  Using data alone, without knowledge of underlying physical models, nonlinear
discrete time regional forecasting dynamical rules are constructed employing
well tested methods from applied mathematics and nonlinear dynamics.
Observations of environmental variables such as wind velocity, temperature,
pressure, etc allow the development of forecasting rules that predict the
future of these variables only. A regional set of observations with appropriate
sensors allows one to forgo standard considerations of spatial resolution and
uncertainties in the properties of detailed physical models. Present global or
regional models require specification of details of physical processes globally
or regionally, and the ensuing, often heavy, computational requirements provide
information of the time variation of many quantities not of interest locally.
In this paper we formulate the construction of data driven forecasting (DDF)
models of geophysical processes and demonstrate how this works within the
familiar example of a 'global' model of shallow water flow on a mid-latitude
beta plane. A sub-region, where observations are made, of the global flow is
selected. A discrete time dynamical forecasting system is constructed from
these observations. DDF forecasting accurately predicts the future of observed
variables.
