Abstract
Abstract. 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 × 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 × 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.
Highlights
In the coastal oceans and marginal seas, basin-scale observation, modelling and forecasting of oceanographic and biogeochemical variables is a continuing challenge
data assimilation (DA) methods are built upon dynamical models and they are based on some kind of minimization of modelling errors (Carrassi et al, 2018), using estimated statistical characteristics of the studied variables
The method relies on the estimate of covariance matrix from the long-term model data, which is decomposed into the full set of EOF modes
Summary
In the coastal oceans and marginal seas, basin-scale observation, modelling and forecasting of oceanographic and biogeochemical variables is a continuing challenge. A similar evaluation has been obtained earlier by Golbeck et al (2015), based on 13 operational models used routinely in the Baltic and North seas. Data assimilation (DA) is a key element to improve the model accuracy with respect to observations, both in the operational forecast and the reanalysis context (Martin et al, 2015; Buizza et al, 2018; Moore et al, 2019). DA methods are built upon dynamical models and they are based on some kind of minimization (minimum variance, variational cost function formulation etc.) of modelling errors (Carrassi et al, 2018), using estimated statistical characteristics of the studied variables. Whereas there are several results from Baltic Sea reanalysis studies available (Axell and Liu, 2016; Liu et al, 2017), the operational Baltic Sea forecasts within CMEMS
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