During the past decade, the world's oceans have been systematically observed by orbiting spectroradiometers such as MODIS and MERIS. These sensors have generated a huge amount of data with unprecedented temporal and spatial coverage. The data is freely available, but not always accessible for marine researchers with no image processing experience. In order to provide historical and current oceanographic parameters for the jellyfish forecasting in the JELLYFOR project, a tool for the generalized processing and archiving of satellite data was created (GRIMAS). Using this generalized software, the large amount of remote sensing data can be accessed, and parameters such as chlorophyll a concentration (CHL), sea surface temperature (SST) and total suspended matter concentration (TSM) can be extracted and gridded for any region on earth. Time-series and climatologies can be easily extracted from this data archive. The products generated can be based on the standard products, as supplied by space agencies, or can be new or regionally calibrated products. All available MODIS and MERIS L2 images from an eight year period (2003-2010) were processed in order to create a gridded dataset of CHL, SST (MODIS only) and of TSM for the three JELLYFOR regions. For two of the regions, data for an extended region was also processed. Multi-year composites (climatologies) of satellite data and time-series can provide a wealth of information for different projects in any region. Climatologies from the two sensors are in good agreement, while significant differences can occur on a scene per scene basis. Total suspended matter concentrations match favourably with in situ data derived from sensors on autonomous buoys. MODIS sea surface temperature corresponds closely to temperature continuously measured underway on research vessels.