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See detailEOF analysis of Sea Surface Temperature in the Canary Island - Madeira region
Troupin, Charles ULiege; Alvera Azcarate, Aïda ULiege; Barth, Alexander ULiege et al

Conference (2011, April 05)

We analyzed Sea Surface Temperature (SST) images in a region covering the Canary Islands and Madeira archipelagos, with the following objectives 1. The reconstruction of incomplete SST satellite images ... [more ▼]

We analyzed Sea Surface Temperature (SST) images in a region covering the Canary Islands and Madeira archipelagos, with the following objectives 1. The reconstruction of incomplete SST satellite images during the year 2009. 2. The determination of the main spatial and temporal patters in the region. SST images for 2009 are downloaded from the Medspiration project (http://www.medspiration.org). The images consist of combined measurements from several satellite systems. The images with less than 5% of valid pixels (e.g., clouds) were removed, so that out of the 365 initial images, 347 were kept. The method used in this work for the reconstruction of missing data is Data INterpolating Empirical Orthogonal Functions (DINEOF, Alvera-Azcárate et al., 2005). The results show that the first mode is largely dominant, with 87% of the variance explained, and represents the regional seasonal cycle. The second mode accounts for 9% of the variance and depicts a separation between coastal waters and open-ocean waters. The signal of the Cape Ghir upwelling filament is also present in the second mode. The reconstruction allows one to reproduce the characteristic mesoscale features of the region: the coastal upwelling, the island wakes (Gran Canaria, Madeira, ... ), the filament and the eddies in the lee of the main islands. A near-operational version of the reconstruction has been implemented and is available at http://gher-diva.phys.ulg.ac.be/DINEOF/dineof_allCAN.html [less ▲]

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See detailAdvanced Data Interpolating Variational Analysis. Application to climatological data
Troupin, Charles ULiege; Sirjacobs, Damien ULiege; Rixen, Michel et al

Poster (2011, April)

DIVA (Data Interpolating Variational Analysis) is a variational analysis tool designed to interpolate irregularly-spaced, noisy data onto any desired location, in most cases on regular grids. It is the ... [more ▼]

DIVA (Data Interpolating Variational Analysis) is a variational analysis tool designed to interpolate irregularly-spaced, noisy data onto any desired location, in most cases on regular grids. It is the combination of a particular methodology, based on the minimization of a functional, and a numerically efficient resolution method, based on a finite elements solver. The intrinsic advantages of DIVA are its natural way to take into account topographic and dynamic constraints (coasts, advection, ...) and its capacity to handle large data sets, frequently encountered in oceanography. In the present work, we describe various improvements to the variational analysis tool. The most significant advance is the development of a full error calculation, whilst until now, only an approximate error-field estimate was available. The key issue is the numerical determination of the real covariance function in DIVA, which is not formulated explicitly. This is solved by two concurrent executions of two DIVA, one providing the covariance for the other. The new calculation of the error field is now perfectly coherent with the inherent background covariance in all cases. The correlation length, which was previously set uniform over the computational domain, is now allowed to vary spatially. The efficiency of the tools for estimating the signal-to-noise ratio, through generalized cross-validation, has also been improved. Finally, a data quality-control method is implemented and allows one to detect possible outliers, based on statistics of the data-reconstruction misfit. The added value of these features are illustrated in the case of a large data set of salinity measured in the Mediterranean Sea. Several analyses are performed with different parameters in order to demonstrate their influence on the interpolated fields. In particular, we examine the benefits of using the parameter optimization tools and the advection constraint. The results are validated by means of a subset of data set apart for an independent validation. The corresponding errors fields are estimated using different methods and underline the role of the data coverage. [less ▲]

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See detailSeaDataNet regional climatologies: an overview
Troupin, Charles ULiege; Ouberdous, Mohamed ULiege; Barth, Alexander ULiege et al

Poster (2011, April)

In the frame of the SeaDataNet project, a set of regional climatologies for temperature and salinity has been developed by the different regional groups. The data used for these climatologies are ... [more ▼]

