods for displaying and interpreting model output and observations
343(9)
11.3 Mathematical methods for analysis of the structure of model variable fields
352(4)
11.4 Calculation of derived variables
356(1)
11.5 Analysis of energetics
356(2)
12 Operational numerical weather prediction
358(8)
12.1 Background
358(2)
12.2 Model reliability
360(1)
12.3 Considerations for operational limited-area models
361(1)
12.4 Computational speed
361(1)
12.5 Post processing
362(1)
12.6 Real-time verification
363(1)
12.7 Managing model upgrades and developments
363(1)
12.8 The relative role of models and forecasters in the forecasting process
364(2)
13 Statistical post processing of model output
366(12)
13.1 Background
366(1)
13.2 Systematic-error removal
367(8)
13.3 Weather generators
375(1)
13.4 Downscaling methods
376(2)
14 Coupled special-applications models
378(23)
14.1 Background
378(3)
14.2 Wave height
381(1)
14.3 Infectious diseases
382(4)
14.4 River discharge, and floods
386(3)
14.5 Transport, diffusion, and chemical transformations of gases and particles
389(4)
14.6 Transportation safety and efficiency
393(1)
14.7 Electromagnetic-wave and sound-wave propagation
394(2)
14.8 Wildland-fire probability and behavior
396(1)
14.9 The energy industry
396(3)
14.10 Agriculture
399(1)
14.11 Military applications
399(2)
15 Computational fluid-dynamics models
401(6)
15.1 Background
401(1)
15.2 Types of CFD models
401(1)
15.3 Scale distinctions between mesoscale models and LES models
402(1)
15.4 Coupling CFD models and mesoscale models
403(2)
15.5 Examples of CFD-model applications
405(1)
15.6 Algorithmic approximations to CFD models
405(2)
16 Climate modeling and downscaling
407(49)
16.1 Global climate prediction
408(23)
16.2 Reanalyses of the current global climate
431(1)
16.3 Climate downscaling
432(19)
16.4 Modeling the climate impacts of anthropogenic landscape changes
451(5)
Appendix Suggested code structure and experiments for a simple shallow-fluid model 456(5)
References 461(62)
Index 523
Tom Warner was a Professor in the Department of Meteorology at the Pennsylvania State University before accepting his current joint appointment with the National Center for Atmospheric Research and the University of Colorado in Boulder, Colorado. His career has involved teaching and research in numerical weather prediction and mesoscale meteorological processes. He has published on these and other subjects in numerous professional journals. His recent research and teaching has focused on atmospheric processes, operational weather prediction, and arid-land meteorology. He is the author of Desert Meteorology (2004), also published by Cambridge University Press.