Building Knowledge
Buch, Englisch, 304 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 712 g
ISBN: 978-3-540-20353-7
Verlag: Springer Berlin Heidelberg
Written by two veteran environmental modelers, this textbook is about quasi-realistic models in the atmospheric and oceanic sciences, which have become an indispensable tool. Understanding the basis and limitations of these models is important since far-reaching decisions about the environment are based on these models. The book describes what these models are, what they are based on, how they function, and then, most innovatively, how they can be used to generate new useful knowledge about the environmental system. This generation of knowledge by computer models is discussed from an epistemological perspective and illustrated by numerous examples from applied and fundamental research. Illustrated by numerous examples, from both applied and fundamental research, the text includes ample technical appendices and is a valuable source of information for graduate students and scientists alike working in the field of environmental sciences.
Zielgruppe
Graduate
Fachgebiete
Weitere Infos & Material
1 Introduction.- 2 Computer Models.- 3 Models and Data.- 4 The Dynamics of Tides and Climate.- 5 Modeling in Applied Environmental Sciences — Forecasting, Analysis and Scenarios.- 6 Modeling in Fundamental Environmental Sciences — Simulation and Hypothesis Testing.- 7 Issues and Conclusions.- Appendices.- A Fluid Dynamics.- A.1 The Balance Equations.- A.1.1 Mass Balances.- A.1.2 Momentum Balance.- A.1.3 Energy Balance.- A.2 Thermodynamic Specification.- A.3 The Phenomenological Flux Laws.- A.4 Boundary Conditions.- A.5 A Closer Look at the Balance Equations.- A.5.1 Cloud Formation.- A.5.2 Radiation.- A.5.3 Photochemical Reactions.- A.6 Reynolds Decomposition.- A.7 Parameterization of Interior Fluxes.- A.7.1 Eddy Diffusivities.- A.7.2 Eddy Viscosities.- A.8 Parameterization of Boundary Layer Fluxes.- A.8.1 The Constant Flux Layer.- A.8.2 The Planetary Boundary Layer.- A.9 Approximations.- A.9.1 Anelastic Approximation.- A.9.2 Shallow Water Approximation.- A.10 Representations.- A.10.1 Vertical Coordinates.- A.10.2 Decoupling.- B Numerics.- B.1 Discretization.- B.2 Partial Differential Equations.- B.2.1 Elliptic Problems.- B.2.2 Parabolic Problems.- B.2.3 Hyperbolic Problems.- B.3 Staggered Grids.- B.4 Spectral Models.- B.5 Finite Element Models.- C Statistical Analysis.- C.1 Random Variables and Processes.- C.1.1 Probability Function.- C.1.2 Bivariate Random Variables.- C.1.3 Random Processes.- C.2 Characteristic Parameters.- C.2.1 Expectation Values.- C.2.2 Empirical Orthogonal Functions.- C.2.3 Decomposition of Variance.- C.2.4 Skill Scores.- C.3 Inference.- C.3.1 Basic Aspects of Estimation.- C.3.2 Estimation of Auto-covariance Functions.- C.3.3 Estimation of Spectra.- C.3.4 Estimation of EOFs.- C.3.5 Hypothesis Testing.- D Data Assimilation.- D.1 Estimation.- D.2Filtering.- D.2.1 Kalman Filter.- D.2.2 Optimal or Statistical Interpolation.- D.2.3 Nudging.- D.2.4 Blending and Direct Insertion.- D.2.5 Minimization.- D.3 Smoothing.- D.3.1 Adjoint Method.- D.3.2 Inverse Method.- D.3.3 Parameter Estimation.- References.
