References of "Beckers, Jean-Marie"
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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 detailLocal ensemble assimilation scheme with global constraints and conservation
Barth, Alexander ULg; Yan, Yajing ULg; Canter, Martin ULg et al

Poster (2014, April)

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. Systematic errors of the observed parts of the ocean state can lead to spurious systematic adjustments 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 observation 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 detailWeb-based application for Data INterpolation Empirical Orthogonal Functions (DINEOF) analysis
Tomazic, Igor ULg; Alvera Azcarate, Aïda ULg; Barth, Alexander ULg et al

Poster (2014, April)

DINEOF (Data INterpolating Empirical Orthogonal Functions) is a powerful tool based on EOF decomposition developed at the University of Liege/GHER for the reconstruction of missing data in satellite ... [more ▼]

DINEOF (Data INterpolating Empirical Orthogonal Functions) is a powerful tool based on EOF decomposition developed at the University of Liege/GHER for the reconstruction of missing data in satellite datasets, as well as for the reduction of noise and detection of outliers. DINEOF is openly available as a series of Fortran routines to be compiled by the user, and as binaries (that can be run directly without any compilation) both for Windows and Linux platforms. In order to facilitate the use of DINEOF and increase the number of interested users, we developed a web-based interface for DINEOF with the necessary parameters available to run high-quality DINEOF analysis. This includes choosing variable within selected dataset, defining a domain, time range, filtering criteria based on available variables in the dataset (e.g. quality flag, satellite zenith angle …) and defining necessary DINEOF parameters. Results, including reconstructed data and calculated EOF modes will be disseminated in NetCDF format using OpenDAP and WMS server allowing easy visualisation and analysis. First, we will include several satellite datasets of sea surface temperature and chlorophyll concentration obtained from MyOcean data centre and already remapped to the regular grid (L3C). Later, based on user’s request, we plan to extend number of datasets available for reconstruction. [less ▲]

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See detailApproximate and Efficient Methods to Assess Error Fields in Spatial Gridding with Data Interpolating Variational Analysis (DIVA)
Beckers, Jean-Marie ULg; Barth, Alexander ULg; Troupin, Charles ULg et al

in Journal of Atmospheric & Oceanic Technology (2014), 31(2), 515-530

We present new approximate methods to provide error fields for the spatial analysis tool Diva. It is first shown how to replace the costly analysis of a large number of covariance functions by a single ... [more ▼]

We present new approximate methods to provide error fields for the spatial analysis tool Diva. It is first shown how to replace the costly analysis of a large number of covariance functions by a single analysis for quick error computations. Then another method is presented where the error is only calculated in a small number of locations and from there the spatial error field itself interpolated by the analysis tool. The efficiency of the methods is illustrated on simple schematic test cases and a real application in the Mediterranean Sea. These examples show that with these methods one has the possibility for quick masking of regions void of sufficient data and the production of "exact" error fields at reasonable cost. The error-calculation methods can also be generalized for use with other analysis methods such as 3D-Var and are therefore potentially interesting for other implementations. [less ▲]

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See detailUntangling spatial and temporal trends in the variability of the Black Sea Cold Intermediate Layer and mixed Layer Depth using the DIVA detrending procedure
Capet, Arthur ULg; Troupin, Charles ULg; Cartensen, Jacob et al

in Ocean Dynamics (2014), 64(3), 315-324

Current spatial interpolation products may be biased by uneven distribution of measurements in time. This manuscript presents a detrending method that recognizes and eliminates this bias. The method ... [more ▼]

Current spatial interpolation products may be biased by uneven distribution of measurements in time. This manuscript presents a detrending method that recognizes and eliminates this bias. The method estimates temporal trend components in addition to the spatial structure and has been implemented within the Data Interpolating Variational Analysis (DIVA) analysis tool. The assets of this new detrending method are illustrated by producing monthly and annual climatologies of two vertical properties of the Black Sea while recognizing their seasonal and interannual variabilities : the mixed layer depth, and the cold content of its Cold Intermediate Layer (CIL). The temporal trends, given as by-products of the method, are used to analyze the seasonal and interannual variability of these variables over the past decades (1955-2011). In particular, the CIL interannual variability is related to the cumulated winter air temperature anomalies, explaining 88\% of its variation. [less ▲]

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See detaildivand-1.0: n-dimensional variational data analysis for ocean observations
Barth, Alexander ULg; Beckers, Jean-Marie ULg; Troupin, Charles ULg et al

in Geoscientific Model Development (2014), 7

A tool for multidimensional variational analysis (divand) is presented. It allows the interpolation and analysis of observations on curvilinear orthogonal grids in an arbitrary high dimensional space by ... [more ▼]

