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See detailReconstruction and analysis of long-term satellite-derived sea surface temperature for the South China Sea
Huynh, Thi Hong Ngu ULg; Alvera Azcarate, Aïda ULg; Barth, Alexander ULg et al

in Journal of Oceanography (2016)

Sea surface temperature (SST) is one of the key variables often used to investigate ocean dynamics, ocean-atmosphere interaction, and climate change. Unfortunately, the SST data sources in the South China ... [more ▼]

Sea surface temperature (SST) is one of the key variables often used to investigate ocean dynamics, ocean-atmosphere interaction, and climate change. Unfortunately, the SST data sources in the South China Sea (SCS) are not abundant due to sparse measurements of in situ SST and a high percentage of missing data in the satellite-derived SST. Therefore, SST data sets with low resolution and/or a short-term period have often been used in previous researches. Here we used Data INterpolating Empirical Orthogonal Functions, a self-consistent and parameter-free method for filling in missing data, to reconstruct the daily nighttime 4-km AVHRR Pathfinder SST for the long-term period spanning from 1989 to 2009. In addition to the reconstructed field, we also estimated the local error map for each reconstructed image. Comparisons between the reconstructed and other data sets (satellite-derived microwave and in situ SSTs) show that the results are reliable for use in many different researches, such as validating numerical models, or identifying and tracking meso-scale oceanic features. Moreover, the Empirical Orthogonal Function (EOF) analysis of the reconstructed SST and the reconstructed SST anomalies clearly shows the subseasonal, seasonal, and interannual variability of SST under the influence of monsoon and El Niño-Southern Oscillation (ENSO), as well as reveals some oceanic features that could not be captured well in previous EOF analyses. The SCS SST often lags ENSO by about half a year. However, in this study, we see that the time lag changes with the frequencies of the SST variability, from 1 to 6 months. [less ▲]

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See detailAssimilation of sea surface temperature, sea ice concentration and sea ice drift in a model of the Southern Ocean
Barth, Alexander ULg; Canter, Martin ULg; Van Schaeybroeck, Bert et al

in Ocean Modelling (2015), 93

Current ocean models have relatively large errors and biases in the Southern Ocean. The aim of this study is to provide a reanalysis from 1985 to 2006 assimilating sea surface temperature, sea ice ... [more ▼]

Current ocean models have relatively large errors and biases in the Southern Ocean. The aim of this study is to provide a reanalysis from 1985 to 2006 assimilating sea surface temperature, sea ice concentration and sea ice drift. In the following it is also shown how surface winds in the Southern Ocean can be improved using sea ice drift estimated from infrared radiometers. Such satellite observations are available since the late seventies and have the potential to improve the wind forcing before more direct measurements of winds over the ocean are available using scatterometry in the late nineties. The model results are compared to the assimilated data and to independent measurements (the World Ocean Database 2009 and the mean dynamic topography based on observations). The overall improvement of the assimilation is quantified, in particular the impact of the assimilation on the representation of the polar front is discussed. Finally a method to identify model errors in the Antarctic sea ice area is proposed based on Model Output Statistics techniques using a series of potential predictors. This approach provides new directions for model improvements. [less ▲]

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See detailA stochastic operational forecasting system of the Black Sea: Technique and validation
Vandenbulcke, Luc ULg; Barth, Alexander ULg

in Ocean Modelling (2015), 93

In this article, we present the latest version of an ensemble forecasting system of the hydrodynamics of the Black Sea, based on the GHER model. The system includes the Weakly Constrained Ensembles ... [more ▼]

In this article, we present the latest version of an ensemble forecasting system of the hydrodynamics of the Black Sea, based on the GHER model. The system includes the Weakly Constrained Ensembles algorithm to generate random, but physically balanced perturbations to initialize members of the ensemble. On top of initial conditions, the ensemble accounts also for uncertainty on the atmospheric forcing fields, and on some scalar parameters such as river flows or model diffusion coefficients. The forecasting system also includes the Ocean Assimilation Kit, a sequential data assimilation package implementing the SEEK and Ensemble Kalman filters. A novel aspect of the forecasting system is that not only our best estimate of the future ocean state is provided, but also the associated error estimated from the ensemble of models. The primary goal of this paper is to quantitatively show that the ensemble variability is a good estimation of the model error, regardless of the magnitude of the forecast errors themselves. In order for this estimation to be meaningful, the model itself should also be well validated. Therefore, we describe the model validation against general circulation patterns. Some particular aspects critical for the Black Sea circulation are validated as well: the mixed layer depth and the shelfopen sea exchanges. The model forecasts are also compared with observed sea surface temperature, and errors are compared to those of another operational model as well. [less ▲]

