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See detailDINEOF-based bias correction of SEVIRI sea surface temperature using Metop-A/AVHRR and ENVISAT/AATSR SST
Tomazic, Igor ULiege; Alvera Azcarate, Aïda ULiege; Barth, Alexander ULiege et al

Poster (2013)

Satellite-derived sea surface temperature (SST) show inter-sensor and inter-regional differences (biases) due to their technical characteristics, multispectral algorithm limitations and the changing ... [more ▼]

Satellite-derived sea surface temperature (SST) show inter-sensor and inter-regional differences (biases) due to their technical characteristics, multispectral algorithm limitations and the changing physical properties of the measured environments. The bias correction is usually calculated as a difference between the SST measurements from two sensors where one is defined as the reference (e.g. ENVISAT/AATSR). These empirical bias fields show gaps due to the satellite characteristics (e.g. narrow swath in case of AATSR) and to the presence of clouds or other atmospheric contamination sources. We present a bias correction approach based on DINEOF (Data Interpolating Empirical Orthogonal Functions) for reconstructing missing data. Two different approaches for deriving SST bias fields were used: analysing SST biases based on reconstructed SST differences or based on differences of the reconstructed SST fields. The method is applied at a large scale (European seas) and at a regional scale (e.g. Mediterranean Sea) to correct SEVIRI and Metop-A/AVHRR SST measurements using ENVISAT/AATSR as a corrector. For SEVIRI we additionally used Metop-A/AVHRR SST as a corrector to analyse the impact of ENVISAT/AATSR failure. Corrected SST fields based on both approaches were validated against independent in situ buoy SST data or with ENVISAT/AATSR SST data for areas without in situ data (e.g. eastern Mediterranean). The method is also compared to the operational bias correction method at Meteo-France/CMS that uses a temporal and spatial averaging. Results show that both approaches lead to near-zero biases when compared to AATSR SST measurements, although the differences of reconstructions exhibit much higher standard deviation (> 0.6 K) compared to the reconstruction of differences (< 0.5 K). Comparison with in situ data expectedly depends on the initial comparison between AATSR and in situ SST for specific regions. [less ▲]

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See detailEstimating Inter-Sensor Sea Surface Temperature Biases using DINEOF analysis
Tomazic, Igor ULiege; Alvera Azcarate, Aïda ULiege; Troupin, Charles ULiege et al

Poster (2013)

Climate studies need long-term data sets of homogeneous quality, in order to discern trends from other physical signals present in the data and to minimise the contamination of these trends by errors in ... [more ▼]

Climate studies need long-term data sets of homogeneous quality, in order to discern trends from other physical signals present in the data and to minimise the contamination of these trends by errors in the source data. Sea surface temperature (SST), defined as one of essential climatology variables, has been increasingly used in both oceanographical and meteorological operational context where there is a constant need for more accurate measurements. Satellite-derived SST provides an indispensable dataset, with both spatially and temporally high resolutions. However, these data have errors of 0.5 K on a global scale and present inter-sensor and inter-regional differences due to their technical characteristics, algorithm limitations and the changing physical properties of the measured environments. These inter-sensor differences should be taken into account in any research involving more than one sensor (SST analysis, long term climate research . . . ). The error correction for each SST sensor is usually calculated as a difference between the SST data derived from referent sensor (e.g. ENVISAT/AATSR) and from the other sensors (SEVIRI, AVHRR, MODIS). However, these empirical difference (bias) fields show gaps due to the satellite characteristics (e.g. narrow swath in case of AATSR) and to the presence of clouds or other atmospheric contaminations. We present a methodology based on DINEOF (Data INterpolation Empirical Orthogonal Functions) to reconstruct and analyse SST biases with the aim of studying temporal and spatial variability of the SST bias fields both at a large scale (European seas) and at a regional scale (Mediterranean Sea) and to perform the necessary corrections to the original SST fields. Two different approaches were taken: by analysing SST biases based on reconstructed SST differences and based on differences of reconstructed SST fields. Corrected SST fields based on both approaches were validated against independent in situ buoy SST data or with ENVISAT/AATSR SST data for areas without in situ data (e.g. eastern Mediterranean). [less ▲]

