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See detailHiSea: High resolution merged satellite sea surface temperature fields
Alvera Azcarate, Aïda ULg; Barth, Alexander ULg; Toussaint, Marie-Eve ULg et al

Conference (2011, May 25)

Several satellites measure Sea Surface Temperature (SST), each of these with different technical specificities and error sources. Together with in situ data, they form a highly complementary data set. The ... [more ▼]

Several satellites measure Sea Surface Temperature (SST), each of these with different technical specificities and error sources. Together with in situ data, they form a highly complementary data set. The creation of merged SST products, integrating the strengths of each of its components and minimising their weaknesses, is however not an easy task, but it is certainly a desirable goal that has generated a large amount of research over the last years. The main objectives of this project are, among others: 1.To develop a technology, based on DINEOF (Data Interpolating Empirical Orthogonal Functions), that allows to merge different data sets at very different sampling intervals (in space and time) and create an integrated product at the highest sampling frequency and with the highest quality possible. 2.To provide improved, merged analyses of variables such as SST and chlorophyll. 3.Obtain a better understanding of the diurnal cycle of the studied variables. 4.To better understand the relation between variables (and take advantage of these relationship to improve the analyses). 5.Using the above-mentioned developments, explore the capability of the developed technology to produce SST forecasts based on multi-variate EOFs and model forecasts. We will present the first preliminary results for merging different SST data sets, as well as our plans for future developments and applications. Website of the project: http://www.gher.ulg.ac.be/HiSea/ [less ▲]

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See detailMerging satellite and in situ sea surface temperature data using DINEOF
Alvera Azcarate, Aïda ULg; Barth, Alexander ULg; Beckers, Jean-Marie ULg

Conference (2011, April 05)

High quality sea surface temperature data sets are needed for various applications, including numerical weather prediction, ocean forecasting and climate research. The coverage, resolution and precision ... [more ▼]

High quality sea surface temperature data sets are needed for various applications, including numerical weather prediction, ocean forecasting and climate research. The coverage, resolution and precision of individual sea surface temperature observations is not sufficient for these applications, therefore merging of complementary data sets is needed to increase the coverage and to reduce the final data set error. DINEOF (Data Interpolating Empirical Orthogonal Functions) is an EOF-based technique to reconstruct missing information -due to clouds, for example in satellite data sets. A new development of this method consists in its capability to merge different data sources into one estimate. This development is tested using AVHRR data of the western Mediterranean Sea and in situ data from various international databases (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)). An error assessment between the satellite and in situ data is performed first, in order to determine the error statistics between these two data sources. The error is calculated by database, platform type (CTD, XBT, drifters, bottles and ships) and depth. This error assessment is used to merge the in situ and satellite data. The impact of the sensor-specific errors on the quality of the final product will be assessed, and compared to the results obtained when the same error estimate is used for all sensors. The benefit of using in situ data in addition to satellite data will be also discussed. Additional information can be found at http://modb.oce.ulg.ac.be/mediawiki/index.php/DINEOF and http://gherdiva.phys.ulg.ac.be/DINEOF/ [less ▲]

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See detailEOF analysis of Sea Surface Temperature in the Canary Island - Madeira region
Troupin, Charles ULg; Alvera Azcarate, Aïda ULg; Barth, Alexander ULg 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 detailSatellite and in situ sea surface temperature comparison and merging in the Mediterranean Sea.
Alvera Azcarate, Aïda ULg; Barth, Alexander ULg; Beckers, Jean-Marie ULg

Conference (2011, April 03)

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

A comparison between in situ and satellite sea surface temperature (SST) for the western Mediterranean Sea is presented. 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 average difference between these type of data and AVHRR (Advanced Very High Resolution Radiometer) SST satellite data. Attention is given also to the relative accuracy of each database, and advantages and shortcomings on the use of each database will be discussed. The error assessment will be used to merge in situ and satellite SST data using DINEOF (Data Interpolating Empirical Orthogonal Functions), an EOF-based technique. The impact of the sensor-specific errors on the quality of the final product will be assessed, and compared to the results obtained when a more general error estimate is used. [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 detailAssimilation of high-frequency radar currents in the Ligurian Sea
Barth, Alexander ULg; Chiggiato, Jacopo; Alvera Azcarate, Aïda ULg et al

Conference (2011, April)

The circulation in the Ligurian Sea is dominated by strong currents, namely the Western Corsican Current and the East Corsican Current, which jointly form the Northern Current. A high mesoscale activity ... [more ▼]

The circulation in the Ligurian Sea is dominated by strong currents, namely the Western Corsican Current and the East Corsican Current, which jointly form the Northern Current. A high mesoscale activity, including meanders and eddy formation, is associated to those energetic currents. The non-linear instability processes and apparently chaotic behavior of this current system make this region a challenging testbed for data assimilation. High-frequency radar surface currents have been measured by the NATO Undersea Research Centre (NURC), La Spezia, Italy from two sites at the Italian Coast (Isola Palmaria and San Rossore). Each of those sites measures the radial currents relative to the position of the radar system. This WERA system captures well the general circulation and mesoscale flow features. The present study shows an application of the assimilation of those measurements in a nested model con- figuration of the Ligurian Sea. It is assumed that the error in the model surface currents comes primarily from uncertainties in the lateral boundary conditions and surface wind fields. The objective of this study is to reduce the uncertainty in these forcing fields by data assimilation. An ensemble of 100 perturbed lateral boundary conditions and surface wind fields is created to take the uncertainty into account. Using an ensemble-smoother technique described in Barth et al, 2010 (Ocean Science) and Barth et al, 2010 (Ocean Dynamics, in press), improved estimates of the wind forcing and boundary conditions are obtained. By rerunning the model with the updated forcing fields, it is verified that the analyzed model solution is closer to the observed HF radar currents. This technique is similar to 4D-Var, but since it is based on the ensemble covariance between forcing fields and observations, it does not require the formulation of an adjoint. [less ▲]

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See detailSeaDataNet regional climatologies: an overview
Troupin, Charles ULg; Ouberdous, Mohamed ULg; Barth, Alexander ULg 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 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 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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