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See detailGeneration of analysis and consistent error fields using the Data Interpolating Variational Analysis (Diva)
Troupin, Charles ULg; Barth, Alexander ULg; Sirjacobs, Damien ULg et al

in Ocean Modelling (2012), 52-53

The Data Interpolating Variational Analysis (Diva) is a method designed to interpolate irregularly-spaced, noisy data onto any desired location, in most cases on regular grids. It is the combination of a ... [more ▼]

The Data Interpolating Variational Analysis (Diva) is a method 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 minimisation of a cost function, and a numerically efficient method, based on a finite-element solver. The cost function penalises the misfit between the observations and the reconstructed field, as well as the regularity or smoothness of the field. The intrinsic advantages of the method 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. The method provides gridded fields in two dimensions, usually in horizontal layers. Three-dimension fields are obtained by stacking horizontal layers. In the present work, we summarize the background of the method and describe the possible methods to compute the error field associated to the analysis. In particular, we present new developments leading to a more consistent error estimation, by determining numerically the real covariance function in Diva, which is never formulated explicitly, contrarily to Optimal Interpolation. The real covariance function is obtained by two concurrent executions of Diva, the first providing the covariance for the second. With this improvement, the error field is now perfectly consistent with the inherent background covariance in all cases. A two-dimension application using salinity measurements in the Mediterranean Sea is presented. Applied on these measurements, Optimal Interpolation and Diva provided very similar gridded fields (correlation: 98.6%, RMS of the difference: 0.02). The method using the real covariance produces an error field similar to the one of OI, except in the coastal areas. [less ▲]

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See detailViewing through the clouds in satellite images
Troupin, Charles ULg; Barth, Alexander ULg; Alvera Azcarate, Aïda ULg et al

Poster (2012, February 24)

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See detailOutlier detection in satellite data using spatial coherence
Alvera Azcarate, Aïda ULg; Sirjacobs, Damien ULg; Barth, Alexander ULg et al

in Remote Sensing of Environment (2012), 119

Satellite data sets often contain outliers (i.e., anomalous values with respect to the surrounding pixels), mostly due to undetected clouds and rain or to atmospheric and land contamination. A methodology ... [more ▼]

Satellite data sets often contain outliers (i.e., anomalous values with respect to the surrounding pixels), mostly due to undetected clouds and rain or to atmospheric and land contamination. A methodology to detect outliers in satellite data sets is presented. The approach uses a truncated Empirical Orthogonal Function (EOF) basis. The information rejected by this EOF basis is used to identify suspect data. A proximity test and a local median test are also performed, and a weighted sum of these three tests is used to accurately detect outliers in a data set. Most satellite data undergo automated quality-check analyses. The approach presented exploits the spatial coherence of the geophysical fields, therefore detecting outliers that would otherwise pass such checks. The methodology is applied to infrared sea surface temperature (SST), microwave SST and chlorophyll-a concentration data over different domains, to show the applicability of the technique to a range of variables and temporal and spatial scales. A series of sensitivity tests and validation with independent data are also conducted. [less ▲]

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See detailStudy of the ecology, population structure and dynamic of the macroalgae Codium elisabethae in Faial (Azores) with underwater visible imagery.
Sirjacobs, Damien ULg

Doctoral thesis (2011)

Codium elisabethae O.C. Schmidt is a dark green globose macroalgae isolating an internal sea water volume in a lumen. Codium elisabethae is endemic to the Macaronesian region and is very similar to Codium ... [more ▼]

