Ocean data assimilation systems encompass a wide range of scalesthat are difficult to control simultaneously using partial observationnetworks. All scales are not observable by all observation systems, which isnot easily taken into account in current ocean operational systems. The mainreason for this difficulty is that the error covariance matrices are usuallyassumed to be local (e.g. using a localisation algorithm in ensemble dataassimilation systems), so that the large-scale patterns are removed from theerror statistics. To better exploit the observational information available for all scales inthe assimilation systems of the Copernicus Marine Environment MonitoringService, we investigate a new method to introduce scale separation in theassimilation scheme. The method is based on a spectral transformation of the assimilation problemand consists in carrying out the analysis with spectral localisation for thelarge scales and spatial localisation for the residual scales. The target isto improve the observational update of the large-scale components of thesignal by an explicit observational constraint applied directly on the largescales and to restrict the use of spatial localisation to the small-scalecomponents of the signal. To evaluate our method, twin experiments are carried out with syntheticaltimetry observations (simulating the Jason tracks), assimilated in a1/4∘ model configuration of the North Atlantic and the Nordic Seas. Results show that the transformation to the spectral domain and the spectrallocalisation provides consistent ensemble estimates of the state of thesystem (in the spectral domain or after backward transformation to thespatial domain). Combined with spatial localisation for the residual scales,the new scheme is able to provide a reliable ensemble update for all scales,with improved accuracy for the large scale; and the performance of the systemcan be checked explicitly and separately for all scales in the assimilationsystem.
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