A Matérn-Based Multivariate Gaussian Random Process for a Consistent Model of the Horizontal Wind Components and Related Variables

Abstract

AbstractThe integration of physical relationships into stochastic models is of major interest, for example, in data assimilation. Here, a multivariate Gaussian random field formulation is introduced that represents the differential relations of the two-dimensional wind field and related variables such as the streamfunction, velocity potential, vorticity, and divergence. The covariance model is based on a flexible bivariate Matérn covariance function for the streamfunction and velocity potential. It allows for different variances in the potentials, nonzero correlations between them, anisotropy, and a flexible smoothness parameter. The joint covariance function of the related variables is derived analytically. Further, it is shown that a consistent model with nonzero correlations between the potentials and positive definite covariance function is possible. The statistical model is fitted to forecasts of the horizontal wind fields of a mesoscale numerical weather prediction system. Parameter uncertainty is assessed by a parametric bootstrap method. The estimates reveal only physically negligible correlations between the potentials.