Comparative Evaluation of Algorithms for Spatial Interpolation of Atmospheric State Parameters Based on a Dynamic Stochastic Model Taking into Account the Vertical Variation of a Meteorological Field

Abstract

The paper presents a comparative analysis of two algorithms for the spatial interpolation of meteorological fields. Both algorithms are based on a four-dimensional low-order parametric dynamic stochastic model, taking into account the vertical variation of a meteorological field. The algorithms are characterized by different representations of the forecast model in state and observation space equations for the Kalman filter. The authors studied the accuracy of the spatial interpolation of temperature and wind fields for the developed algorithms. The results of the study are presented in this paper. Numerical simulation was conducted using long-term upper-air observations obtained for a typical mesometeorological range. The results of the study demonstrate that the accuracy of interpolation for the two considered algorithms is comparable.

Keywords

• kalman filter
• spatial interpolation
• data assimilation
• numerical simulation
• low-order parametric dynamic stochastic model