Książki

 

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.

Discusses this generation of knowledge by computer models from an epistemological perspective and illustrates it by numerous examples from applied and fundamental research.

Includes ample technical appendices and is a valuable source of information for graduate students and scientists alike working in the field of environmental sciences.

Computer Modeling in Atmospheric and Oceanic Sciences: Building Knowledge

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.2 Filtering.- 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.