Use of FerryBox surface temperature and salinity measurements to improve model based state estimates for the German Bight Sebastian Grayek a,b, ⁎, Joanna Staneva a , Johannes Schulz-Stellenfleth a , Willhelm Petersen a , Emil V. Stanev a a Institute for Coastal Research, GKSS Research Centre, Max-Planck-Strasse 1, 21502 Geesthacht, Germany b Institute for Chemistry and Biology of the Sea (ICBM), University of Oldenburg, Carl-von-Ossietzky-Strasse 9-11, D-26111 Oldenburg, Germany abstract article info Available online 4 March 2011 Keywords: FerryBox data Reconstruction of ocean state Temperature and salinity Data assimilation The potential of FerryBox sea surface temperature (SST) and salinity (SSS) measurements for the improvement of state estimates in the German Bight is investigated. The paper quests the hypothesis that the parallel analysis of remote sensing and FerryBox data, as well as data simulated by a numerical model, could increase the efficiency of using the information contained in the FerryBox data when producing state estimates. The analysis uses output from a 3-D primitive equation numerical model, up-to-date remote sensing products, and classical in-situ observations as complementary information. A Kalman filter approach is applied to extrapolate one-dimensional FerryBox data acquired along the ferry route from Cuxhaven to Immingham to larger two-dimensional areas. The method makes use of a priori information about the background statistics provided by a numerical model. Maps of extrapolation errors are presented. The impact of the special FerryBox sampling with a revisit time of typically 36 h is investigated based on synthetic data. In particular the aliasing problem associated with the M2 tidal signal is discussed. It is demonstrated that reasonable extrapolation errors can be achieved with a linear interpolation method in combination with a filter operation. Real FerryBox measurements acquired in 2007 are used for assimilation experiments with a 3-D primitive equation model. A standard optimal interpolation (OI) technique is applied for this purpose. The required background statistics are estimated from a free run performed with the model. It is demonstrated that an assimilation of FerryBox SST data leads to a qualitative improvement of the SST state estimates over large areas. Our analysis showed that the natural variability of SSS along the FerryBox track is small compared to the measurement errors and the errors resulting from the specific FerryBox sampling. The use of FerryBox SSS data in an assimilation system is therefore more demanding than the use of the respective SST data. Comparisons with independent observations demonstrate that the improvements in the SSS state estimates are more pronounced for synoptic and short time events. © 2011 Elsevier B.V. All rights reserved. 1. Introduction Baroclinic processes are still not sufficiently addressed in the numerical modelling of coastal oceans. Combining data from different observations (e.g., in-situ and remote sensing), or analysing the consistency between the numerical simulations and observations can contribute to a better understanding of the baroclinic dynamics, thus avoiding some shortcomings in coastal-ocean modelling. The recent FerryBox project supported by the European Union (Petersen et al., 2006) has proved successful in establishing the operational use of FerryBox system and supporting research develop- ment. In particular, the quantification of the water transport in the North Sea has been further improved. The benefit of linking remote sensing (satellite) observations with in-situ data (FerryBox measure- ments) is one motivation for the present study. It has in fact been shown by Petersen et al. (2008) that FerryBox data can provide valuable information over large areas when combined with other observations (e.g., remote sensed data). Furthermore, by providing real-time data, the FerryBox systems can contribute to an efficient assimilation into prognostic numerical models and improve their accuracy. Data assimilation in the coastal ocean (Lermusiaux, 1997; Echevin et al., 2000), which has been developing rapidly in recent years, still does not find as wide operational application as in the open ocean. Nevertheless, there is a number of examples demonstrating the good quality of the existing data and the potential of their use. For instance, Høyer and She (2004) computed error statistics for satellite Sea Surface Temperature (SST) observations in the region of the Baltic and the North Sea. Andreu-Burillo et al. (2007) used for the Irish Sea, which exhibits ocean conditions similar to the ones addressed in the present paper, the Proudman Oceanographic Laboratory Coastal Ocean Modelling System with a resolution of 1.8 km and estimated the impact of the data-to-model resolution difference using two different sets of satellite data. The impact of assimilating FerryBox data into numerical models has been studied for the Aegean Sea (Korres et al., Journal of Marine Systems 88 (2011) 45–59 ⁎ Corresponding author at: Institute for Coastal Research, GKSS Research Centre, Max-Planck-Strasse 1, 21502 Geesthacht, Germany. E-mail address: grayek@icbm.de (S. Grayek). 0924-7963/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.jmarsys.2011.02.020 Contents lists available at ScienceDirect Journal of Marine Systems journal homepage: www.elsevier.com/locate/jmarsys