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See detailThermocline characterisation in the Cariaco basin: A modelling study of the thermocline annual variation and its relation with winds and chlorophyll-a concentration
Alvera Azcarate, Aïda ULg; Barth, Alexander ULg; Weisberg, Robert H. et al

in Continental Shelf Research (2011), 31(1), 73-84

The spatial and temporal evolution of the thermocline depth and width of the Cariaco basin (Venezuela) is analysed by means of a three-dimensional hydrodynamic model. The thermocline depth and width are ... [more ▼]

The spatial and temporal evolution of the thermocline depth and width of the Cariaco basin (Venezuela) is analysed by means of a three-dimensional hydrodynamic model. The thermocline depth and width are determined through the fitting of model temperature profiles to a sigmoid function. The use of whole profiles for the fitting allows for a robust estimation of the thermocline characteristics, mainly width and depth. The fitting method is compared to the maximum gradient approach, and it is shown that, under some circumstances, the method presented in this work leads to a better characterization of the thermocline. After assessing, through comparison with independent {\it in situ} data, the model capabilities to reproduce the Cariaco basin thermocline, the seasonal variability of this variable is analysed, and the relationship between the annual cycle of the thermocline depth, the wind field and the distribution of chlorophyll-a concentration in the basin is studied. The interior of the basin reacts to easterly winds intensification with a rising of the thermocline, resulting in a coastal upwelling response, with the consequent increase in chlorophyll-a concentration. Outside the Cariaco basin, where an open-ocean, oligothrophic regime predominates, wind intensification increases mixing of the surface layers and induces therefore a deepening of the thermocline. The seasonal cycle of the thermocline variability in the Cariaco basin is therefore related to changes in the wind field. At shorter time scales (i.e. days), it is shown that other processes, such as the influence of the meandering Caribbean Current, can also influence the thermocline variability. The model thermocline depth is shown to be in good agreement with the two main ventilation events that took place in the basin during the period of the simulation. [less ▲]

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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 detailCorrecting surface winds by assimilating High-Frequency Radar surface currents in the German Bight
Barth, Alexander ULg; Alvera Azcarate, Aïda ULg; Beckers, Jean-Marie ULg et al

in Ocean Dynamics (2011), 61(5), 599-610

Surface winds are crucial for accurately modeling the surface circulation in the coastal ocean. In the present work, high-frequency (HF) radar surface currents are assimilated using an ensemble scheme ... [more ▼]

Surface winds are crucial for accurately modeling the surface circulation in the coastal ocean. In the present work, high-frequency (HF) radar surface currents are assimilated using an ensemble scheme which aims to obtain improved surface winds taking into account ECMWF (European Centre for Medium-Range Weather Forecasts) winds as a first guess and surface current measurements. The objective of this study is to show that wind forcing can be improved using an approach similar to parameter estimation in ensemble data assimilation. Like variational assimilation schemes, the method provides an improved wind field based on surface current measurements. However, the technique does not require an adjoint and it is thus easier to implement. In addition, it does not rely on a linearization of the model dynamics. The method is validated directly by comparing the analyzed wind speed to independent in situ measurements and indirectly by assessing the impact of the corrected winds on model sea surface temperature (SST) relative to satellite SST. [less ▲]

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See detailComparison between satellite and in situ sea surface temperature data in the Western Mediterranean Sea
Alvera Azcarate, Aïda ULg; Troupin, Charles ULg; Barth, Alexander ULg 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 detailEnhanced ocean temperature forecast skills through 3-D super-ensemble multi-model fusion
Lenartz, Fabian ULg; Mourre, B.; Barth, Alexander ULg et al

in Geophysical Research Letters (2010), 37(L19606),

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See detailA web interface for griding arbitrarily distributed in situ data based on Data-Interpolating Variational Analysis (DIVA)
Barth, Alexander ULg; Alvera Azcarate, Aïda ULg; Troupin, Charles ULg 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 detailCariaco basin dynamics: Study of the thermocline depth variability and its relation with open ocean conditions
Alvera Azcarate, Aïda ULg; Barth, Alexander ULg; Weisberg, Robert H. et al

Conference (2010, August 11)

The Cariaco basin (Venezuela) is a semi-enclosed trench located along the coast of Venezuela, with maximum depths of about 1400 m. It is connected to the open ocean by two shallow passages of less than ... [more ▼]

