Data assimilation in the FOAM operational short‐range ocean forecasting system: a description of the scheme and its impact

Abstract

ABSTRACT:A detailed description of the data assimilation scheme used in the Forecasting Ocean Assimilation Model (FOAM) operational ocean forecasting system is presented. The theoretical basis for the scheme is an improved version of the analysis correction scheme, which includes information from previously assimilated data. The scheme requires the a priori specification of error covariance information for the background model field and the observations. The way in which these error covariances have been estimated is described and some examples are given. The FOAM system assimilates sea surface temperature, sea-level anomaly, temperature profile, salinity profile and sea-ice concentration data. Aspects of the scheme that are specific to each of these observation types are described.Two sets of experiments demonstrating the impact of the data assimilation are presented. The first set are in an idealized identical-twin setting, using the 1 9°-resolution North Atlantic FOAM configuration in which the state of the true ocean is assumed to be known. These experiments show that the analyses and forecasts are improved by assimilating the altimeter sea-level-anomaly data. The second set of experiments comprise data impact studies in a realistic hindcast setting. These experiments show a positive impact on the analyses from the Argo temperature-and salinity-profile data.  Crown

An ocean modelling and assimilation guide to using GOCE geoid products

“…Argo. See Martin et al (2007) for a more detailed description of FOAM. The along track altimeter SLA data is obtained from Collecte Localisation Satellites (CLS).…”

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“…As one of the outcomes of the GSOP intercomparison, [25] compares the variability in state estimates from multi-decadal syntheses ( [11], [28], [29], [30], [31] [32], [33], [34], [35], [36], [37]). There is a large spread in the various estimates of some quantities such as global upper ocean heat and freshwater content (Fig.…”

“…Another result that is common to many studies is the necessity of assimilation of altimeter data to represent mesoscale variability (e.g., [26], [33], [102]). Reference [103] has shown that four altimeters are needed in real time to get similar quality performance as two altimeters in delayed time.…”

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“…As discussed by MW10, the 1D implicit diffusion operator is closely linked to the recursive filter (Lorenc, 1992;Hayden and Purser, 1995), which has been developed extensively in meteorology for constructing correlation models in multiple dimensions (Wu et al, 2002;Purser et al, 2003aPurser et al, , 2003bLiu et al, 2007). The recursive filter has also been employed in ocean data assimilation systems (Martin et al, 2007;Dobricic and Pinardi, 2008;Liu et al, 2009).…”