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1.
This paper and its companion [Thacker, W.C., Sindlinger, L., 2007-this issue. Estimating salinity to complement observed temperature: 2. Northwestern Atlantic. Journal of Marine Systems. doi:10.1016/j.jmarsys.2005.06.007.] document initial efforts in a project with the goal of developing capability for estimating salinity on a region-by-region basis for the world oceans. The primary motivation for this project is to provide information for correcting salinity, and thus density, when assimilating expendable-bathythermograph (XBT) data into numerical simulations of oceanic circulation, while a secondary motivation is to provide information for calibrating salinity from autonomous profiling floats. Empirical relationships between salinity and temperature, which can be identified from archived conductivity–temperature–depth (CTD) data, provide the basis for the salinity estimates.The Gulf of Mexico was chosen as the first region to explore for several reasons: (1) It's geographical separation from the Caribbean Sea and the North Atlantic Ocean makes it a “small ocean” characterised by a deep central basin surrounded by a substantial continental shelf. (2) The archives contain a relatively large number of CTD data that can be used to establish empirical relationships. (3) The sharp fronts associated with the Loop Current and its rings, which separate water with different thermal and haline characteristics, pose a challenge for estimating salinity. In spite of the shelf and the fronts, the relationship between salinity and temperature was found to be sufficiently regular that a single empirical model could be used to estimate salinity on each pressure surface for the entire Gulf for all seasons. In and below the thermocline, root-mean-square estimation errors are small — less than 0.02 psu for pressures greater than 400 dbar, corresponding to potential density errors of less than 0.015 kg/m3. Errors for estimates nearer to the surface can be an order of magnitude larger.  相似文献   

2.
A new automated quality control system for oceanic temperature and temperature–salinity profiles is presented. Substantial development was needed for some of the quality control algorithms although the checks were based on documented procedures used elsewhere where appropriate. A new automated ship track check was developed: the results of an undetected position error can be very damaging to ocean analyses. Also important is a check against a gridded background, this can be a climatology but near the surface it is advantageous to use an estimate that is evolving over time. Bayesian probability theory is used in the background check and the associated check against nearby observations (buddy check). The system was used to process archive data for 1956–2004. As a by-product monthly model-free objective analyses for this period were produced. Versions of the system are used for near-real time ocean analysis and for initialising both short-range ocean forecasts and seasonal atmosphere–ocean forecasts. The main features of the oceanic observing systems are presented along with quality control statistics, examples of errors that can occur and some additional problematic cases.  相似文献   

3.
Currently there are different approaches to filter algorithms based on the Kalman filter. One of the most used filter algorithms is the Ensemble Kalman Filter (EnKF). It uses a Monte Carlo approach to the filtering problem. Another approach is given by the Singular Evolutive Extended Kalman (SEEK) and Singular Evolutive Interpolated Kalman (SEIK) filters. These filters operate explicitly on a low-dimensional error space which is represented by an ensemble of model states. The EnKF and the SEIK filter have been implemented within a parallel data assimilation framework in the Finite Element Ocean Model FEOM. In order to compare the filter performances of the algorithms, several data assimilation experiments are performed. The filter algorithms have been applied with a model configuration of FEOM for the North Atlantic to assimilate the sea surface height in twin experiments. The dependence of the filter estimates on the represented error subspace is discussed. In the experiments the SEIK algorithm provides better estimates than the EnKF. Furthermore, the SEIK filter is much cheaper in terms of computing time.  相似文献   

4.
Several studies on coupled physical–biogeochemical models have shown that major deficiencies in the biogeochemical fields arise from the deficiencies in the physical flow fields. This paper examines the improvement of the physics through data assimilation, and the subsequent impact on the ecosystem response in a coupled model of the North Atlantic. Sea surface temperature and sea surface height data are assimilated with a sequential method based on the SEEK filter adapted to the coupling needs. The model domain covers the Atlantic from 20°S to 70°N at eddy-permitting resolution. The biogeochemical model is a NPZD-DOM model based on the P3ZD formulation. The results of an annual assimilated simulation are compared with an annual free simulation.With assimilation, the representation of the mixed layer depth is significantly improved in mid latitudes, even though the mixed layer depth is generally overestimated compared to the observations. The representation of the mean and variance of the currents is also significantly improved.The nutrient input in the euphotic zone is used to assess the data assimilation impact on the ecosystem. Data assimilation results in a 50% reduction of the input due to vertical mixing in mid-latitudes, and in a four- to six-fold increase of the advective fluxes in mid-latitudes and subtropics. Averaged zonally, the net impact is a threefold increase for the subtropical gyre, and a moderate (20–30%) decrease at mid and high latitudes.Surface chlorophyll concentration increases along the subtropical gyre borders, but little changes are detected at mid and high latitudes. An increase of the primary production appears along the Gulf Stream path, but it represents only 12% on average for mid and high latitudes. In the subtropical gyre centre, primary production is augmented but stays underestimated (20% of observations). These experiments show the benefits of physical data assimilation in coupled physical–biogeochemical applications.  相似文献   

5.
We consider the problem of combined state-parameter estimations in biased nonlinear models with non-Gaussian extensions of the Deterministic Ensemble Kalman Filter (DEnKF). We focus on the particular framework of ocean ecosystem models. Such models present important obstacles to the use of data assimilation methods based on Kalman filtering due to the non-linearity of the models, the constraints of positiveness that apply to the variables and parameters, and the non-Gaussian distribution of the variables in which they result.We present extensions of the DEnKF dealing with these difficulties by introducing a nonlinear change of variables (anamorphosis function) in order to execute the analysis step with Gaussian transformed variables and parameters. Several strategies to build the anamorphosis functions are investigated and compared within the framework of twin experiments realized in a simple 1D ocean ecosystem model. A solution to the problem of the specification of the observation error for transformed observations is suggested. The study highlights the inability of the plain DEnKF with a simple post-processing of the negative values to properly estimate parameters when constraints of positiveness apply to the variables. It goes on to show that the introduction of the Gaussian anamorphosis can remedy these assimilation biases.  相似文献   

6.
The Singular Evolutive Extended Kalman (SEEK) filter has been implemented to assimilate in-situ data in a 1D coupled physical-ecosystem model of the Ligurian Sea. The biogeochemical model describes the partly decoupled nitrogen and carbon cycles of the pelagic food web. The GHER hydrodynamic model (1D version) is used to represent the physical forcings. The data assimilation scheme (SEEK filter) parameterizes the error statistics by means of a set of empirical orthogonal functions (EOFs). Twin experiments are first performed with the aim to choose the suitable experimental protocol (observation and estimation vectors, number of EOFs, frequency of the assimilation,…) and to assess the SEEK filter performances. This protocol is then applied to perform real data assimilation experiments using the DYFAMED data base. By assimilating phytoplankton observations, the method has allowed to improve not only the representation of the phytoplankton community, but also of other variables such as zooplankton and bacteria that evolve with model dynamics and that are not corrected by the data assimilation scheme. The validation of the assimilation method and the improvement of model results are studied by means of suitable error measurements.  相似文献   

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