Meteorological data such as air temperature and rainfall are typically recorded at the ground level at a limited number of weather stations scattered over a region. Therefore, meteorological data values have to be estimated at intermediate unsampled locations in order to generate continuous surfaces for a whole area. Traditionally, two different approaches have been taken to create continuous surfaces of meteorological observations. The first approach builds on the fact that the spatial distribution of meteorological data is known to be dependent on relief. Thus, this approach aims at finding the functional relationship existing between a meteorological variable and elevation and other topographical covariates (e.g., slope orientation). The second approach, that of interpolation, makes use of the potentially existing spatial association between observations. In addition, several hybrid methods have been devised in an attempt to incorporate valuable information both from surrounding observations and terrain variables into the estimation process. However, the modelling process may become very cumbersome to apply when one tries to incorporate more than one terrain variable into a single estimation process when using methods such as thin plate splines, universal kriging or cokriging. More commonly, the relationship between relief and meteorological variable may be computed using linear regression, and then the residuals are interpolated using a geostatistical method such as ordinary kriging (e.g., residual kriging). These processes are combined when using partial thin plate splines. Whatever the method, flexibility is required when daily meteorological data are analyzed. The purpose of this paper is to demonstrate a flexible and effective method of mapping meteorological data which included both geographical and terrain covariates and neighbor observations in the estimation process. Here, feed-forward back-propagation neural networks are employed to estimate daily meteorological data values at unknown locations using terrain variables (e.g., easting, northing, elevation, aspect, roughness, distance to sea) derived from a digital elevation model (DEM) and observations from a long record made a

surrounding weather stations. Minimum air temperature and rainfall data for England and Wales are used to illustrate the methodology. Interpolated surfaces are compared with those obtained using ordinary kriging and thin plate splines. Results shown by the correlation between the estimated and observed values at a spatially independent test data set suggest that neural networks are an effective method for the spatial interpolation of daily meteorological data. In particular, they have the important advantage that only one model, incorporating both relief information and spatial covariance structure, has to be specified for a long daily meteorological record. The method also shows promise for spatial interpolation since it neither requires the specification of the function being modelled (e.g. linearity) nor makes underlying statistical assumptions about the data (e.g. normality).