|Main scientific achievements
||For the first time, statistical and spectral characteristics of sea turbulence were comprehensively investigated in the open part of the Caspian Sea; general regularities of dissemination and distribution of pollutants in the sea were identified. A semi-empirical model has been developed to determine the upper temperature of quasi-homogeneous layer for the known state of the heat flux from the sea surface, of the wind speed and direction at the sea surface. The effect of the variability of hydro physical parameters of spread of pollutants in the Caspian Sea was studied in a wide range. Physical and geographical distribution model of pollutants has been developed in various meteorological conditions, taking into account the morphometry of the day and the configuration line of the Caspian Sea coast; a long-term forecast was given in the first approach after investigation of the cause of changes in the level and determination of the degree of the impact of influencing factors. Hydrometeorological Atlas of the Caspian Sea was compiled and published. A number of features of distribution in the water area of sea surface temperature and surface currents were identified on the basis of drifter data - factors affecting the Caspian Sea level fluctuations. According to satellite altimetry from Orbital Jason 1, Jason 2 and ENVİSAT, study of the amplitude of seasonal changes in the Caspian Sea level in the entire water area and distribution of phases by respective tracks, at research time. And according to these data, the study of the seasonal changes in sea level and the water level regime in the river Volga. According to the model MERRA of the Goddard Research Center of the US National Aeronautics and Space Administration (NASA), the assessment of precipitation over various areas of the Caspian Sea water area in different times of the Year on the basis of satellite data by comparison with the corresponding ground data. Conducting series of long-term veyvlet analysis on the software MatLab complex. Based on cross-veylvet and veylvet-covariant analysis, determining the of correlation degree covariance in different periods of time and on the basis of different data-level from the stations. Identify elements that can play the role of abrupt changes of level indicators.