Reconstruction of the surface temperature fields according to the fragmentary data of remote sensing

We propose a gap-filling method for the data of remote sensing of the hydrophysical and biological characteristics of the water surface. The proposed method of reconstruction is based on the representation of the fields of surface characteristics as the sums of certain numbers of empirical orthogonal functions (EOF) making the largest contributions to the total variance of the field. According to the fragmentary data obtained as a result of processing of the satellite images for the summer season, we construct estimates of the mean field and of the four-dimensional space covariance function of the surface temperature of the Black Sea. The coefficients of expansion are computed by the method of least squares or determined with the help of a genetic searching algorithm. The results of numerical experiments show that the proposed method is quite promising for applications in the problems of gap filling in the available satellite data.     

The reconstruction of the structure of hygrophysical fields, using incomplete data

The paper suggests a method for reconstructing the spatial structure of hydrological elements, using incomplete information, with the noise component being concurrently filtered. This problem is of vital relevance in the context of conducting satellite observations when the observed area is covered by clouds. To solve the problem, the fields are decomposed by the system of empirical orthogonal functions. The reconstructed fields acquired by the different techniques using model andin situ data are then compared. As an example, the paper examines the reconstruction of the oxygen field, using the survey data compiled during the summer of 1986. The computations allow a deduction that the method at issue may be efficiently employed to handle the problems of field reconstruction, using scarce data, with the purpose of generating a data base on spatio-temporal variability of diverse environment components. Translated by Vladimir A. Puchkin.     

A method for the statistical prediction of hydrological and chemical fields,exemplified by computation of the oxygen content in the Sea of Azov surface layer

To predict the concentration of oxygen in the Sea of Azov, the paper relies on the decomposition of this parameter by a system of empirical orthogonal functions and on the prediction of decomposition coefficients via a grouped consideration of arguments. This method is applied to construct an optimal model for predicting the spatial distribution of oxygen in various seasons of the year, using a data on the mean seasonal dissolved oxygen concentration compiled at 32 reference stations occupied from 1958 to 1988. The experimentally derived numerical data indicate that the method can be effectively used to make short-term predictions. Translated by Vladimir A. Puchkin.