This review describes the fundamental assumptions and current methodologies of the two main kinds of environmental forecast; the first is valid for a Limited period of time into the future and over a limited space-time 'target', and is largely determined by the initial and preceding state of the environment, such as the weather or pollution levels, up to the time when the forecast is issued and by its state at the edges of the region being considered; the second kind provides statistical information over long periods of time and/or over large space-time targets, so that they only depend on the statistical averages of the initial and 'edge' conditions. Environmental forecasts depend on the various ways that models are constructed. These range from those based on the 'reductionist' methodology (i.e., the combination of separate, scientifically based, models for the relevant processes) to those based on statistical methodologies, using a mixture of data and scientifically based empirical modeling. These are, as a rule, focused on specific quantities required for the forecast. The persistence and predictability of events associated with environmental and turbulent hows and the reasons for variation in the accuracy of their forecasts (of the first and second kinds) are now better understood and better modeled. This has partly resulted from using analogous results of disordered chaotic systems, and using the techniques of calculating ensembles of realizations, ideally involving several different models, so as to incorporate in the probabilistic forecasts a wider range of possible events. The rationale for such an approach needs to be developed. However, other insights have resulted from the recognition of the ordered, though randomly occurring, nature of the persistent motions in these flows, whose scales range from those of synoptic weather patterns (whether storms or 'blocked' anticyclones) to small scale vortices. These eigen states can be predicted from the reductionist models or may be modeled specifically, for example, in terms of 'self-organized' critical phenomena. It is noted how in certain applications of turbulent modeling its methods are beginning to resemble those of environmental simulations, because of the trend to introduce 'on-line' controls of the turbulent flows in advanced flows in advanced engineering fluid systems. In real time simulations, for both local environmental processes and these engineering systems, maximum information is needed about the likely flow patterns in order to optimize both the assimilation of limited real-time data and the use of limited real-time computing capacity. It is concluded that philosophical studies of how scientific models develop and of the concept of determinism in science are helpful in considering these complex issues. (C) 1999 Elsevier Science B.V. All rights reserved.
