Assimilating temperature and salinity profile observations using an anisotropic recursive filter in a coastal ocean model

Liu, Ye; Zhu, Jiang; She, Jun; Zhuang, Shiyu; Fu, Weiwei; Gao, Jidong

doi:10.1016/j.ocemod.2009.06.005

Cited by 29 publications

(30 citation statements)

References 24 publications

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“…This has been demonstrated in some previous studies (e.g., Liu et al, 2009;Fu et al, 2011;Zhuang et al, 2011). Although results from these studies are encouraging, the experiments usually cover a relatively short period ranging from months to a year.…”

Section: Published By Copernicus Publications On Behalf Of the Europesupporting

confidence: 55%

A 20-year reanalysis experiment in the Baltic Sea using three-dimensional variational (3DVAR) method

She

Dobrynin

2012

Ocean Sci.

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Abstract.A 20-year retrospective reanalysis of the ocean state in the Baltic Sea is constructed by assimilating available historical temperature and salinity profiles into an operational numerical model with three-dimensional variational (3DVAR) method. To determine the accuracy of the reanalysis, the authors present a series of comparisons to independent observations on a monthly mean basis.In the reanalysis, temperature (T) and salinity (S) fit better with independent measurements than the free run at different depths. Overall, the mean biases of temperature and salinity for the 20 year period are reduced by 0.32 • C and 0.34 psu, respectively. Similarly, the mean root mean square error (RMSE) is decreased by 0.35 • C for temperature and 0.3 psu for salinity compared to the free run. The modeled sea surface temperature, which is mainly controlled by the weather forcing, shows the least improvements due to sparse in situ observations. Deep layers, on the other hand, witness significant and stable model error improvements. In particular, the salinity related to saline water intrusions into the Baltic Proper is largely improved in the reanalysis. The major inflow events such as in 1993 and 2003 are captured more accurately as the model salinity in the bottom layer is increased by 2-3 psu. Compared to independent sea level at 14 tide gauge stations, the correlation between model and observation is increased by 2 %-5 %, while the RMSE is generally reduced by 10 cm. It is found that the reduction of RMSE comes mainly from the reduction of mean bias. In addition, the changes in density induced by the assimilation of T/S contribute little to the barotropic transport in the shallow Danish Transition zone.The mixed layer depth exhibits strong seasonal variations in the Baltic Sea. The basin-averaged value is about 10 m in summer and 30 m in winter. By comparison, the assimilation induces a change of 20 m to the mixed layer depth in deep waters and wintertime, whereas small changes of about 2 m occur in summer and shallow waters. It is related to the strong heating in summer and the dominant role of the surface forcing in shallow water, which largely offset the effect of the assimilation.

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Section: Published By Copernicus Publications On Behalf Of the Europesupporting

confidence: 55%

A 20-year reanalysis experiment in the Baltic Sea using three-dimensional variational (3DVAR) method

She

Dobrynin

2012

Ocean Sci.

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“…In both data assimilation simulations, the time window for choosing observations is 3 d before and after assimilation time and the assimilating frequency is once every 7 d. For simplicity, the observations are considered to be uncorrelated (e.g. Liu et al, 2009). The observation error variances (diagonal elements of observational error covariance matrix) for individual observations are estimated according to the relative 'age' of each observation: …”

Section: Experimental Design and Configurationsmentioning

confidence: 99%

“…For this evaluation of model simulations, all quality-controlled observations are used. In REANA and REANAB, the d is calculated just before the assimilation analysis time and the corresponding observations are not yet assimilated into RCO-SCOBI (Liu et al, 2009). …”

Section: Root Mean Square Deviationsmentioning

confidence: 99%

Improving the multiannual, high-resolution modelling of biogeochemical cycles in the Baltic Sea by using data assimilation

Liu

Meier

Eilola

2014

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C TThe impact of assimilating temperature, salinity, oxygen, phosphate and nitrate observations on marine ecosystem modelling is assessed. For this purpose, two 10-yr (1970Á1979) reanalyses of the Baltic Sea are carried out using the ensemble optimal interpolation (EnOI) method and a coupled physical-biogeochemical model of the Baltic Sea. To evaluate the reanalyses, climatological data and available biogeochemical and physical in situ observations at monitoring stations are compared with results from simulations with and without data assimilation. In the first reanalysis, only observed temperature and salinity profiles are assimilated, whereas biogeochemical observations are unused. Although simulated temperature and salinity improve considerably as expected, the quality of simulated biogeochemical variables does not improve and deep water nitrate concentrations even worsen. This unexpected behaviour is explained by a lowering of the halocline in the Baltic proper due to the assimilation causing increased oxygen concentrations in the deep water and consequently altered nutrient fluxes. In the second reanalysis, both physical and biogeochemical observations are assimilated and good quality in all variables is found. Hence, we conclude that if a data assimilation method like the EnOI is applied, all available observations should be used to perform reanalyses of high quality for the Baltic Sea biogeochemical state estimates.

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“…For anisotropic Gaussian operators, the so-called triad (hexad) algorithm (Purser et al, 2003b;Purser, 2005) allows one to determine from the aspect tensor of the 2D (3D) Gaussian function, the three (six) generalized gridlines along which the 1D filters should be applied. Within that framework, various flow-dependent formulations of the aspect tensor have been proposed (De Pondeca et al, 2006;Liu et al, 2007Liu et al, , 2009Sato et al, 2009). Of particular interest here is the hybrid formulation of Sato et al (2009) where the inverse of the aspect tensor of the Gaussian function is defined as a linear combination of a 'conventional term' based on a quasi-isotropic, static formulation (A −1 iso ) and an 'ensemble term' formed from the sample covariance of the gradient of the ensemble-generated perturbations, normalized by the sample variance of the perturbations:…”

Section: Ensemble Estimation Methodsmentioning

confidence: 99%

“…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).…”

Section: Introductionmentioning

confidence: 99%

On the diffusion equation and its application to isotropic and anisotropic correlation modelling in variational assimilation

Weaver

Mirouze

2012

Quart J Royal Meteoro Soc

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Assimilating temperature and salinity profile observations using an anisotropic recursive filter in a coastal ocean model

Cited by 29 publications

References 24 publications

A 20-year reanalysis experiment in the Baltic Sea using three-dimensional variational (3DVAR) method

A 20-year reanalysis experiment in the Baltic Sea using three-dimensional variational (3DVAR) method

Improving the multiannual, high-resolution modelling of biogeochemical cycles in the Baltic Sea by using data assimilation

On the diffusion equation and its application to isotropic and anisotropic correlation modelling in variational assimilation

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