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. The tested data assimilation (DA) method based on EOF(Empirical Orthogonal Functions) reconstruction of observations decreasedcentred root-mean-square difference (RMSD) of surface temperature (SST) andsalinity (SSS) in reference to observations in the NE Baltic Sea by 22 %and 34 %, respectively, compared to the control run without DA. The methodis based on the covariance estimates from long-term model data. Theamplitudes of the pre-calculated dominating EOF modes are estimated frompoint observations using least-squares optimization; the method builds thevariables on a regular grid. The study used a large number of in situ FerryBoxobservations along four ship tracks from 1 May to 31 December 2015, andobservations from research vessels. Within DA, observations werereconstructed as daily SST and SSS maps on the coarse grid with a resolutionof 5  ×  10 arcmin by N and E (ca. 5 nautical miles) andsubsequently were interpolated to the fine grid of the prognostic model witha resolution of 0.5  ×  1 arcmin by N and E (ca. 0.5 nauticalmiles). The fine-grid observational fields were used in the DA relaxationscheme with daily interval. DA with EOF reconstruction technique was foundto be feasible for further implementation studies, since (1) the method that workson the large-scale patterns (mesoscale features are neglected by taking onlythe leading EOF modes) improves the high-resolution model performance by acomparable or even better degree than in the other published studies, and (2) themethod is computationally effective.

ocean science

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