Codium elisabethae O.C. Schmidt is a dark green globose macroalgae isolating an internal sea water volume in a lumen. Codium elisabethae is endemic to the Macaronesian region and is very similar to Codium bursa C. Agardh whose distribution range spans the West-European, North-Western African and Mediterranean coasts and which was proposed as a potential indicator of coastal environmental changes based on the study of its ecology, revealing its long lifespan. Until recently, relatively little was known on Codium elisabethae as compared to the more widespread Codium bursa. To fill this gap, the present research aimed at producing an accurate description of the ecology and population dynamics of Codium elisabethae occupying the rocky shores of the Monte da Guia Special Area of Conservation (SAC)/Natura 2000 network (Faial, Azores). To achieve this, two reference sites were selected for long term monitoring: a sheltered no-go reserve exhibiting a dense Codium elisabethae population (Caldeirinhas), and a location experiencing more exposed conditions holding a sparser population (Ponta Furada). First, environmental conditions experimented by benthic organisms were extensively quantified and interpretated in regard to topographical particularities of each site. The study of reproduction dynamics showed a persistent summer fertility and an important vegetative reproduction. Important nutrient concentration ratio was found between the Codium elisabethae lumen water and surrounding sea water (mean ratios: nitrates: 5.7; ammonium: 3.4; phosphates: 3.1). In situ counting’s and size measurements revealed much higher densities of young recruits in the site of the Caldeirinhas (order of 20 ind/m²) than in the one of Ponta Furada (order of 1/m), for both summer and winter. Secondly, underwater visible imagery was exploited as an efficient and non-invasive alternative to classical in situ population estimation. Between August 2003 and November 2005, fifteen seabed photo coverages were collected by scuba-divers. Subsequent image processing consisted in mosaicing, interactive identification, and automatic change detection methods. This allowed quantifying the seasonal fluctuations of population structures (density, percentage cover and biomass) and of population dynamics (growth, recruitment, mortality and primary production). Chi-square tests of image-derived estimates and in situ measurements confirmed the validity of a centimeter precision for the estimation of population structure of individuals above 4 cm diameter. Important variability of population structure and density was observed within the sites at small spatial scales. Significant differences of population structure and dynamics parameters are demonstrated between two close-by but contrasting coastal habitats. Population density showed a sharp reduction in autumn 2003 and did not recover fully in spring and summer 2004. During the following year, population of the protected site maintained density and biomass, while at the exposed site population density dropped. In contrast with conclusions from earlier studies on the Azorian Codium elisabethae and on the Mediterranean Codium bursa, the present study revealed higher biomass (34 - 730 g dry wt.m-²), growth rates (up to 2.5 cm/month in summer) and primary production (0.53 – 11.5 g dry wt.m-².day-1), and demonstrated the seasonal fluctuations of these parameters for the studied Azorian Codium elisabethae population. The lifespan of Codium elisabethae was estimated to reach at least 7 years in the SAC of Monte da Guia based on an integration of average seasonal growth rates measured by imagery on extended population samples. This study demonstrates the high potential of registered underwater photomosaics time-series for long term surveys of macroalgae populations. This work provides also a strong framework to further developments and applicability to other species, which should be helpful to strengthen our current understanding of benthic ecosystem processes. [less ▲]

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

Poster (2011, March 21)

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See detailCloud filling of ocean colour and sea surface temperature remote sensing products over the Southern North Sea by the Data Interpolating Empirical Orthogonal Functions methodology.
Sirjacobs, Damien ULg; Alvera Azcarate, Aïda ULg; Barth, Alexander ULg et al

in Journal of Sea Research (2011), 65(1), 114-130

Optical remote sensing data is now being used systematically for marine ecosystem applications, such as the forcing of biological models and the operational detection of harmful algae blooms. However ... [more ▼]

Optical remote sensing data is now being used systematically for marine ecosystem applications, such as the forcing of biological models and the operational detection of harmful algae blooms. However, applications are hampered by the incompleteness of imagery and by some quality problems. The Data Interpolating Empirical Orthogonal Functions methodology (DINEOF) allows calculation of missing data in geophysical datasets without requiring a priori knowledge about statistics of the full data set and has previously been applied to SST reconstructions. This study demonstrates the reconstruction of complete space-time information for 4 years of surface chlorophyll a (CHL), total suspended matter (TSM) and sea surface temperature (SST) over the Southern North Sea (SNS) and English Channel (EC). Optimal reconstructions were obtained when synthesising the original signal into 8 modes for MERIS CHL and into 18 modes for MERIS TSM. Despite the very high proportion of missing data (70%), the variability of original signals explained by the EOF synthesis reached 93.5 % for CHL and 97.2 % for TSM. For the MODIS TSM dataset, 97.5 % of the original variability of the signal was synthesised into 14 modes. The MODIS SST dataset could be synthesised into 13 modes explaining 98 % of the input signal variability. Validation of the method is achieved for 3 dates below 2 artificial clouds, by comparing reconstructed data with excluded input information. Complete weekly and monthly averaged climatologies, suitable for use with ecosystem models, were derived from regular daily reconstructions. Error maps associated with every reconstruction were produced according to Beckers et al. (2006) [6]. Embedded in this error calculation scheme, a methodology was implemented to produce maps of outliers, allowing identification of unusual or suspicious data points compared to the global dynamics of the dataset. Various algorithms artefacts were associated with high values in the outlier maps (undetected cloud edges, haze areas, contrails, cloud shadows). With the production of outlier maps, the data reconstruction technique becomes also a very efficient tool for quality control of optical remote sensing data and for change detection within large databases. [less ▲]

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See detailData Interpolating Empirical Orthogonal Functions (DINEOF): a tool for geophysical data analyses
Alvera Azcarate, Aïda ULg; Barth, Alexander ULg; Sirjacobs, Damien ULg et al

in Mediterranean Marine Science (2011), 12(3), 5-11

An overview of the technique called DINEOF (Data Interpolating Empirical Orthog- onal Functions) is presented. DINEOF reconstructs missing information in geophys- ical data sets, such as satellite imagery ... [more ▼]