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See detailThe seamod.ro operational stochasting Black Sea forecasting system
Vandenbulcke, Luc ULg; Capet, Arthur; Grégoire, Marilaure ULg et al

Poster (2015, May 08)

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See detailThe seamod.ro operational stochasting forecasting system of the Black Sea
Vandenbulcke, Luc ULg; Barth, Alexander ULg; Capet, Arthur et al

Poster (2015, April 15)

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See detailOcean Modeling: Bias correction through stochastic forcing.
Canter, Martin ULg; Barth, Alexander ULg

Conference (2015, April 14)

With this work, we aim at developping a new method of bias correction using data assimilation. This method is based on the stochastic forcing of a model to correct bias. First, through a preliminary run ... [more ▼]

With this work, we aim at developping a new method of bias correction using data assimilation. This method is based on the stochastic forcing of a model to correct bias. First, through a preliminary run, we estimate the bias of the model and its possible sources. Then, we es- tablish a forcing term which is directly added inside the model’s equations. We create an ensemble of runs and consider the forcing term as a control variable during the assimilation of observations. We then use this analysed forcing term to correct the bias of the model. Since the forcing is added inside the model, it acts as a source term, unlike external forcings such as wind. This procedure has been developed and successfully tested with a twin experiment on a Lorenz 95 model. Indeed, we were able to estimate and recover an artificial bias that had been added into the model. This bias had a spatial structure and was constant through time. The mean and behaviour of the corrected model corresponded to those the reference model. It is currently being applied and tested on the sea ice ocean NEMO LIM model, which is used in the PredAntar project. NEMO LIM is a global and low resolution (2 degrees) coupled model (hydrodynamic model and sea ice model) with long time steps allowing simulations over several decades. Due to its low resolution, the model is subject to bias in area where strong currents are present. We aim at correcting this bias by using perturbed current fields from higher resolution models and randomly generated perturbations. The random perturbations need to be constrained in order to respect the physical properties of the ocean, and not create unwanted phenomena. To construct those random perturbations, we first create a random field with the Diva tool (Data-Interpolating Variational Analysis). Using a cost function, this tool penalizes abrupt variations in the field, while using a custom correlation length. It also decouples disconnected areas based on topography. Then, we filter the field to smoothen it and remove small scale variations. We use this field as a random stream function, and take its derivatives to get zonal and meridional velocity fields. We also constrain the stream function along the coasts in order not to have currents perpendicular to the coast. The randomly generated stochastic forcing are then directly injected into the NEMO LIM model’s equa- tions in order to force the model at each timestep, and not only during the assimilation step. The first results on a twin experiment with the NEMO LIM model will be presented. [less ▲]

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See detailAssimilation of sea surface temperature, sea ice concentration and sea ice drift in a model of the Southern Ocean
Barth, Alexander ULg; Canter, Martin ULg; Van Schaeybroeck, Bert et al

Poster (2015)

Current ocean models have relatively large errors and biases in the Southern Ocean. The aim of this study is to provide a reanalysis from 1985 to 2006 assimilating sea surface temperature, sea ice ... [more ▼]

Current ocean models have relatively large errors and biases in the Southern Ocean. The aim of this study is to provide a reanalysis from 1985 to 2006 assimilating sea surface temperature, sea ice concentration and sea ice drift. In the following it is also shown how surface winds in the Southern Ocean can be improved using sea ice drift es- timated from infrared radiometers. Such satellite observations are available since the late seventies and have the potential to improve the wind forcing before more direct measure- ments of winds over the ocean are available using scatterometry in the late nineties. The model results are compared to the assimilated data and to independent measurements (the World Ocean Database 2009 and the mean dynamic topography based on observations). The overall improvement of the assimilation is quantified, in particular the impact of the assimilation on the representation of the polar front is discussed. Finally a method to iden- tify model errors in the Antarctic sea ice area is proposed based on Model Output Statistics techniques using a series of potential predictors. This approach provides new directions for model improvements. [less ▲]

