Polynomial algorithm of the spatial forecast of atmospheric state parameters based on the Kalman filtering and its application

摘要

In the paper, the problem of spatial forecast of mesoscale fields at the point of space uncovered by meteorological information is discussed. The algorithms for estimating and forecasting the atmospheric parameters based on Kalman filtering theory. The offered algorithm takes into account horizontal statistical structure of a field at separate atmospheric levels and its time dynamics. The atmospheric parameter in a point is defined on the basis of a second-order polynomial model. The offered algorithm of the spatial forecast is investigated on the data long-term balloon observations for layer-by-layer averaging of temperature, zonal and meridional 1 wind velocity components.