An overview of the technique called DINEOF (Data Interpolating Empirical Orthog- onal Functions) is presented. DINEOF reconstructs missing information in geophys- ical data sets, such as satellite imagery or time series. A summary of the technique is given, with its main characteristics, recent developments and future research di- rections. DINEOF has been applied to a large variety of oceanographic variables in various domains of different sizes. This technique can be applied to a single variable (monovariate approach), or to several variables together (multivariate approach), with no complexity increase in the application of the technique. Error fields can be computed to establish the accuracy of the reconstruction. Examples are given to illustrate the capabilities of the technique. DINEOF is freely offered to download, and help is provided to users in the form of a wiki and through a discussion email list. [less ▲]

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See detailHigh-resolution Climatology of the northeast Atlantic using Data-Interpolating Variational Analysis (DIVA)
Troupin, Charles ULg; Machin, Francis; Ouberdous, Mohamed ULg 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 detailSynthesis of regional product activities JRA4-JRA9
Beckers, Jean-Marie ULg; Alvera Azcarate, Aïda ULg; Barth, Alexander ULg et al

Conference (2010, April 01)

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See detailA web interface for gridding and visualizing oceanographic data sets
Barth, Alexander ULg; Alvera Azcarate, Aïda ULg; Sirjacobs, Damien ULg 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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See detailCloud-free satellite data for operational applications using DINEOF
Alvera Azcarate, Aïda ULg; Barth, Alexander ULg; Sirjacobs, Damien ULg et al

Conference (2010, February 24)

DINEOF (Data Interpolating Empirical Orthogonal Functions) is an EOF-based technique to reconstruct missing data in satellite data sets, such as gaps created by the presence of clouds. It is parameter ... [more ▼]

DINEOF (Data Interpolating Empirical Orthogonal Functions) is an EOF-based technique to reconstruct missing data in satellite data sets, such as gaps created by the presence of clouds. It is parameter-free, meaning that no a priori information is needed (such as signal to noise ratio, or correlation length) to calculate the missing data: this information is extracted from the data through the EOF decomposition. In addition, computational time is lower than for other frequently used techniques to reconstruct missing data in satellites, such as optimal interpolation. Multivariate reconstructions can be also done, using extended EOFs. These characteristics make DINEOF very suitable for operational reconstruction of satellite data. Recently added to DINEOF is a technique to filter the temporal covariance matrix which allows to reduce spurious variability in the temporal EOFs, and therefore leads to improved reconstructions. We will present a general description of the technology, with examples of applications to different variables. We will also give an example of a near real time reconstruction of sea surface temperature in the western Mediterranean Sea. Conceived as a demonstration product for DINEOF, it is hosted at http://gher-diva.phys.ulg.ac.be/DINEOF/dineof.html and it is automatically updated daily, presenting the cloud-free sea surface temperature for the last ten days, as well as the original data, outliers and error fields. [less ▲]

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See detailDIVA: new features
Beckers, Jean-Marie ULg; Alvera Azcarate, Aïda ULg; Barth, Alexander ULg et al

Scientific conference (2009, October 23)

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See detailHigh-resolution Climatology of the North-East Atlantic using Data-Interpolating Variational Analysis
Troupin, Charles ULg; Machín, Francisco; Ouberdous, Mohamed ULg et al

Conference (2009, April 21)

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See detailEvolution of Western Mediterranean Sea Surface Temperature between 1985 and 2005
Troupin, Charles ULg; Lenartz, Fabian; Sirjacobs, Damien ULg et al

Conference (2009, April 20)

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See detailWeekly satellite sea surface temperature around Corsica, a DINEOF analysis of AVHRR data (1998), foreseeing comparison with interpolated and modelled fields.
Sirjacobs, Damien ULg; Lenartz, Fabian ULg; Troupin, Charles ULg et al

Poster (2009, January)

Providing wide coverage and high spatio-temporal resolution, SST satellite archives are valuable sources of information for sound understanding of the ocean dynamics, including validation of ... [more ▼]

Providing wide coverage and high spatio-temporal resolution, SST satellite archives are valuable sources of information for sound understanding of the ocean dynamics, including validation of hydrodynamical modelling studies. Yet original SST fields have also many gaps (clouds, retrieval problems), but they are known to exhibit strong spatial and temporal correlations for regions of similar dynamics. This is exploited by the parameter free statistical technique DINEOF (Data Interpolation with Empirical Orthogonal Functions) [Alvera-Azcárate et al. (2005) Ocean Modell.; Beckers et al. (2006) Ocean Sciences] to produce full weekly analysis of the variability of the sea surface temperature (SST) around Corsica and in the Ligurian Sea at weekly temporal resolution during the year 1998. A detection of outliers implemented in DINEOF analysis is tested for pointing out unusual or invalid SST data. This study is realised foreseeing a comparison of DINEOF weekly averaged reconstructed fields with those obtained by interpolating methods on the same dataset (Data Interpolating Variationnal Analysis and Optimal Interpolation schemes), and with outputs of an implementation of the GHER 3D model in this area. [less ▲]

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