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See detailThe SANGOMA Tools for Data Assimilation
Nerger, Lars; Altaf, Umer; Barth, Alexander ULg et al

Poster (2015)

The EU-funded project SANGOMA – Stochastic Assimilation of the Next Gener- ation Ocean Model Applications –provides new developments in data assimilation to ensure that future operational systems can make ... [more ▼]

The EU-funded project SANGOMA – Stochastic Assimilation of the Next Gener- ation Ocean Model Applications –provides new developments in data assimilation to ensure that future operational systems can make use of state-of-the-art data-assimilation methods and related analysis tools. One task of SANGOMA is to develop a collection of common tools for data assimilation with a uniform interface so that the tools are usable from dif- ferent data assimilation systems. The tool developments mainly aim at tools that support ensemble-based data assimilation applications like for the generation of perturbations, to perform transformations, to compute diagnostics, as well as further utilities. In addition, a selection of ensemble filter analysis steps is included. The tools are implemented in Fortran and as scripts for Matlab or Octave. They are provided as free open-source programs via the project web site [http://www.data-assimilation.net]. This contribution provides an overview of the tools that are available in the latest release V1 of the SANGOMA tools as well as the plans for the next release. [less ▲]

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See detailLocal ensemble assimilation scheme with global constraints and conservation
Barth, Alexander ULg; Yan, Yajing ULg; Canter, Martin ULg et al

Conference (2015)

Ensemble assimilation schemes applied in their original, global formulation have no problem in respecting linear conservation properties if the ensemble perturbations are setup accordingly. For realistic ... [more ▼]

Ensemble assimilation schemes applied in their original, global formulation have no problem in respecting linear conservation properties if the ensemble perturbations are setup accordingly. For realistic ocean systems, only a relatively small number of ensemble members can be calculated. A localization of the ensemble increment is thus necessary to filter out spurious long-range correlations. However, the conservation of the global property will be lost if the assimilation is performed locally since the conservation requires a coupling between model grid points, which is filtered out by the localization. In the ocean, the distribution of observations is highly inhomogeneous. System- atic errors of the observed parts of the ocean state can lead to spurious systematic adjust- ments of the non-observed part of the ocean state due to data assimilation. As a result, global properties which should be conserved, increase or decrease in long-term simulations. We propose an assimilation scheme (with stochastic or deterministic analysis steps) which is formulated globally (i.e. for the whole state vector) but where spurious long-range correlations can be filtered out. The scheme can thus be used to enforce global conservation properties and non-local observation operators. Both aspects are indeed linked since one can introduce the global conservation as a weak constraint by using a global ob- servation operator. The conserved property becomes thus an observed value. The proposed scheme is tested with the Kuramoto-Sivashinsky model which is conservative. The benefit compared to the traditional covariance localization scheme (with an ad-hoc step enforcing conservation) where observations are assimilated sequentially is shown. The assimilation scheme is suitable to be implemented on parallel computers where the number of available computing cores is a multiple of the ensemble size. [less ▲]

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See detailBias correction with data assimilation
Canter, Martin ULg; Barth, Alexander ULg

Conference (2015)

With this work, we aim at developping a new method of bias correction using data assimilation. This method is based on the stochastic forcing of a model to correct bias. First, through a preliminary run ... [more ▼]

