Journal of Conference Abstracts

Environmental Forecasting

Julian Hunt (ah208@damtp.cam.ac.uk)

DAMTP, Silver Street, Cambridge, CB3 9EW, U.K.

The fundamental assumptions and current methodologies of the two main kinds of environmental forecast are reviewed; the first kind 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, at 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/or larger space-time targets, so that they only depned on the statistics of the initial or '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 modelling. These are, as a rule, focussed on specific quantities required for the forecast. The persistence and predictability of events associated with environmental and turbulent flows and the reasons for variation in the accuracy of their forecast (of the first and second kind) are now better understood and better modelled. This has partly resulted from using analogous results of disordered chaotic systems, and using the techniques of calculating ensembles of realisation, ideally involving several different models, so as to incorporate in the probabilistic forecast a wider range of possible events. The rationale for such an approach is discussed.

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' anti-cyclones) to small scale vortices. They explain the linear growth of errors in medium range (up to 5 day) large scale forecasts. Their tendency to shelter themselves from external disturbances through the adjustment of the vorticity in their surrounding of shear layers is an important mechanism that helps ensure their persistence and their universality. Note that these 'eigen-states' can be predicted from the reductionist models or for some processes may be modelled using general principles about complex systems such as 'self organised' critical phenomena.