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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 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)

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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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See detailAnalysis of high frequency geostationary ocean colour data using DINEOF
Alvera Azcarate, Aïda ULg; Vanhellemont, Quinten; Ruddick, Kevin et al

in Estuarine Coastal & Shelf Science (2015), 159

DINEOF (Data Interpolating Empirical Orthogonal Functions), a technique to reconstruct missing data, is applied to turbidity data obtained through the Spinning Enhanced Visible and Infrared Imager (SEVIRI ... [more ▼]

DINEOF (Data Interpolating Empirical Orthogonal Functions), a technique to reconstruct missing data, is applied to turbidity data obtained through the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat Second Generation 2. The aim of this work is to assess if the tidal variability of the southern North Sea in 2008 can be accurately reproduced in the reconstructed dataset. Such high frequency data have not previously been analysed with DINEOF and present new challenges, like a strong tidal signal and long night-time gaps. An outlier detection approach that exploits the high temporal resolution (15 min) of the SEVIRI dataset is developed. After removal of outliers, the turbidity dataset is reconstructed with DINEOF. In situ Smartbuoy data are used to assess the accuracy of the reconstruction. Then, a series of tidal cycles are examined at various positions over the southern North Sea. These examples demonstrate the capability of DINEOF to reproduce tidal variability in the reconstructed dataset, and show the high temporal and spatial variability of turbidity in the southern North Sea. An analysis of the main harmonic constituents (annual cycle, daily cycle, M2 and S2 tidal components) is performed, to assess the contribution of each of these modes to the total variability of turbidity. The variability not explained by the harmonic fit, due to the natural processes and satellite processing errors as noise, is also assessed. [less ▲]

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See detailEOF analysis of long-term reconstructed AVHRR Pathfinder SST in the South China Sea
Huynh, Thi Hong Ngu ULg; Alvera Azcarate, Aïda ULg; Barth, Alexander ULg et al

Poster (2014, May 02)

Sea surface temperature (SST) is one of the key variables often used to investigate ocean dynamics, ocean-atmosphere interaction, and climate change. For recent decades, the AVHRR Pathfinder SST, measured ... [more ▼]

Sea surface temperature (SST) is one of the key variables often used to investigate ocean dynamics, ocean-atmosphere interaction, and climate change. For recent decades, the AVHRR Pathfinder SST, measured by infrared sensors, has been widely used because of its high resolution and long time-series. The disadvantage of the AVHRR Pathfinder SST is high percentage of missing data due to cloud coverage. This becomes more serious in the South China Sea (SCS) because it is located in the tropical region, frequently covered by clouds. In this study, we used the Data INterpolating Empirical Orthogonal Functions (DINEOF) method to reconstruct daily night-time 4 km AVHRR Pathfinder SST spanning from 1989 to 2009 for the whole SCS. In order to better understand the spatial and temporal variability of the SCS SST, an EOF analysis of the reconstructed field is performed in association with surface wind. The first SST mode, accounting for 69% of the variance, presents the cooling (warming) of the basin due to the solar inclination through seasons, water exchange, topography, and monsoon-induced cyclonic circulation. The second SST mode, explaining 24.8% of the variance, shows the advection of cold and warm water from two opposite directions along the southwest-northeast diagonal of the basin. The second SST mode is affected by the atmospheric anticyclone (cyclone) located over the Philippine Sea. Comparing both SST modes with Nino3.0 index, it shows that the interannual variability of the SCS SST is influenced by the moderate and strong ENSO events with a lag of 5-6 months. Moreover, the analysis of the high-resolution reconstructed dataset reveals some oceanic features that could not be captured in previous EOF analyses. [less ▲]

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See detailBias correction using data assimilation: Application on the Lorenz ’95 and NEMO-LIM models.
Canter, Martin ULg; Barth, Alexander ULg

Poster (2014, May 01)

Data assimilation has been used for decades in fields like engineering or signal processing to improve forecast models. Ensemble Kalman filters and other sequential data assimilation methods are examples ... [more ▼]

Data assimilation has been used for decades in fields like engineering or signal processing to improve forecast models. Ensemble Kalman filters and other sequential data assimilation methods are examples of developments which reduce the uncertainty of the model by taking observations into account. The widespread interest in addressing systematic forecast model errors only arose when the advances in modelling, data assimilation and computational power had reduced random errors to the point of commensurability with systematic errors, also known as bias. We present here 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 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. [less ▲]

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