The Cariaco basin (Venezuela) is a semi-enclosed trench located along the coast of Venezuela, with maximum depths of about 1400 m. It is connected to the open ocean by two shallow passages of less than 150 m depth. Limited basin ventilation, coupled with a small vertical mixing results in anoxic conditions from about 250 m to the bottom. The dynamics of the Cariaco Basin are studied by means of a three-dimensional hydrodynamic model. The numerical model has a resolution of 1/60 degree and is an implementation of the Regional Ocean Modeling System (ROMS) nested in the global HYCOM solution from the Naval Research Laboratory. Of particular interest are the mechanisms that link the basin's interior to the Caribbean Sea, which can lead to the ventilation of the basin's anoxic sub-surface waters. To assess the influence of the open ocean on the basin, the spatial and temporal evolution of the thermocline depth and width is studied, as well as its relationship with wind variability and chlorophyll-a concentration: at seasonal scales, the interior of the basin reacts to easterly winds intensification with a rising of the thermocline, resulting in a coastal upwelling response, with the consequent increase in chlorophyll-a concentration. Outside the Cariaco basin, where an open-ocean, oligotrophic regime predominates, wind intensification increases mixing of the surface layers and induces therefore a deepening of the thermocline. At shorter time scales (i.e. days), it is shown that other processes, such as the influence of the meandering Caribbean Current, can also influence the thermocline variability within the Cariaco basin. [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 detailReconstruction of missing data in satellite and in situ data sets with DINEOF (Data Interpolating Empirical Orthogonal Functions)
Alvera Azcarate, Aïda ULg; Barth, Alexander ULg; Beckers, Jean-Marie ULg

Conference (2010, July 12)

DINEOF (Data Interpolating Empirical Orthogonal Functions), a method to reconstruct missing data in geophysical data sets, is presented. Based on a truncated Empirical Orthogonal Functions (EOF) basis ... [more ▼]

DINEOF (Data Interpolating Empirical Orthogonal Functions), a method to reconstruct missing data in geophysical data sets, is presented. Based on a truncated Empirical Orthogonal Functions (EOF) basis, DINEOF uses an iterative procedure to calculate the values at the missing locations. A clear advantage of DINEOF is that no aprioriate knowledge about the statistics of the data set being reconstructed is needed (such as covariance or correlation length): the EOF basis is used internally to infer necessary information about the data, so no estimation of those parameters is needed. This characteristic is specially interesting for heterogeneous data distributions for which is difficult to derive this information. Also obtained are estimations of the error covariance of the reconstructed field, and outliers, i.e. data that present anomalous values with respect to the surrounding information in the original data, for which the residuals are larger than the statistically expected misfit calculated during the analysis. When very few data is available, the estimated covariance between two successive images used in the EOF calculation might not sufficiently robust. As a consequence, spikes appear in the temporal EOFs, which result in unrealistic discontinuities in the reconstruction. A temporal filter has been applied to the covariance matrix used to determined the EOFs, which effectively enhance temporal continuity. This has been applied to a SST data set of the Black Sea and the reconstruction error is estimated by cross-validation. On-going work includes the development of a merging capability within DINEOF that will allow to blend data from different platforms (satellite and in situ data). [less ▲]

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See detailComparison between in situ and satellite surface temperature in the Western Mediterranean Sea
Alvera Azcarate, Aïda ULg; Barth, Alexander ULg; Troupin, Charles ULg 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 detailMultiparametric observations and analysis in the Bay of Calvi (Corsica), an ideal site for studying the human activity effects and climate changes in the Mediterranean Sea; STARESO
Gobert, Sylvie ULg; Alvera Azcarate, Aïda ULg; Barth, Alexander ULg et al

Conference (2010, May)

STARESO (Station de REcherche Sous marine et Océanographique) is the marine and oceanographic research station of the University of Liège (Belgium) managed by the French company STARESO S.A.. Constructed ... [more ▼]

STARESO (Station de REcherche Sous marine et Océanographique) is the marine and oceanographic research station of the University of Liège (Belgium) managed by the French company STARESO S.A.. Constructed in 1969, it is located near Calvi (Corsica, Western Mediterranean Sea) in an oligotrophic area chosen for the exceptional quality of its coastal waters STARESO offers to the oceanographers, by diving or with boats, a direct access to the sea. The variety of the accessible ecosystems is remarkable and unique in the Mediterranean basin: -the Bay of Calvi is characterized by healthy and very diverse biocenosis (e.g. Posidonia meadows, rocky and sandy communities, -a steep submarine canyon, with depths greater than 1 000 meters, is accessible in 15 minutes of navigation; -the Liguro-Provençal front, a major hydrologic structure, is situated between 10 and 15 miles of the coast. STARESO is accessible all the year for everybody and is functioning like an oceanographic research vessel. The Station is a platform for all oceanographic disciplines with a scientific expertise widely based on a long tradition of interdisciplinary work, and a direct access to time series of physical, chemical and biological data registered with automated systems and variety of sensors deployed in the Bay of Calvi since 30 years. This platform provides the opportunity to reach coastal, pelagic, benthic, deep systems with a manageable cost and ship requirements in a pristine zone. [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 ULg; Barth, Alexander ULg; Troupin, Charles ULg 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 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 detailSeaDataNet regional climatologies: an overview
Troupin, Charles ULg; Ouberdous, Mohamed ULg; Barth, Alexander ULg et al

Poster (2010, March 29)

Detailed reference viewed: 41 (2 ULg)
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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