A tool for multidimensional variational analysis (divand) is presented. It allows the interpolation and analysis of observations on curvilinear orthogonal grids in an arbitrary high dimensional space by minimizing a cost function. This cost function penalizes the deviation from the observations, the deviation from a first guess and abruptly varying fields based on a given correlation length (potentially varying in space and time). Additional constraints can be added to this cost function such as an advection constraint which forces the analysed field to align with the ocean current. The method decouples naturally disconnected areas based on topography and topology. This is useful in oceanography where disconnected water masses often have different physical properties. Individual elements of the a priori and a posteriori error covariance matrix can also be computed, in particular expected error variances of the analysis. A multidimensional approach (as opposed to stacking 2-dimensional analysis) has the benefit of providing a smooth analysis in all dimensions, although the computational cost is increased. Primal (problem solved in the grid space) and dual formulations (problem solved in the observational space) are implemented using either direct solvers (based on Cholesky factorization) or iterative solvers (conjugate gradient method). In most applications the primal formulation with the direct solver is the fastest, especially if an a posteriori error estimate is needed. However, for correlated observation errors the dual formulation with an iterative solver is more efficient. The method is tested by using pseudo observations from a global model. The distribution of the observations is based on the position of the ARGO floats. The benefit of the 3-dimensional analysis (longitude, latitude and time) compared to 2-dimensional analysis (longitude and latitude) and the role of the advection constraint are highlighted. The tool divand is free software, and is distributed under the terms of the GPL license (http://modb.oce.ulg.ac.be/mediawiki/index.php/divand). [less ▲]

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See detailMulti-scale optimal interpolation: application to DINEOF analysis spiced with a local optimal interpolation
Beckers, Jean-Marie ULg; Barth, Alexander ULg; Tomazic, Igor ULg et al

in Ocean Science Discussions (2014), 11

We present a method in which the optimal interpolation of multi-scale processes can be untangled into a succession of simpler interpolations. First, we prove how the optimal analysis of a superposition of ... [more ▼]

We present a method in which the optimal interpolation of multi-scale processes can be untangled into a succession of simpler interpolations. First, we prove how the optimal analysis of a superposition of two processes can be obtained by different mathematical formulations involving iterations and analysis focusing on a single process. From the 5 different mathematical equivalent formulations we then select the most efficient ones by analyzing the behavior of the different possibilities in a simple and well controlled test case. The clear guidelines deduced from this experiment are then applied in a real situation in which we combine large-scale analysis of hourly SEVIRI satellite images using DINEOF with a local optimal interpolation using a Gaussian covariance. It is 10 shown that the optimal combination indeed provides the best reconstruction and can therefore be exploited to extract the maximum amount of useful information from the original data [less ▲]

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See detailInterpolation of SLA Using the Data-Interpolating Variational Analysis in the Coastal Area of the NW Mediterranean Sea
Troupin, Charles ULg; Barth, Alexander ULg; Beckers, Jean-Marie ULg et al

Poster (2013, October 07)

The spatial interpolation of along-track Sea-Level Anomalies (SLA) data to produce gridded map has numerous applications in oceanography (model validation, data assimilation, eddy tracking, ...). Optimal ... [more ▼]

The spatial interpolation of along-track Sea-Level Anomalies (SLA) data to produce gridded map has numerous applications in oceanography (model validation, data assimilation, eddy tracking, ...). Optimal Interpolation (OI) is often the preferred method for this task, as it leads to the lowest expected error and provides an error field associated to the analyzed field. However, the method suffers from limitations such as the numerical cost (due to the inversion of covariance matrices) as well as the isotropic covariance function, generally employed in altimetry. The Data-Interpolating Variational Analysis (DIVA) is a gridding method based on the minimization of a cost function using a finite-element technique. The cost function penalizes the departures from observations, the smoothness of the gridded field and physical constraints (advection, diffusion, ...). It has been shown that DIVA and OI are equivalent (provided some assumptions on the covariances are made), the main difference is that in DIVA, the covariance function is not explicitly formulated. The technique has been previously applied for the creation of regional hydrographic climatologies, which required the processing of a large number of data points. In this work we present the application and adaptation of Diva to the analysis of SLA in the Mediterranean Sea and the production of weekly maps of SLA in this region. The peculiarities of SLA along-track data are addressed: • number of observations: the finite-element technique coupled to improvements in the matrix inversion (parallel or iterative solvers) lead to a decrease of the computational time, meaning that sub-sampling of the initial data set is not required. • quality of the different missions: the weight attributed to each data point can be easily set according to the satellite that provided the observations, so that different measurement noise variances are considered. • spatial correlation scale: it varies spatially in the domain according to the value of the Rossby radius of deformation. • long-wavelength errors: each data point is associated a class, and a detrending technique allows the determination of the trend for each class, leading to a reduction of the inconsistencies between missions. • anisotropy of physical coastal features: a pseudo-velocity field derived from regional bathymetry enhances the correlations along the main currents. Particular attention will be paid to the influence of this constraint in the coastal area. The analysis and error fields obtained over the Mediterranean Sea are compared with the available gridded products from AVISO. Different ways to compute the error field are compared. The impact of the use of multiple missions to prepare the gridded fields is also examined. In situ measurements from an intensive multi-sensor experiment carried out north of the Balearic Islands in May 2009 serve to assess the quality of the gridded fields in the coastal area. [less ▲]

