2014 , Volume 19, № 3, p.27-37

Environmental assessment using the observational data is one of the most pressing problems at the moment. Such an assessment is carried out with the involvement of mathematical models based on data assimilation systems. Kalman filter algorithm is currently one of the most popular approaches to solve the problem of the data assimilation. Ensemble Kalman filter is supposed to be a promising direction in the research on the application of the Kalman filter in the data assimilation. This article outlines currently accepted approaches to solving the problem of data assimilation for the environment, which are based on the ensemble Kalman filter. Along with the approximate description of covariance of the estimation errors using ensemble forecasts and on the basis of the control theory we propose to use sub-optimal algorithms. In these algorithms, the probability averaging is replaced by the time-averaging relied on the assumption of ergodicity of the forecast errors. An application of the proposed assimilation algorithm is considered in the examples given in this article. We deal with the data on passive gas components in the atmosphere and with the problem of simulating processes in the ocean. The numerical results, obtained by use of data assimilation, are analyzed in the case of modeling of passive gas components in the atmosphere over the Siberian region, as well as in the case of modeling of the summer runoff in the Laptev Sea. The simulated data are considered in both examples, taking the actual distribution of observational data. In both examples, the effectiveness of the proposed version of the ensemble Kalman filter is proved.