Abstract: This paper provides a synergetic approach between numerical modeling and remote sensing of bio-optical water properties. The work demonstrates that appropriate data-assimilation schemes make numerical modeling a suitable and reliable tool for filling the gaps arising due to satellite imagery unavailability and/or cloud covering. In this research we apply the Princeton Ocean Model to the Sea of Azov, assimilating bio-optical indexes ( index 34 and b bp (555)) from MODIS L2 products. These data identify the presence of suspended matter (mineral s...
(read more)
Topics: 
Meteorology
Remote sensing