In the frame of the SeaDataNet project, a set of regional climatologies for temperature and salinity has been developed by the different regional groups. The data used for these climatologies are distributed by the SeaDataNet data centers. These climatologies have several uses: 1. The detection of outliers by comparison of the in situ data with the climatological fields; 2. The the optimization of locations of new observations; 3. The initialization of numerical hydrodynamic model; 4. The definition of a reference state to identify anomalies and to detect long-term climatic trends. These climatologies are produced with the help of the Data Interpolating Variational Analysis (DIVA) software. Here we present the latest developments in the regional climatologies along with the choice of parameters by the different groups. [less ▲]

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See detailEvidence of wind-induced temperature anomalies in the tropical and subtropical North Atlantic Ocean in winter-spring 2010
Troupin, Charles ULiege; Machín, Francis

Poster (2011, April)

During the first months of 2010, the tropical and subtropical North Atlantic displayed anomalously high temperatures, with values seldom observed during the last decades. In situ and remote sensing data ... [more ▼]

During the first months of 2010, the tropical and subtropical North Atlantic displayed anomalously high temperatures, with values seldom observed during the last decades. In situ and remote sensing data are used to evaluate horizontal, vertical and temporal extensions of the anomalies. The repercussions on the seasonal evolution of the mixed layer are examined; in particular, it is shown that the northwest Africa coastal upwelling is significantly weakened in comparison to previous years. The consequences on the biological variables are examined by means of satellite-derived measurements. A simple mechanism related to changes in wind intensity is proposed in order to explain our observations. The wind weakening coincides with a strongly negative value of the North Atlantic Oscillation index. [less ▲]

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See detailAdvanced Data Interpolating Variational Analysis. Application to climatological data.
Troupin, Charles ULiege; Sirjacobs, Damien ULiege; Rixen, Michel et al

Poster (2011, March 21)

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See detailComparison between satellite and in situ sea surface temperature data in the Western Mediterranean Sea
Alvera Azcarate, Aïda ULiege; Troupin, Charles ULiege; Barth, Alexander ULiege et al

in Ocean Dynamics (2011), 61(6), 767-778

A comparison between in situ and satellite sea surface temperature (SST) is presented for the western Mediterranean Sea during 1999. Several international databases are used to extract in situ data (World ... [more ▼]

A comparison between in situ and satellite sea surface temperature (SST) is presented for the western Mediterranean Sea during 1999. Several international databases are used to extract in situ data (World Ocean Database (WOD), MEDAR/Medatlas, Coriolis Data Center, International Council for the Exploration of the Sea (ICES) and International Comprehensive Ocean-Atmosphere Data Set (ICOADS)). The in situ data are classified into different platforms or sensors (CTD, XBT, drifters, bottles, ships), in order to assess the relative accuracy of these type of data respect to AVHRR (Advanced Very High Resolution Radiometer) SST satellite data. It is shown that the results of the error assessment vary with the sensor type, the depth of the in situ measurements, and the database used. Ship data are the most heterogeneous data set, and therefore present the largest differences with respect to in situ data. A cold bias is detected in drifter data. The differences between satellite and in situ data are not normally distributed. However, several analysis techniques, as merging and data assimilation, usually require Gaussian-distributed errors. The statistics obtained during this study will be used in future work to merge the in situ and satellite data sets into one unique estimation of the SST. [less ▲]

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See detailCAIBEX workshop: Mesoscale experiments and modelling - Cape Ghir
Troupin, Charles ULiege; Sangrà, Pablo; Arístegui, Javier

Scientific conference (2010, November 29)

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See detailA web interface for griding arbitrarily distributed in situ data based on Data-Interpolating Variational Analysis (DIVA)
Barth, Alexander ULiege; Alvera Azcarate, Aïda ULiege; Troupin, Charles ULiege et al

in Advances in Geosciences (2010), 28(28), 29-37

Spatial interpolation of observations on a regular grid is a common task in many oceanographic disciplines (and geosciences in general). It is often used to create climatological maps for physical ... [more ▼]

