References of "Beckers, Jean-Marie"
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See detailStatus of Diva online as VRE application
Barth, Alexander ULiege; Troupin, Charles ULiege; Watelet, Sylvain ULiege et al

Conference (2017, October 19)

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See detailNew Diva capabilities for climatologies
Barth, Alexander ULiege; Troupin, Charles ULiege; Watelet, Sylvain ULiege et al

Conference (2017, October 17)

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See detailProgress with analysis of DIVA as cloud service
Barth, Alexander ULiege; Troupin, Charles ULiege; Watelet, Sylvain ULiege et al

Conference (2017, October 16)

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See detailExperiences with using netCDF 4
Troupin, Charles ULiege; Watelet, Sylvain ULiege; Barth, Alexander ULiege et al

Conference (2017, October 05)

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See detailNotebooks for documenting work-flows
Troupin, Charles ULiege; Barth, Alexander ULiege; Muñoz, Cristian et al

Conference (2017, October 02)

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See detailSoftware citation and process traceability
Troupin, Charles ULiege; Muñoz, Cristian; Rújula, Miquel Angel et al

Conference (2017, October)

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See detailCharacteristics of surface chlorophyll-a concentrations in the South China Sea
Huynh, Thi Hong Ngu ULiege; Alvera Azcarate, Aida ULiege; Barth, Alexander ULiege et al

Poster (2017, April 25)

In this study, the spatial and temporal variability of surface chlorophyll-a (Chl-a) concentrations in the South China Sea (SCS) is investigated, using the cloud-free MODISA Chl-a data set (2003-2016 ... [more ▼]

In this study, the spatial and temporal variability of surface chlorophyll-a (Chl-a) concentrations in the South China Sea (SCS) is investigated, using the cloud-free MODISA Chl-a data set (2003-2016) reconstructed by the Data Interpolating Empirical Orthogonal Functions technique. EOF analysis on the reconstructed data set presents the characteristics of the surface Chl-a: (1) the first mode presents the high Chl-a concentrations in the coastal regions, except those of the Palawan and Philippines, generally with peaks in summer (June-July) and winter (November-December). (2) the second mode shows the seasonal variability of Chl-a in the whole basin, increasing in winter and decreasing in summer. (3) the third mode highlights the out-of-phase variability of the southern SCS Chl-a between the west and east coasts in winter and summer. The analysis also indicates that the variability of surface Chl-a is influenced by ENSO with a time lag of 5-9 months. [less ▲]

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See detailBlending of Radial HF Radar Surface Current and Model Using ETKF Scheme For The Sunda Strait
Subekti Mujiasih, ULiege; Riyadi, Mochammad; Wandono, Dr et al

Conference (2017)

Preliminary study of data blending of surface current for Sunda Strait-Indonesia has been done using the analysis scheme of the Ensemble Transform Kalman Filter (ETKF). The method is utilized to combine ... [more ▼]

Preliminary study of data blending of surface current for Sunda Strait-Indonesia has been done using the analysis scheme of the Ensemble Transform Kalman Filter (ETKF). The method is utilized to combine radial velocity from HF Radar and u and v component of velocity from Global Copernicus - Marine environment monitoring service (CMEMS) model. The initial ensemble is based on the time variability of the CMEMS model result. Data tested are from 2 CODAR Seasonde radar sites in Sunda Strait and 2 dates such as 09 September 2013 and 08 February 2016 at 12.00 UTC. The radial HF Radar data has a hourly temporal resolution, 20-60 km of spatial range, 3 km of range resolution, 5 degree of angular resolution and spatial resolution and 11.5-14 MHz of frequency range. The u and v component of the model velocity represents a daily mean with 1/12 degree spatial resolution. The radial data from one HF radar site is analyzed and the result compared to the equivalent radial velocity from CMEMS for the second HF radar site. Error checking is calculated by root mean squared error (RMSE). Calculation of ensemble analysis and ensemble mean is using Sangoma software package. The tested R which represents observation error covariance matrix, is a diagonal matrix with diagonal elements equal 0.05, 0.5 or 1.0 m 2 /s 2 . The initial ensemble members comes from a model simulation spanning a month (September 2013 or February 2016), one year (2013) or 4 years (2013-2016). The spatial distribution of the radial current are analyzed and the RMSE values obtained from independent HF radar station are optimized. It was verified that the analysis reproduces well the structure included in the analyzed HF radar data. More importantly, the analysis was also improved relative to the second independent HF radar site. RMSE of the improved analysis is better than first HF Radar site Analysis. The best result of the blending exercise was obtained for observation error variance equal to 0.05 m 2 /s 2 . This study is still preliminary step, but it gives promising result for bigger size of data, combining other model and further development [less ▲]