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See detailReconstruction of the long-term satellite-derived sea surface temperature in the South China Sea
Huynh, Thi Hong Ngu ULiege; Alvera Azcarate, Aïda ULiege; Barth, Alexander ULiege et al

Poster (2012, February 24)

The AVHRR (Advanced Very High Resolution Radiometer) sea surface temperature is very useful for researches in oceanography because of its high resolution. An AVHRR limitation is the high missing data ... [more ▼]

The AVHRR (Advanced Very High Resolution Radiometer) sea surface temperature is very useful for researches in oceanography because of its high resolution. An AVHRR limitation is the high missing data percentage due to cloud coverage. In the South China Sea, the average missing data is usually more than 80%, especially more than 95% in the region near the Borneo Island. In this study, we use DINEOF tool to reconstruct a daily night-time AVHRR data set with horizontal resolution of 4km spanning from 1989 to 2009. Besides, a comparison between the results and in situ data is shown. The EOF analysis shows that the first three modes explain about 95% of seasonal variability. [less ▲]

Detailed reference viewed: 59 (7 ULiège)
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See detailViewing through the clouds in satellite images
Troupin, Charles ULiege; Barth, Alexander ULiege; Alvera Azcarate, Aïda ULiege et al

Poster (2012, February 24)

Detailed reference viewed: 20 (6 ULiège)
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See detailScience based management of coastal waters
Delhez, Eric ULiege; Barth, Alexander ULiege

in Journal of Marine Systems (2011, October), 88(1),

Detailed reference viewed: 142 (35 ULiège)
See detailHiSea: High resolution merged satellite sea surface temperature fields
Alvera Azcarate, Aïda ULiege; Barth, Alexander ULiege; Toussaint, Marie-Eve ULiege 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 ▲]

Detailed reference viewed: 46 (7 ULiège)
See detailEOF analysis of Sea Surface Temperature in the Canary Island - Madeira region
Troupin, Charles ULiege; Alvera Azcarate, Aïda ULiege; Barth, Alexander ULiege et al

Conference (2011, April 05)

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

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

Detailed reference viewed: 63 (6 ULiège)
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See detailSeaDataNet regional climatologies: an overview
Troupin, Charles ULiege; Ouberdous, Mohamed ULiege; Barth, Alexander ULiege et al

Poster (2011, April)

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

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

Detailed reference viewed: 30 (2 ULiège)
See detailAssimilation of high-frequency radar currents in the Ligurian Sea
Barth, Alexander ULiege; Chiggiato, Jacopo; Alvera Azcarate, Aïda ULiege 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 ▲]

Detailed reference viewed: 55 (5 ULiège)
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See detailAdvanced Data Interpolating Variational Analysis. Application to climatological data
Troupin, Charles ULiege; Sirjacobs, Damien ULiege; Rixen, Michel et al

Poster (2011, April)

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

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

Detailed reference viewed: 106 (17 ULiège)
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See detailAdvanced Data Interpolating Variational Analysis. Application to climatological data.
Troupin, Charles ULiege; Sirjacobs, Damien ULiege; Rixen, Michel et al

Poster (2011, March 21)

Detailed reference viewed: 31 (5 ULiège)
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See detailHigh-resolution measurements and modelling of the Cape Ghir upwelling filament during the CAIBEX cruise
Troupin, Charles ULiege; Beckers, Jean-Marie ULiege; Sangrà, Pablo et al

Conference (2010, April 26)

Detailed reference viewed: 35 (1 ULiège)
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See detailSynthesis of regional product activities JRA4-JRA9
Beckers, Jean-Marie ULiege; Alvera Azcarate, Aïda ULiege; Barth, Alexander ULiege et al

Conference (2010, April 01)

Detailed reference viewed: 21 (3 ULiège)
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See detailSeaDataNet regional climatologies: an overview
Troupin, Charles ULiege; Ouberdous, Mohamed ULiege; Barth, Alexander ULiege et al

Poster (2010, March 29)

Detailed reference viewed: 49 (2 ULiège)
See detailA web interface for gridding and visualizing oceanographic data sets
Barth, Alexander ULiege; Alvera Azcarate, Aïda ULiege; Sirjacobs, Damien ULiege et al

Conference (2010, March)

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

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

Detailed reference viewed: 86 (3 ULiège)