Spatial interpolation of observations on a regular grid is a common task in many oceanographic disciplines (and geosciences in general). It is often used to create climatological maps for physical, biological or chemical parameters representing e.g. monthly or seasonally averaged fields. Since instantaneous observations can not be directly related to a field representing an average, simple spatial interpolation of observations is in general not acceptable. DIVA (Data-Interpolating Variational Analysis) is an analysis tool which takes the error in the observations and the typical spatial scale of the underlying field into account. Barriers due to the coastline and the topography in general and also currents estimates (if available) are used to propagate the information of a given observation spatially. DIVA is a command-line driven application written in Fortran and Shell Scripts. To make DIVA easier to use, a web interface has been developed (http://gher-diva.phys.ulg.ac.be). Installation and compilation of DIVA is therefore not required. The user can directly upload the data in ASCII format and enter several parameters for the analysis. The analyzed field, location of the observations, and the error mask are presented as different layers using the Web Map Service protocol. They are visualized in the browser using the Javascript library OpenLayers allowing the user to interact with layers (for example zooming and panning). Finally, the results can be downloaded as a NetCDF file, Matlab/Octave file and Keyhole Markup Language (KML) file for visualization in applications such as Google Earth. [less ▲]

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See detailEMODNET Chemical Data Products Experts Workshop
Troupin, Charles ULiege

Scientific conference (2010, September 20)

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See detailHigh-resolution Climatology of the northeast Atlantic using Data-Interpolating Variational Analysis (DIVA)
Troupin, Charles ULiege; Machin, Francis; Ouberdous, Mohamed ULiege et al

in Journal of Geophysical Research (2010), 115(C08005), 20

Numerous climatologies are available at different resolutions and cover various parts of the global ocean. Most of them have a resolution too low to represent suitably regional processes and the methods ... [more ▼]

Numerous climatologies are available at different resolutions and cover various parts of the global ocean. Most of them have a resolution too low to represent suitably regional processes and the methods for their construction are not able to take into account the influence of physical effects (topographic constraints, boundary conditions, advection, etc.). A high-resolution atlas for temperature and salinity is developed for the northeast Atlantic Ocean on 33 depth levels. The originality of this climatology is twofold: (1) For the data set, data are collected on all major databases and aggregated to lead to an original data collection without duplicates, richer than the World Ocean Database 2005, for the same region of interest. (2) For the method, climatological fields are constructed using the variational method Data-Interpolating Variational Analysis. The formulation of the latter allows the consideration of coastlines and bottom topography, and has a numerical cost almost independent on the number of observations. Moreover, only a few parameters, determined in an objective way, are necessary to perform an analysis. The results show overall good agreement with the widely used World Ocean Atlas, but also reveal significant improvements in coastal areas. Error maps are generated according to different theories and emphasize the importance of data coverage for the creation of such climatological fields. Automatic outlier detection is performed, and its effects on the analysis are examined. The method presented here is very general and not dependent on the region, hence it may be applied for creating other regional atlas in different zones of the global ocean. [less ▲]

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See detailSuper-ensemble techniques applied to wave forecast: performance and limitations
Lenartz, Fabian ULiege; Beckers, Jean-Marie ULiege; Chiggiato, Jacopo et al

in Ocean Science (2010), 6(2), 595-604

Nowadays, several operational ocean wave forecasts are available for a same region. These predictions may considerably differ, and to choose the best one is generally a difficult task. The super-ensemble ... [more ▼]

Nowadays, several operational ocean wave forecasts are available for a same region. These predictions may considerably differ, and to choose the best one is generally a difficult task. The super-ensemble approach, which consists in merging different forecasts and past observations into a single multi-model prediction system, is evaluated in this study. During the DART06 campaigns organized by the NATO Undersea Research Centre, four wave forecasting systems were simultaneously run in the Adriatic Sea, and significant wave height was measured at six stations as well as along the tracks of two remote sensors. This effort provided the necessary data set to compare the skills of various multi-model combination techniques. Our results indicate that a super-ensemble based on the Kalman Filter improves the forecast skills: The bias during both the hindcast and forecast periods is reduced, and the correlation coefficient is similar to that of the best individual model. The spatial extrapolation of local results is not straightforward and requires further investigation to be properly implemented. [less ▲]

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See detailComparison between in situ and satellite surface temperature in the Western Mediterranean Sea
Alvera Azcarate, Aïda ULiege; Barth, Alexander ULiege; Troupin, Charles ULiege et al

Conference (2010, May 06)

A comparison between satellite and in situ sea surface temperature (SST) data in the Western Mediterranean Sea in 1999 is realised. The aim of this study is to better understand the differences between ... [more ▼]

