Band 37, Heft 6

Algorithms for stochastic perturbation of parameters and tendencies of physical parameterizations for subgrid-scale processes are implemented into the ensemble prediction system. This system is based on the global semi-Lagrangian atmospheric model SL-AV with the resolution of 0.9 × 0.72 degrees in longitude and latitude, respectively, 96 vertical levels, and our implementation of the Local Ensemble Tranform Kalman Filter (LETKF). The use of stochastically perturbed parameterizations allows to generate ensembles with a significantly larger spread compared to one obtained with the method of static parameter perturbation. An improvement in the probabilistic estimates of the ensemble forecast for different seasons is shown.

A new INM RAS global dynamical model of Earth’s ionosphere F region (100–500 km), which takes into account plasma-chemical processes, ambipolar diffusion, and advective ion transport due to electromagnetic drifts and neutral wind is presented. The model includes parameterizations of polar electric fields induced by magnetospheric convection and simplified equatorial drifts considerations. The focus of the paper is directed on the description of specific methods developed and utilized in the ionospheric model. Key processes responsible for the formation of global ionospheric features are outlined and their representation in the model is evaluated. The main global ionospheric characteristic features, such as seasonal and diurnal cycles, the equatorial ionization anomaly (EIA), polar ionization caps and the main trough have been adequately reproduced based on this model.

The paper is focused on the study of the superexponential growth of the average number of particles in a stochastically homogeneous propagating medium. A mosaic Voronoi field (‘mosaic’) is considered as a random density model. The notion of ‘effective’ correlation radius is introduced to compare the results with previously obtained estimates of superexponential parameters for a spherically symmetric layered mosaic. It is shown that transition from the layered random density model to a chaotic one preserving the correlation scale and one-dimensional distribution weakens the ‘superexponential’ property of the particle flux.

The problem of variational observation data assimilation is considered for the mathematical thermodynamics model developed at the Marchuk Institute of Numerical Mathematics of RAS with the aim to reconstruct the sea surface heat flux. The sensitivity of functionals of solutions to observation data is studied for the considered variational assimilation problem and the results of numerical experiments for the Black Sea dynamics problem are presented.