With this work, we aim at developping a new method of bias correction using data assimilation. This method is based on the stochastic forcing of a model to correct bias. First, through a preliminary run, we estimate the bias of the model and its possible sources. Then, we establish a forcing term which is directly added inside the model’s equa- tions. We create an ensemble of runs and consider the forcing term as a control variable during the assimilation of observations. We then use this analysed forcing term to correct the bias of the model. Since the forcing is added inside the model, it acts as a source term, unlike external forcings such as wind. This procedure has been developed and successfully tested with a twin experiment on a Lorenz 95 model. Indeed, we were able to estimate and recover an artificial bias that had been added into the model. This bias had a spatial structure and was constant through time. The mean and behaviour of the corrected model corresponded to those the reference model. It is currently being applied and tested on the sea ice ocean NEMO LIM model, which is used in the PredAntar project. NEMO LIM is a global and low resolution (2 de- grees) coupled model (hydrodynamic model and sea ice model) with long time steps allow- ing simulations over several decades. Due to its low resolution, the model is subject to bias in area where strong currents are present. We aim at correcting this bias by using perturbed current fields from higher resolution models and randomly generated perturbations. The random perturbations need to be constrained in order to respect the physical properties of the ocean, and not create unwanted phenomena. To construct those random perturbations, we first create a random field with the Diva tool (Data-Interpolating Varia- tional Analysis). Using a cost function, this tool penalizes abrupt variations in the field, while using a custom correlation length. It also decouples disconnected areas based on to- pography. Then, we filter the field to smoothen it and remove small scale variations. We use this field as a random stream function, and take its derivatives to get zonal and meridional velocity fields. We also constrain the stream function along the coasts in order not to have currents perpendicular to the coast. The randomly generated stochastic forcing are then directly injected into the NEMO LIM model’s equations in order to force the model at each timestep, and not only during the assimilation step. The first results on a twin experiment with the NEMO LIM model will be presented. [less ▲]

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See detailAssimilation of HF radar surface currents to optimize forcing in the northwestern Mediterranean Sea
Marmain, J.; Molcard, A.; Forget, P. et al

Poster (2015)

HF radar measurements are used to optimize surface wind forcing and baroclinic open boundary condition (OBC) forcing in order to constrain model coastal surface cur- rents. This method is applied to a ... [more ▼]

HF radar measurements are used to optimize surface wind forcing and baroclinic open boundary condition (OBC) forcing in order to constrain model coastal surface cur- rents. This method is applied to a northwestern Mediterranean (NWM) regional primitive equation model configuration. A new radar data set, provided by two radars deployed in the Toulon area (France), is used. To our knowledge, this is the first time that radar mea- surements of the NWM Sea are assimilated into a circulation model. Special attention has been paid to the improvement of the model coastal current in terms of speed and position. The data assimilation method uses an ensemble Kalman smoother to optimize forcing in order to improve the model trajectory. Twin experiments are initially performed to evaluate the method skills (not shown here). Real measurements are then fed into the circulation model and significant improvements to the modeled surface currents, when compared to observations, are obtained. [less ▲]

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See detailContribution of EMODnet Chemistry to the management and visualization of marine chemical data
Vinci, M.; Giorgetti, A.; Lipizer, M. et al

Conference (2015)

EMODnet Chemistry is a thematic component of the European Marine Observa- tion and Data Network launched by DG MARE in 2009 to improve the availability of high quality environmental data and support the ... [more ▼]

