Assessment of sea surface temperature observational networks in the Baltic Sea and North Sea

The satellite and in situ Sea Surface Temperature (SST) observational networks in the Baltic Sea and North Sea are evaluated based on the quality of the gridded SST products generated from the networks. A multi-indicator approach is applied in the assessment. It includes evaluation of data quality, effective data coverage, field reconstruction error and model nowcast error. The results show that the best available full-coverage SST product is generated by assimilating the SST observations to obtain a yearly mean model bias of 0.07 °C and RMSE of 0.64 °C. The effective data coverage rate is 31% by using AVHRR (Advanced Very High Resolution Radiometer) data from NOAA (National Ocean and Atmosphere Administration) satellites 12, 14 and 16. The data redundancy increases rapidly with the number of infrared sensors. Using either NOAA satellite 12 or all 3 satellites makes a small difference with regard to derived effective coverage and the ocean model nowcast error. The influence of using the in situ SST observations in the SST field reconstruction is negligibly small. Instead, the major role of in situ SST observations is in calibrating the satellite observations. To study the relative importance of data quality and data coverage, an assessment is done for two satellite products: one product is based entirely on NOAA 12 data and has larger coverage but lower quality. The other product is a subset of the SAF products (derived from NOAA 14 and 16) and has lower coverage but higher quality. Based on current monitoring, modelling and assimilation technology, the results suggest that the data quality is an important factor in further improving the quality of the gridded SST products. Recommendations are made for possible further improvements of the existing SST observational networks.