By adapting a flow-following physical framework to the statistical modeling of large spatio–temporal datasets, KAUST researchers have developed a more robust and realistic general method for dealing with wind-driven phenomena. The approach promises to greatly improve the accuracy of pollutant dispersion prediction by incorporating more physically realistic processes into geostatistical modeling.