A comparison between satellite and in situ sea surface temperature (SST) data in the Western Mediterranean Sea in 1999 is realised. The aim of this study is to better understand the differences between these two data sets, in order to realise merged maps of SST using satellite and in situ data. When merging temperature from different platforms, it is crucial to take the expected RMS error of the observations into account and to correct for possible biases. Advanced Very High Resolution Radiometer (AVHRR) SST day-time and night-time satellite data are used, and the in situ data have been obtained from various databases (World Ocean Database’05, Coriolis, Medar/Medatlas and ICES). Statistics about the differences due to the hour of the day, the month of the year, the type of sensor/platform used (CTD, XBT, drifter, etc) and the spatial distribution are made using a combination of error measures, diagrams and statistical hypothesis testing. In addition to quantify the errors between different platforms, several assumptions often made when creating gridded analyses will be critically reviewed: unbiased data sets, non-correlated errors of the observations, spatially uniform variance, and Gaussian-distributed data. [less ▲]

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See detailError assessment of sea surface temperature satellite data relative to in situ data: effect of spatial and temporal coverage
Alvera Azcarate, Aïda ULiege; Barth, Alexander ULiege; Troupin, Charles ULiege et al

Conference (2010, April 30)

A comparison between satellite and in situ sea surface temperature (SST) data in the Western Mediterranean Sea in 1999 is shown. The aim of this study is to better understand the differences between these ... [more ▼]

A comparison between satellite and in situ sea surface temperature (SST) data in the Western Mediterranean Sea in 1999 is shown. The aim of this study is to better understand the differences between these two data sets, in order to compute merged maps of SST using satellite and in situ data. When merging temperature from different platforms, it is crucial to take the expected RMS error of the observations into account and to correct for possible biases. Different in situ data sensors and platforms (CTD, XBT, drifter, etc) are available for the comparison, each with specificities in the nature of the measurement (accuracy and precision of the measures), and with different spatial and temporal distributions. A comparison with satellite data needs to take these factors into account. Statistics about the differences due to the hour of the day, the month of the year, the type of sensor/ platform used and the spatial distribution is therefore realised through a combination of error measures, diagrams and statistical hypothesis testing. The data used are Advanced Very High Resolution Radiometer (AVHRR) SST day-time and night-time satellite data, and in situ temperature data from various databases (World Ocean Database’05, Coriolis, Medar/Medatlas and ICES). [less ▲]

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See detailHigh-resolution measurements and modelling of the Cape Ghir upwelling filament during the CAIBEX cruise
Troupin, Charles ULiege; Beckers, Jean-Marie ULiege; Sangrà, Pablo et al

Conference (2010, April 26)

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See detailHigh-resolution measurements and modelling of the Cape Ghir upwelling filament during the CAIBEX survey
Troupin, Charles ULiege; Sangrà, Pablo; Arístegui, Javier et al

Poster (2010, April 26)

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See detailSynthesis of regional product activities JRA4-JRA9
Beckers, Jean-Marie ULiege; Alvera Azcarate, Aïda ULiege; Barth, Alexander ULiege et al

Conference (2010, April 01)

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See detailGODIVA: a 4-dimension implementation of DIVA
Troupin, Charles ULiege; Ouberdous, Mohamed ULiege; Beckers, Jean-Marie ULiege

Poster (2010, March 29)

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See detailSeaDataNet regional climatologies: an overview
Troupin, Charles ULiege; Ouberdous, Mohamed ULiege; Barth, Alexander ULiege et al

Poster (2010, March 29)

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See detailSeasonal variability of the oceanic upper layer and its modulation of biological cycles in the Canary Island region
Troupin, Charles ULiege; Sangrà, Pablo; Arístegui, Javier

in Journal of Marine Systems (2010), 80(3-4), 172-183

The Canary Island region is rich in mesoscale phenomena that affect cycles of physical and biological processes. A 1D version of the Regional Oceanic Modeling System (ROMS) is used south of the Gran ... [more ▼]

