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

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