Satellite remote sensing of oil spill pollution in the southeastern Baltic Sea

Shipping activities in the Baltic Sea, including oil transport and oil handled in harbors, have a number of negative impacts on the marine environment and coastal zone. Oil discharges from ships represent a significant threat to marine ecosystems. Oil spills cause the contamination of seawater, shores, and beaches, which may persist for several months and represent a threat to marine resources. One of the main tasks in the ecological monitoring of the Baltic Sea is an operational satellite and aerial detection of oil spillages, determination of their characteristics, establishment of the pollution sources and forecast of probable trajectories of the oil spill transport. Since 1993 there is no regular aerial surveillance of the oil spills in the Russian sector of the southeastern Baltic Sea. In June 2003 LUKOIL-Kaliningradmorneft initiated a pilot project, aimed to the complex monitoring of the southeastern Baltic Sea, in connection with a beginning of oil production at continental shelf of Russia. It was performed on the base of satellite remote sensing (AVHRR NOAA, SeaWiFS, MODIS, TOPEX/Poseidon, Jason-1, SAR imagery of ERS-2 and ENVISAT) of SST, sea level, chlorophyll concentration, mesoscale dynamics, wind and waves, and oil spills. A number of oil spills have been detected in the period between June 2003 and July 200

Propagation of the Black Sea Waters in the Sea of Azov Based on the Satellite Data and the NEMO Model

Purpose. The paper is purposed at studying the dynamics and reasons of the Black Sea water inflows to the Sea of Azov, as well as the features of their seasonal variability. Methods and Results. Medium and high resolution satellite data, and also the results of numerical modeling the salinity field of the Azov-Black Sea basin for 2008-2009 by the high resolution (1 km) NEMO model were used. The analysis showed that the transparent and salty Black Sea waters were recorded most frequently in the southern and southeastern parts of the Azov Sea during a cold season. Based on the satellite measurements, the maximum number of inflows was observed in November and March, and the minimum one – from June to October. Similar results were obtained from the data of numerical calculations for 2008-2009: in winter, intense salt water inflows to the Sea of Azov (the flow exceeds 20 tons/s) are observed in a third of cases, and in some cases, the estimated salt flux attains 60 tons/s, whereas in summer their number is close to zero. Further the Black Sea waters move predominantly in a cyclonic direction, sometimes reaching the basin center. In some cases, high density gradients induce the development of an intense cyclonic eddy near the strait at the front of the Black Sea water inflows. The simulation data made it possible to assess the relationship between the wind and the salt fluxes to the Sea of Azov. It is shown that this relationship is of a cubic nature that is partly explained by increase of the inflowing water salinity caused by the intensified vertical mixing during the storms. Conclusions. The main hydrodynamic reasons for the Black Sea water inflows to the Sea of Azov and their seasonal variability are the following: 1) intense wind transfer during the south winds; 2) frontal currents at the boundary of upwellings near the Kerch Peninsula during the western and southwestern winds; 3) orbital currents of the passing anticyclones which are able to induce a northerly water transport in the strait at any wind conditions

Analysis of the Black Sea surface currents retrieved from space imagery

International audienc

Analysis of the Black Sea surface currents retrieved from space imagery

International audienc

Retrieving ocean surface current by 4-D variational assimilation of sea surface temperature images

Remote Sensing Data Assimilation Special IssueInternational audienceIn this article we propose a new method to estimate ocean mesoscale structures of the surface current velocity by processing sea surface satellite images. Assuming that the intensity level can be described by a transport-diffusion equation, the proposed approach is based on variational assimilation of image observations within a simple transport-diffusion model. This approach permits to retrieve the current velocity field from a sequence of satellite images. Results of processing synthetic data and real NOAA-AVHRR satellite images are presented and commented