Computational Methods For The Atmosphere And The Oceans

This book provides a survey of the frontiers of research in the numerical modeling and mathematical analysis used in the study of the atmosphere and oceans. The details of the current practices in global atmospheric and ocean models, the assimilation of observational data into such models and the numerical techniques used in theoretical analysis of the atmosphere and ocean are among the topics covered. • Truly interdisciplinary: scientific interactions between specialties of atmospheric and ocean sciences and applied and computational mathematics • Uses the approach of computational mathematicians, applied and numerical analysts and the tools appropriate for unsolved problems in the atmospheric and oceanic sciences• Contributions uniquely address central problems and provide a survey of the frontier of research

A compilation of the current state of the art in numerical modeling of the atmosphere and ocean.

This textbook introduces step by step the basic numerical methods to solve the equations governing the motion of the atmosphere and ocean, and describes how to develop a set of corresponding instructions for the computer as part of a code. Today's computers are powerful enough to allow 7-day forecasts within hours, and modern teaching of the subject requires a combination of theoretical and computational approaches. The presentation is aimed at beginning graduate students intending to become forecasters or researchers, that is, users of existing models or model developers. However, model developers must be well versed in the underlying physics as well as in numerical methods. Thus, while some of the topics discussed in the modeling of the atmosphere and ocean are more advanced, the book ensures that the gap between those scientists who analyze results from model simulations and observations and those who work with the inner works of the model does not widen further. In this spirit, the course presents methods whereby important balance equations in oceanography and meteorology, namely the advection-diffusion equation and the shallow water equations on a rotating Earth, can be solved by numerical means with little prior knowledge. The numerical focus is on the finite-difference (FD) methods, and although more powerful methods exist, the simplicity of FD makes it ideal as a pedagogical introduction to the subject. The book also includes suitable exercises and computer problems.

Numerical Methods for Atmospheric and Oceanic Sciences caters to the needs of students of atmospheric and oceanic sciences in senior undergraduate and graduate courses as well as students of applied mathematics, mechanical and aerospace engineering. The book covers fundamental theoretical aspects of the various numerical methods that will help both students and teachers in gaining a better understanding of the effectiveness and rigour of these methods. Extensive applications of the finite difference methods used in the processes involving advection, barotropic, shallow water, baroclinic, oscillation and decay are covered in detail. Special emphasis is given to advanced numerical methods such as Semi-Lagrangian, Spectral, Finite Element and Finite Volume methods. Each chapter includes various exercises including Python codes that will enable students to develop the codes and compare the numerical solutions obtained through different numerical methods.

This book is thesecond volume of a three volume series recording the "Radon Special Semester 2011 on Multiscale Simulation & Analysis in Energy and the Environment" that took placein Linz, Austria, October 3-7, 2011. This volume addresses the common ground in the mathematical and computational procedures required for large-scale inverse problems and data assimilation in forefront applications. The solution of inverse problems is fundamental to a wide variety of applications such as weather forecasting, medical tomography, and oil exploration. Regularisation techniques are needed to ensure solutions of sufficient quality to be useful, and soundly theoretically based. This book addresses the common techniques required for all the applications, and is thus truly interdisciplinary. Thiscollection of surveyarticlesfocusses onthe large inverse problems commonly arising in simulation and forecasting in the earth sciences. For example, operational weather forecasting models have between 107 and 108 degrees of freedom. Even so, these degrees of freedom represent grossly space-time averaged properties of the atmosphere. Accurate forecasts require accurate initial conditions. With recent developments in satellite data, there are between 106 and 107 observations each day. However, while these also represent space-time averaged properties, the averaging implicit in the measurements is quite different from that used in the models. In atmosphere and ocean applications, there is a physically-based model available which can be used to regularise the problem. We assume that there is a set of observations with known error characteristics available over a period of time. The basic deterministic technique is to fit a model trajectory to the observations over a period of time to within the observation error. Since the model is not perfect the model trajectory has to be corrected, which defines the data assimilation problem. The stochastic view can be expressed by using an ensemble of model trajectories, and calculating corrections to both the mean value and the spread which allow the observations to be fitted by each ensemble member. In other areas of earth science, only the structure of the model formulation itself is known and the aim is to use the past observation history to determine the unknown model parameters. The book records the achievements of Workshop2 "Large-Scale Inverse Problems and Applications in the Earth Sciences". Itinvolves experts in the theory of inverse problems together with experts working on both theoretical and practical aspects of the techniques by which large inverse problems arise in the earth sciences.

Large-scale changes are taking place in the way modelling is performed within the US EPA, and a new generation of environmental models is currently under construction. The US EPA is engaging in several modelling efforts in response to Congressional mandates such as the Clean Air Act and the Clean Water Act. These mandates require the scientific modelling of the impact of pollutants on human health and the environment. The complexity of scale in environmental models has increased by several orders of magnitude, with a simultaneous demand for increased stability, accuracy and efficiency in the computed model solution. This book showcases numerical algorithms appropriate to the subject areas listed below and explores how new algorithmic methods would benefit the US EPA's environmental models and other environmental studies.