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See detailCorrecting circulation biases in a lower-resolution global general circulation model with data assimilation
Canter, Martin ULiege; Barth, Alexander ULiege; Beckers, Jean-Marie ULiege

in Ocean Dynamics (2016)

In this study, we aim at developing a new method of bias correction using data assimilation. This method is based on the stochastic forcing of a model to correct bias by directly adding an additional ... [more ▼]

In this study, we aim at developing a new method of bias correction using data assimilation. This method is based on the stochastic forcing of a model to correct bias by directly adding an additional source term into the model equations. This method is presented and tested first with a twin experiment on a fully controlled Lorenz ’96 model. It is then applied to the lower-resolution global circulation NEMO-LIM2 model, with both a twin experiment and a real case experiment. Sea surface height observations are used to create a forcing to correct the poorly located and estimated currents. Validation is then performed throughout the use of other variables such as sea surface temperature and salinity. Results show that the method is able to consistently correct part of the model bias. The bias correction term is presented and is consistent with the limitations of the global circulation model causing bias on the oceanic currents. [less ▲]

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See detailCorrection of inertial oscillations by assimilation of HFradar data in a model of the Ligurian Sea
Vandenbulcke, Luc ULiege; Barth, Alexander ULiege; Beckers, Jean-Marie ULiege

in Ocean Dynamics (2016)

This article aims at analyzing if high-frequency radar observations of surface currents allow to improve model forecasts in the Ligurian Sea, where inertial oscillations are a dominant feature. An ... [more ▼]

This article aims at analyzing if high-frequency radar observations of surface currents allow to improve model forecasts in the Ligurian Sea, where inertial oscillations are a dominant feature. An ensemble of ROMS models covering the Ligurian Sea, and nested in the Mediterranean Forecasting System, is coupled with two WERA high-frequency radars. A sensitivity study allows to determine optimal parameters for the ensemble filter. By assimilating observations in a single point, the obtained correction shows that the forecast error covariance matrix represents the inertial oscillations, as well as large- and meso-scale processes. Furthermore, it is shown that the velocity observations can correct the phase and amplitude of the inertial oscillations. Observations are shown to have a strong effect during approximately half a day, which confirms the importance of using a high temporal observation frequency. In general, data assimilation of HF radar observations leads to a skill score of about 30 % for the forecasts of surface velocity. [less ▲]

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See detail10th Diva user workshop
Watelet, Sylvain ULiege; Barth, Alexander ULiege; Troupin, Charles ULiege et al

Scientific conference (2016, October 03)

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See detailOceanBrowser: on-line visualization of gridded ocean data and in situ observations
Barth, Alexander ULiege; Watelet, Sylvain ULiege; Troupin, Charles et al

Conference (2016, October)

Detailed reference viewed: 18 (4 ULiège)
See detailDINEOF analyses of Sea Surface Temperature data in the Black Sea
Alvera Azcarate, Aida ULiege; Vandenbulcke, Luc ULiege; Barth, Alexander ULiege et al

Scientific conference (2016, September 29)

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See detailAnalysis of Ocean in Situ Observations and Web - Based Visualization: From Individual Measurements to an Integrated View
Barth, Alexander ULiege; Watelet, Sylvain ULiege; Troupin, Charles et al

in Diviacco, Paolo; Leadbetter, Adam; Glaves, Helen (Eds.) Oceanographic and Marine Cross-Domain Data Management for Sustainable Development (2016)

The sparsity of observations poses a challenge common to various ocean disciplines. Even for physical parameters where the spatial and temporal coverage is higher, current observational networks ... [more ▼]

The sparsity of observations poses a challenge common to various ocean disciplines. Even for physical parameters where the spatial and temporal coverage is higher, current observational networks undersample a broad spectrum of scales. This situation is generally more severe for chemical and biological parameters because such sensors are less widely deployed. The present chapter describes the analysis tool DIVA (Data-Interpolating Variational Analysis) which is designed to generate gridded fields from in situ observations. DIVA has been applied to various physical (temperature and salinity), chemical (concentration of nitrate, nitrite and phosphate) and biological parameters (abundance of a species). The chapter also shows the technologies used to visualize the gridded fields. Visualization of analyses from in situ observations provide a unique set of challenges since the accuracy of the analysed field is not spatially uniform as it strongly depends on the location of the observations. In addition, an adequate treatment of the depth and time dimensions is essential. [less ▲]

