Application of 3-D ensemble variational data assimilation to a Baltic Sea reanalysis 19892013

Abstract

A B S T R A C T A 3-D ensemble variational (3DEnVar) data assimilation method has been implemented and tested for oceanographic data assimilation of sea surface temperature (SST), sea surface salinity (SSS), sea ice concentration (SIC), and salinity and temperature profiles. To damp spurious long-range correlations in the ensemble statistics, horizontal and vertical localisation was implemented using empirical orthogonal functions. The results show that the 3DEnVar method is indeed possible to use in oceanographic data assimilation. So far, only a seasonally dependent ensemble has been used, based on historical model simulations. Near-surface experiments showed that the ensemble statistics gave inhomogeneous and anisotropic horizontal structure functions, and assimilation of real SST and SIC fields gave smooth, realistic increment fields. The implementation was multivariate, and results showed that the cross-correlations between variables work in an intuitive way, for example, decreasing SST where SIC was increased and vice versa. The profile data assimilation also gave good results. The results from a 25-year reanalysis showed that the vertical salinity and temperature structure were significantly improved, compared to both dependent and independent data.

Sea level variability in the Swedish Exclusive Economic Zone and adjacent seawaters: influence on a point absorbing wave energy converter

“…In order to compensate for those deficiencies, observations are combined with model simulations to obtain a homogeneous data set with high resolution in time and space, and reasonably close to observations. This can be achieved with a process called data assimilation, in which observations are used to update the circulation model to keep it from deviating too far away from reality (Axell and Liu, 2016).…”

“…However, the differences in currents and SSH compared to a free run without data assimilation are rather small. For more information regarding the model description and validation, see Axell and Liu (2016) and the product documentation (Copernicus, 2018). In general, the results obtained for SSH in the SEEZ and the adjacent seawaters are rather good: mean correlations of about 0.91 and mean root mean square (rms) errors of about 9 cm are calculated by comparing hourly instantaneous model data with corresponding coastal observations for three different years.…”

“…The same problem is experienced by other technologies, such as oscillating water columns, as suggested by Muetze and Vining (2006) and by López et al (2015), and in more general terms by WECs which have a part that is fixed in position relative to the sea bed and a part that moves with the waves. Well-known point absorbers, such as Carnegie CETO (Kenny, 2014), Ocean Power Technologies Powerbuoy (OPT, 2018) and Archimedes Wave Swing (Beirdol et al, 2007), are challenged by SL changes, either because of a limited stroke length or because of the exponential decrease in available energy with depth.…”

Sea Level Variability in the Swedish Exclusive Economic Zone and adjacent seawaters: Influence on a Point Absorbing Wave Energy Converter

“…In order to compensate for those deficiencies, observations are combined with model simulations to obtain a homogeneous data set with high resolution in time and space, and reasonably close to observations. This can be achieved with a process called data assimilation, in which observations are used to update the circulation model to keep it from deviating too far away from reality (Axell and Liu, 2016 (Axell and Liu, 2016) and the product documentation (Copernicus, 2018). In general, the results obtained for MSL in the SEEZ and the adjacent seawaters are rather good: mean correlations of about 0.91 and mean RMS errors of about 9 cm are calculated by comparing hourly instantaneous model data with corresponding coastal observations for three different years.…”

Combining Satellite Imagery and Numerical Modelling to Study the Occurrence of Warm Upwellings in the Southern Baltic Sea in Winter

“…An alternative approach involves an iteration-based solution of the cost function utilising variance-based methods such as the 3DVar and 4DVar [64]. Many of those methods were applied to hydrodynamic models of the Baltic Sea to assimilate both the point source and the satellite data [65][66][67][68][69].…”