An automatic moving window approach for mapping meteorological data

Abstract

Two distinctive characteristics of meteorological data are their global extent and their replication in time. Examples of such variables are rainfall, atmospheric pressure, temperature and humidity. Mapping these variables requires an initial interpolation of values on a regular grid. In Earth sciences, geostatistics is a widely used approach for this type of interpolation; generally, however, the variables have a local character, only one data set is available and the data are usually time independent. In this paper we show how these techniques can be adapted to take account of the global character and high spatial and temporal variability of meteorological data, by using a moving window approach to model selection and parameter inference within the framework of the universal kriging model. The universal kriging model is general because it includes the constant mean case as well as cases in which the mean is variable (in the terminology of geostatistics, a variable mean is known as drift). The latter is not unusual in meteorological variables such as rainfall, where topography, latitude or wind directions may produce definite patterns. The minimum Akaike information criterion is used for selecting a model for which the parameters are estimated by maximum likelihood. Finally, once a model has been selected (i.e. a value for the order of the drift and parameters of the semi-variogram or covariance function), universal kriging provides unbiased estimates and an assessment of the estimation variance. The implementation of this methodology is discussed and the methodology is illustrated by an application to mapping monthly rainfall over a large area of the west of the African continent. Copyright © 2005 Royal Meteorological Society.