Predictability and Nonlinear Modelling in Natural Sciences and Economics

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Johan Grasman, Gerrit van Straten
Kluwer Academic Publishers, 1994 - Mathematics - 653 pages
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Researchers in the natural sciences are faced with problems that require a novel approach to improve the quality of forecasts of processes that are sensitive to environmental conditions. Nonlinearity of a system may significantly complicate the predictability of future states: a small variation of parameters can dramatically change the dynamics, while sensitive dependence of the initial state may severely limit the predictability horizon. Uncertainties also play a role here.
This proceedings volume addresses such problems by using tools from chaos theory and systems theory, which are adapted for the analysis of problems in the environmental sciences. Sensitive dependence on the initial state (chaos) and the parameters are analyzed using various methods, for example Lyapunov exponents and Monte Carlo simulation. Uncertainty in the structure and the values of parameters of a model is studied in relation to processes that depend on the environmental conditions. These methods also apply to biology and economics.
Audience: Research workers at universities and (semi-) governmental institutes for the environment, agriculture, ecology, meteorology and water management, and theoretical economists.

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The Liouville equation and prediction of forecast skill
An improved formula to describe error growth in meteorological models

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