EMODnet Chemistry is a thematic component of the European Marine Observa- tion and Data Network launched by DG MARE in 2009 to improve the availability of high quality environmental data and support the Marine Strategy Framework Directive (MSFD) requirements. The aim is twofold: the first task is to make available and reusable the big amount of fragmented and inaccessible data, hosted in the European research institutes and environmental agencies, after processing them into interoperable formats, using agreed standards and vocabularies and assessing their accuracy and precision. The second objec- tive is to develop visualization data products useful for the tasks of the MSFD. EMODnet Chemistry involves a European network of 46 institutes from 29 coastal countries, covering most European seas. Data managed by the EMODnet Chemistry distributed infrastructure include chemical properties measured in three matrices: seawater, sediment and biota and address three descriptors of Good Environmental Status (GES) defined by the MSFD: eu- trophication, contaminants in the environment and in seafood. The pillars of the project include the assembly of data and metadata according to standardized procedures, the pro- cessing into interoperable formats, the definition of common quality control procedures, the assessment of data quality and the generation of suitable data products for all European sea regions, in agreement with the requests of the MSFD. The technical set-up is based on the principle of adopting and adapting the SeaDataNet pan-European infrastructure for ocean and marine data management which is managed by NODCs and relies on a distributed net- work of data centres. The quality of the data has appeared as a key issue when merging heterogeneous data coming from different sources. The data validation loop includes a first set of controls done by the data collators prior to the inclusion of the data in the infras- tructure, a data aggregation and data quality control, performed in a coordinated way on the five Regional Data Buffers which are related to the Baltic Sea, the North Sea, the At- lantic area (including the Atlantic coast and the Macaronesia), the Mediterranean Sea and the Black Sea respectively. Regular reports are sent to the data collators to correct errors or anomalies in the master copy of the data, available from the EMODnet infrastructure, and to guarantee the data quality upgrading. Besides this, the consortium started the col- lection of quality information “ex-ante”, related to the source laboratories analysis (based on ISO/IEC 17025/2005). In order to test new strategies for data storage and reanalysis and to upgrade the infrastructure performances, EMODnet Chemistry has chosen the Cloud hosting offered by Cineca (the Consortium of Italian Universities and research institutes) to host the Regional Data Buffers and facilitate the analysis and visualization services. Finally,beside the delivery of data and products, the results of the data harvesting by this Europe wide consortium of institutes for all the European Seas provide a useful starting point for a gap analysis to gain understanding where the future monitoring efforts should be focused. [less ▲]

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See detailAssimilation of sea surface temperature, sea ice concentration and sea ice drift in a model of the Southern Ocean
Barth, Alexander ULg; Canter; Van Schaeybroeck, Bert et al

Conference (2015)

Detailed reference viewed: 13 (1 ULg)
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See detailWeb-based visualization of gridded dataset usings OceanBrowser
Barth, Alexander ULg; Watelet, Sylvain ULg; Troupin, Charles ULg et al

Conference (2015)

OceanBrowser is a web-based visualization tool for gridded oceanographic data sets. Those data sets are typically four-dimensional (longitude, latitude, depth and time). OceanBrowser allows one to ... [more ▼]

OceanBrowser is a web-based visualization tool for gridded oceanographic data sets. Those data sets are typically four-dimensional (longitude, latitude, depth and time). OceanBrowser allows one to visualize horizontal sections at a given depth and time to examine the horizontal distribution of a given variable. It also offers the possibility to display the results on an arbitrary vertical section. To study the evolution of the variable in time, the horizontal and vertical sections can also be animated. Vertical section can be generated by using a fixed distance from coast or fixed ocean depth. The user can customize the plot by changing the color-map, the range of the color-bar, the type of the plot (linearly interpolated color, simple contours, filled contours) and download the current view as a simple image or as Keyhole Markup Language (KML) file for visualization in applications such as Google Earth. The data products can also be accessed as NetCDF files and through OPeNDAP. Third-party layers from a web map service can also be integrated. OceanBrowser is used in the frame of the SeaDataNet project (http://gher-diva.phys.ulg.ac.be/web-vis/) and EMODNET Chemistry (http://oceanbrowser.net/emodnet/) to distribute gridded data sets interpolated from in situ observation using DIVA (Data-Interpolating Variational Analysis). [less ▲]

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See detailCoastal Ocean Forecasting: science foundation and user benefits
Kourafalou, V. H.; Kourafalou, P.; Staneva, J. et al

in Journal of Operational Oceanography (2015), 8

The advancement of Coastal Ocean Forecasting Systems (COFS) requires the support of continuous scientific progress addressing: (a) the primary mechanisms driving coastal circulation; (b) methods to ... [more ▼]

The advancement of Coastal Ocean Forecasting Systems (COFS) requires the support of continuous scientific progress addressing: (a) the primary mechanisms driving coastal circulation; (b) methods to achieve fully integrated coastal systems (observations and models), that are dynamically embedded in larger scale systems; and (c) methods to adequately represent air-sea and biophysical interactions. Issues of downscaling, data assimilation, atmosphere-wave-ocean couplings and ecosystem dynamics in the coastal ocean are discussed. These science topics are fundamental for successful COFS, which are connected to evolving downstream applications, dictated by the socioeconomic needs of rapidly increasing coastal populations. [less ▲]

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