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See detailHypoxia in the Black Sea northwestern shelf: From eutrophication to climatic stressors.
Capet, Arthur ULg; Beckers, Jean-Marie ULg; Grégoire, Marilaure ULg

in 40th CIESM congress proceedings (2013, October)

Abstract The dynamics of seasonal hypoxia, which affects the Black Sea north-western shelf since the mid 1970's until present days, is investigated by means of a 3D biogeochemical model. Comparison of the ... [more ▼]

Abstract The dynamics of seasonal hypoxia, which affects the Black Sea north-western shelf since the mid 1970's until present days, is investigated by means of a 3D biogeochemical model. Comparison of the model results with in -situ data reveals that the phenomenon may have been underestimated after the mid 1990's due to the distribution of observations. We investigate the mechanism of hypoxia at seasonal scale, and identify the main drivers of its interannual variability. While high nutrients discharge caused severe hypoxia in the 1980's, it was sustained in the 1990's by the pool of organic matter accumulated during the previous years in the sediments layer. With an increasing intensity, climatic stressors intensifies the response of hypoxia to nutrient discharge, and affect the seasonal dynamics of hypoxia by extending its temporal scale. [less ▲]

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See detailWP8 and WP9 developments: Data-Interpolating Variational Analysis (Diva) developments
Troupin, Charles ULg; Barth, Alexander ULg; Ouberdous, Mohamed et al

Conference (2013, September 27)

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See detailApplication of the Data-Interpolating Variational Analysis (DIVA) to sea-level anomaly measurements in the Mediterranean Sea
Troupin, Charles ULg; Barth, Alexander ULg; Beckers, Jean-Marie ULg et al

Poster (2013, September 23)

In ocean sciences, numerous techniques are available for the spatial interpolation of in situ data. These techniques mainly differ in the mathematical formulation and the numerical efficiency. Among them ... [more ▼]

In ocean sciences, numerous techniques are available for the spatial interpolation of in situ data. These techniques mainly differ in the mathematical formulation and the numerical efficiency. Among them, DIVA, which is based on the minimization of a cost function using a finite-element technique (figure 1). The cost function penalizes the departure from observations, the smoothness or regularity of the gridded field and can also include physical constraints. The technique is particularly adapted for the creation of climatologies, which required a large to several regional seas or part of the ocean to generate hydrographic climatologies. Sea-level anomalies (SLA) can be deduced from satellite-borne altimeters. The measurements are characterized by a high spatial resolution along the satellite tracks, but often a large distance between neighbour tracks. This implies the use of simultaneous altimetry missions for the construction of gridded maps. An along-track long wave-length error (correlated noise, e.g. due to orbit, residual tidal correction or inverse barometer errors) also affects the measurement and has to be taken into account in the interpolation. In this work we present the application and adaptation of Diva to the analysis of SLA in the Mediterranean Sea and the production of weekly maps of SLA in this region. Determination of the parameters The two main parameters that determines an analysis with DIVA are the correlation length (L) and the signal-to-noise ratio (SNR). Because of the particular spatial distribution of the measurements, the tools implemented in Diva for the analysis parameter determination tend to underestimate L and overestimate SNR, leading to noisy analysis (the observation constraint dominates the regularity constraint). Some adaptations of the tools are necessary to solve this issue. Numerical cost Because of the large number of observations to be processed (in comparison with in situ measurements on a similar period), the interpolation method employed is expected to be numerically efficient. Improvements in the implementation of Diva further improved the numerical performance of the method, especially thanks to the use of a parallel solver for the matrix inversion. The performance of finite-element mesh generator was also enhanced, so that interpolation of a data set of more than 1 million data points on a 100-by-100 grid can be performed in a few minutes on a personal laptop. Analysis and error field The analysis and error fields obtained over the Mediterranean Sea are compared with the available gridded products from AVISO. Different ways to compute the error field are compared. The impact of the use of multiple missions to prepare the gridded fields is also examined. [less ▲]

