Virtual Posters

The study of the mesoscale processes, such as polar lows over the Nordic seas has become especially important due to the significant sea ice decrease in the Arctic Ocean in recent years. Polar lows are mesoscale, typically 100-1000 km in diameter, maritime weather systems with a relatively short life time from 3 to 36 hours. The most intense form of polar cyclones make up a small group referred to as polar lows. Polar low is a small, but fairly intense maritime cyclone that forms pole-ward of the main baroclinic zone. The polar lows can have a direct impact on the increased economic activity in the Arctic region. The polar lows can lead to the increase of marine production. Also they influence on the navigation and oil-gas industry. Therefore, one of the most important tasks for science now is an early detection and prediction of polar lows, investigation of their characteristics and track of their movements. Only the use of satellite data allows obtaining regular and spacious information about the polar lows. However the current meteorological observational network has severe limitations in detecting cyclones, especially small mesoscale cyclones. There is a strong need for new and/or improved methods to detect and monitor polar lows. Satellite remote sensing seems to be the most feasible tool for detection and monitoring of the polar lows. Several remote sensing sensors are capable to detect a polar low but each of them suffers from various deficiencies. In the work, satellite passive microwave data have been intensively exploited aiming at obtaining the fields of geophysical parameters inside the polar lows. DMSP SSM/I data were used in the research. Quantitative fields of geophysical parameters such as total atmospheric water vapour content, inside the polar lows were built using SSM/I data has estimated from 1995 to 2009 years over the Nordic seas. The polar lows have been identified on the satellite passive microwave imagery by means of the analysis of atmospheric water vapour fields. At NIERSC high accuracy Neural Network-based algorithms for atmospheric total water vapour content retrievals from SSM/I data over the open polar oceans have been developed tuned and validated (Bobylev et al., 2010). These algorithms were used for parameters estimation inside the polar lows over the Nordic seas. The application of such the method is the most attractive in the Polar Regions characterized by low water vapor, allowing accurate parameter estimation as opposed to tropical areas.