High quality SST data sets are need for various applications, including numerical weather prediction, ocean forecasting and climate research. The coverage, resolution and precision of individual SST satellite observations is not sufficient for these applications, therefore merging of these complementary data sets is needed to reduce the final data set error. This is usually performed by optimal interpolation (OI). Within HiSea we have extended the capabilities of DINEOF (Data INterpolating Empirical Orthogonal Functions) to merge data from different platforms, including polar-orbiting satellites, geostationary satellites and in situ data. The high spatial resolution of the polar-orbiting satellites and the high temporal resolution of the geostationary satellites are retained, and the EOF basis used by the analysis represents more realistically the complex variability of SST datasets than the parametric covariance used in most OI applications. The merged fields, and their error maps have been compared to independent data to assess their accuracy. HiSea has been applied to Mediterranean SST and to North Sea total suspended matter. These two examples showed that the developed technique can be applied to different variables and domains. We will show the most important results and conclusions reached through HiSea and discuss the perspectives for future developments.
103_HISEA_4.pdf (608.52 KB) TOMAZIC BECKERS ALVERA-AZCÁRATE Belgian Earth Observation Days 2013 HISEA