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See detailDerivation of high resolution TSM data by merging geostationary and polar-orbiting satellite data in the North Sea.
Alvera Azcarate, Aïda ULg; Barth, Alexander ULg; Vanhellemont, Quinten et al

Conference (2013, September 09)

There is a need for high resolution ocean colour data, both in space and time, for a better assessment of the variability of these data and their influence in the environment, specially at shallow areas ... [more ▼]

There is a need for high resolution ocean colour data, both in space and time, for a better assessment of the variability of these data and their influence in the environment, specially at shallow areas where factors as tides and wind play a role in their dynamics. High spatial resolution is achieved by polar-orbiting satellites, but at a low temporal resolution. The opposite is true for geostationary satellites. In order to exploit the complementary nature of geostationary and polar data, a merging methodology has been developed to obtain a unique estimate of the North Sea Total Suspended Matter (TSM). The largest difficulty in developing a merging methodology is the correct estimation of the error covariance matrix, which can be specially complex for variables like TSM. In this work, the error covariance is not parametrized a priori using an analytical expression, but expressed using a truncated spatial EOF basis calculated by analysing MODIS data using DINEOF (Data INterpolating Empirical Orthogonal Functions). This EOF basis represents more realistically the complex variability of the TSM data sets than the parametric covariance used in most optimal interpolation applications. This EOF basis is subsequently used to merge MODIS and SEVIRI TSM data using an optimal interpolation approach. Results for the North Sea 2009 TSM will be shown, demonstrating the possibilities of this technique. The influence of including variables like winds or tides in the analysis, through multivariate approaches, will be assessed. [less ▲]

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See detailSeasonal hypoxia in the Black Sea north-western shelf. Is there any recovery after eutrophication ?
Capet, Arthur ULg; Beckers, Jean-Marie ULg; Grégoire, Marilaure ULg

in 9th EGU General Assembly (2013, April 11)

The Black Sea North-western shelf (NWS) is a shallow eutrophic area in which seasonal tratification of the water column isolates bottom waters from the atmosphere and prevents entilation to compensate for ... [more ▼]

The Black Sea North-western shelf (NWS) is a shallow eutrophic area in which seasonal tratification of the water column isolates bottom waters from the atmosphere and prevents entilation to compensate for the large consumption of oxygen, due to respiration in the bottom aters and in the sediments. A 3D coupled physical biogeochemical model is used to investigate he dynamics of bottom hypoxia in the Black Sea NWS at different temporal scales from seasonal o interannual (1981-2009) and to differentiate the driving factors (climatic versus eutrophication) f hypoxic conditions in bottom waters. Model skills are evaluated by comparison with 14500 in- itu oxygen measurements available in the NOAA World Ocean Database and the Black Sea ommission data. The choice of skill metrics and data subselections orientate the validation rocedure towards specific aspects of the oxygen dynamics, and prove the model’s ability to esolve the seasonal cycle and interannual variability of oxygen concentration as well as the patial location of the oxygen depleted waters and the specific threshold of hypoxia. During the eriod 1981-2009, each year exhibits seasonal bottom hypoxia at the end of summer. This henomenon essentially covers the northern part of the NWS, receiving large inputs of nutrients rom the Danube, Dniestr and Dniepr rivers, and extends, during the years of severe hypoxia, owards the Romanian Bay of Constanta. In order to explain the interannual variability of bottom ypoxia and to disentangle its drivers, a statistical model (multiple linear regression) is proposed sing the long time series of model results as input variables. This statis- tical model gives a eneral relationships that links the intensity of hypoxia to eutrophication and climate related variables. The use of four predictors allows to reproduce 78% of hypoxia interannual variability: he annual nitrate discharge (N ), the sea surface temperature in the month preceding tratification (T ), the amount of semi-labile organic matter in the sediments (C) and the duration f the stratification (D). Eutrophication (N ,C) and climate (T ,D) predictors explain a similar mount of variability (∼ 35%) when considered separately. A typical timescale of 9.3 years is found to describe the inertia of sediments in the recovering process after eutrophication. From his analysis, we find that under standard conditions (i.e. average atmospheric conditions, ediments in equi- librium with river discharges), the intensity of hypoxia can be linked to the evel of nitrate discharge through a non-linear equation (power law). Bottom hypoxia does not ffect the whole Black Sea NWS but rather exhibits an important spatial variability. This heterogeneous distribution, in addition to the seasonal fluctuations, complicates the monitoring f ottom hypoxia leading to contradictory conclusions when the interpretation is done from different ets of data. We find that it was the case after 1995 when the recovery process was verestimated due to the use of observations concentrated in areas and months not typically ffected by hypoxia. This stresses out the urging need of a dedicated monitoring effort in the WS f the Black Sea focused on the areas and the period of the year concerned by recurrent hypoxic events. [less ▲]

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