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See detailAnalysis of SMOS sea surface salinity data using DINEOF
Alvera Azcarate, Aïda ULiege; Barth, Alexander ULiege; Parard, Gaëlle ULiege et al

in Remote Sensing of Environment (2016), 180

n analysis of daily Sea Surface Salinity (SSS) at 0.15 ° × 0.15° spatial resolution from the Soil Moisture and Ocean Salinity (SMOS) satellite mission using DINEOF (Data Interpolating Empirical Orthogonal ... [more ▼]

n analysis of daily Sea Surface Salinity (SSS) at 0.15 ° × 0.15° spatial resolution from the Soil Moisture and Ocean Salinity (SMOS) satellite mission using DINEOF (Data Interpolating Empirical Orthogonal Functions) is presented. DINEOF allows reconstructing missing data using a truncated EOF basis, while reducing the amount of noise and errors in geophysical datasets. This work represents a first application of DINEOF to SMOS SSS. Results show that a reduction of the error and the amount of noise is obtained in the DINEOF SSS data compared to the initial SMOS SSS data. Errors associated to the edge of the swath are detected in 2 EOFs and effectively removed from the final data, avoiding removing the data at the edges of the swath in the initial dataset. The final dataset presents a centered root mean square error of 0.2 in open waters when comparing with thermosalinograph data at their original spatial and temporal resolution. Constant biases present near land masses, large scale biases and latitudinal biases cannot be corrected with DINEOF because persistent signals are retained in high order EOFs, and therefore these need to be corrected separately. The signature of the Douro and Gironde rivers is detected in the DINEOF SSS. The minimum SSS observed in the Gironde plume corresponds to a flood event in June 2013, and the shape and size of the Douro river shows a good agreement with chlorophyll-a satellite data. These examples show the capacity of DINEOF to remove noise and provide a full SSS dataset at a high temporal and spatial resolution with reduced error, and the possibility to retrieve physical signals in zones with high initial errors. [less ▲]

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See detailData-Interpolating Variational Analysis (DIVA) software: recent development and application
Watelet, Sylvain ULiege; Back, Örjan; Barth, Alexander ULiege et al

Poster (2016, April 20)

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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 ULiege; Alvera Azcarate, Aïda ULiege; Barth, Alexander ULiege 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 detailAnalysis of ocean in situ observations and web-based visualization
Barth, Alexander ULiege; Watelet, Sylvain ULiege; Troupin, Charles ULiege et al

Conference (2016)

The sparsity of observations poses a challenge common to various ocean science disciplines. Even for physical parameters where the spatial and temporal coverage is higher, current observational networks ... [more ▼]

The sparsity of observations poses a challenge common to various ocean science disciplines. Even for physical parameters where the spatial and temporal coverage is higher, current observational networks undersample a broad spectrum of scales. The situation is generally more severe for chemical and biological parameters because related sensors are less widely deployed. The analysis tool DIVA (Data-Interpolating Variational Analysis) is designed to generate gridded fields from in situ observations. DIVA has been applied to various physical (temperature and salinity), chemical (concentration of nitrate, nitrite and phosphate) and biological parameters (abundance of a species) in the context of different European projects (SeaDataNet, EMODnet Chemistry and EMODnet Biology). We show the technologies used to visualize the gridded fields based on the Web Map Services standard. Visualization of analyses from in situ observations provides a unique set of challenges since the accuracy of the analysed field is not spatially uniform as it strongly depends on the observations location. In addition, an adequate handling of depth and time dimensions is essential. Beside visualizing the gridded fields, access is also given to the underlying observations. It is thus also possible to view more detailed information about the variability of the observations. The in situ observation visualization service allows one to display vertical profiles and time series and it is built upon OGC standards (the Web Feature Service and Web Processing Services) and following recommendation from the INSPIRE directive. [less ▲]

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See detailDecline of the Black Sea oxygen inventory
Capet, Arthur ULiege; Stanev, Emil; Beckers, Jean-Marie ULiege et al

in Biogeosciences (2016), 13

Detailed reference viewed: 73 (11 ULiège)