Application of nonlinear forecasting techniques for meteorological modeling

A nonlinear forecasting method was used to predict the behavior of a cloud coverage time series several hours in advance. The method is based on the r econstruction of a chaotic strange attractor using four years of cloud abso rption data obtained from half-hourly Meteosat infrared images from Northwe stern Spain. An exhaustive nonlinear analysis of the time series was carrie d out to reconstruct the phase space of the underlying chaotic attractor. T he forecast values are used by a non-hydrostatic meteorological model ARPS for daily weather prediction and their results compared with surface temper ature measurements from a meteorological station and a vertical sounding. T he effect of noise in the time series is analyzed in terms of the predictio n results.