Dynamic-stochastic spatial forecast of mesoscale temperature and wind fields as applied to estimation of technogenic pollution distribution

Original methodology and algorithms of spatial extrapolation of mesometeorological fields to the territory uncovered with observational data using the extended Kalman filter filter algorithm and the generalized dynamic-stochastic model of the spatiotemporal behavior of the parameters described by the first-order stochastic differential equations are considered. The results of statistical estimation of the quality of the suggested algorithms used for spatial prediction of the temperature and wind velocity fields on the mesoscale level are discussed.