Data assimilation of sea surface temperature and salinity using basin-scale reconstruction from empirical orthogonal functions: a feasibility study in the northeastern Baltic Sea

Abstract. The tested data assimilation (DA) method based on EOF (Empirical Orthogonal Functions) reconstruction of observations decreased centred root-mean-square difference (RMSD) surface temperature (SST) and salinity (SSS) in reference to the NE Baltic Sea by 22 % 34 %, respectively, compared control run without DA. is covariance estimates from long-term model data. amplitudes pre-calculated dominating modes are estimated point using least-squares optimization; builds variables a regular grid. study used large number situ FerryBox along four ship tracks 1 May 31 December 2015, research vessels. Within DA, were reconstructed as daily SST SSS maps coarse grid with resolution 5 × 10 arcmin N E (ca. nautical miles) subsequently interpolated fine prognostic 0.5 miles). fine-grid observational fields DA relaxation scheme interval. technique was found be feasible for further implementation studies, since (1) that works large-scale patterns (mesoscale features neglected taking only leading modes) improves high-resolution performance comparable or even better degree than other published (2) computationally effective.