The Canary Island region is rich in mesoscale phenomena that affect cycles of physical and biological processes. A 1D version of the Regional Oceanic Modeling System (ROMS) is used south of the Gran Canaria Island to simulate seasonal climatologies of these cycles. The model is forced with monthly air–sea fluxes averaged from 1993 to 2002 and initialized with mean in situ profiles of temperature, salinity, oxygen and nitrate concentrations. The K-Profile Parameterization (KPP) mixed layer submodel is compared with other submodels using idealized numerical experiments. When forced with realistic air–sea fluxes, the model correctly reproduces the annual cycle of temperature (mixed layer depth), with minimum surface values of 18 °C (maximal depth > 105 m) in February during convective mixing resulting from a negative heat flux. Maximum temperatures above 23 °C (minimal depth < 20 m) are simulated from September to October after strong summer heating and a decrease in Trade Winds intensity. A simple ecosystem model is coupled to the physical model, which provides simulated biological cycles that are in agreement with regional observations. A phytoplankton bloom develops in late winter, driven by the injection of new nutrients into the euphotic layer. Simulated chlorophyll shows a deep maximum fluctuating around 100 m with concentrations around 1 mg Chla /m³, while surface values are low (around 0.1 mg Chla /m³) during most of the year. The physical and biological model results are validated by comparisons with data from regional studies, climatological fields and time-series from the ESTOC station. [less ▲]

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See detailA web interface for gridding and visualizing oceanographic data sets
Barth, Alexander ULiege; Alvera Azcarate, Aïda ULiege; Sirjacobs, Damien ULiege et al

Conference (2010, March)

Spatial interpolation of observations on a regular grid is a common task in many oceanographic disciplines (and geosciences in general). Diva (Data-Interpolating Variational Analysis) is an analysis tool ... [more ▼]

Spatial interpolation of observations on a regular grid is a common task in many oceanographic disciplines (and geosciences in general). Diva (Data-Interpolating Variational Analysis) is an analysis tool for gridding oceanographic in situ data. Diva takes the error in the observations and the typical spatial scale of the underlying field into account. Barriers due to the coastline and the topography in general are also used to propagate the information of a given observation spatially. Diva is a command-line driven application. To make Diva easier to use, a web interface has been developed. The user can directly upload his/her data in ASCII format and enter several parameters for the analysis. The analyzed field, location of the observations, and the error mask are then directly visualized in the browser. While this interface allows the user to create his/her own gridded field, a web interface is also developed to visualize pre-computed gridded oceanographic data sets. Those data sets are typically four-dimensional (longitude, latitude, depth and time). The system allows to visualize horizontal sections at a given depth and time to study the horizontal distribution of a given variable. It is also possible 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. 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 system is build using a client and server architecture. The server is written in Python using the Web Server Gateway Interface. The server implements version 1.1.1 and 1.3.0 of the Web Map Service (WMS) protocol of the Open Geospatial Consortium. On the server, all oceanographic data sets are stored as NetCDF files organized in folders and sub-folders allowing for a hierarchical presentation of the available variables. The client is build as a web application using the OpenLayers Javascript library. The web interface is accessible at http://gher-diva.phys.ulg.ac.be/. It is currently used for climatologies created in the frame of the SeaDataNet project and will be used for the EMODNET project (chemical lot). Thrid-party data centers can also integrate the web interface of Diva to show an interpolated field of in situ data as an additional WMS layer. A demonstration near-real time cloud-free sea surface temperature (SST) product of the Mediterranean Sea is presented. The reconstruction of the data set missing information (due to clouds, for example) is realised using DINEOF (Data Interpolating Empirical Orthogonal Functions). DINEOF is an EOF-based technique that does no need a priori information about the data set (such as signal to noise ratio, or correlation length) and that has shown to be faster and equally reliable than other widely used techniques for reconstructing missing data, such as optimal interpolation. Here we present a daily reconstruction of the Western Mediterranean SST. Cloudy data are downloaded from the Ifremer Medspiration ftp site. After extracting the data from the study zone, they are added to a data set containing the last 6 months of SST. A first DINEOF reconstruction is performed to identify outliers, i.e. pixels for which the analysis-observation difference (the residuals) are larger than the statistically expected misfit calculated during the analysis. Proximity to a cloud edge and deviation respect to a local median also penalize a pixel in the outlier classification. These outliers are removed from the original data set, and a second DINEOF reconstruction is performed, along with the calculation of error maps. Plots are realised, and the reconstruction of the latest 10 days is shown at http://gher-diva.phys.ulg.ac.be/DINEOF/dineof.html, together with the original data, the error maps and identified outliers. The whole procedure takes less than two hours and has been running automatically for more than 5 months. This product is intended as a demonstration of the capabilities of DINEOF as a near-real time technique to reconstruct missing data in satellite data sets. This procedure can be easily applied to other variables and other geographical zones. [less ▲]

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