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See detailEstimating Inter-Sensor Sea Surface Temperature Biases using DINEOF analysis
Tomazic, Igor ULg; Alvera Azcarate, Aïda ULg; Troupin, Charles ULg 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 detailDINEOF-based bias correction of SEVIRI sea surface temperature using Metop-A/AVHRR and ENVISAT/AATSR SST
Tomazic, Igor ULg; Alvera Azcarate, Aïda ULg; Barth, Alexander ULg 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 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 detailAn EOF-based technique to compute merged high resolution sea surface temperature fields
Alvera Azcarate, Aïda ULg; Troupin, Charles ULg; Barth, Alexander ULg et al

Conference (2012, May 10)

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

High quality sea surface temperature (SST) data sets are needed for various applications, including numerical weather prediction, ocean forecasting and climate research. The coverage, resolution and precision of individual SST satellite observations is not sufficient for these applications, therefore the merging of these complementary data sets is needed to reduce the final data set error. This is usually performed by optimal interpolation (OI).We present an extension of the capabilities of DINEOF (Data INterpolating Empirical Orthogonal Functions) to merge data from different platforms. The analysis is based on the formalism of OI, but the crucial difference is that the error covariance is not parametrized a priori using an analytical expression, but expressed using a spatial EOF basis calculated by DINEOF. This EOF basis represents more realistically the complex variability of SST data sets than the parametric covariance used in most OI applications. An example will be presented using data from a polar-orbiting satellite (AVHRR on MetOp) and a geostationary satellite (SEVIRI on MSG). The high spatial resolution of the polar-orbiting satellite and the high temporal resolution of the geostationary satellite are retained to create a very high spatial and temporal resolution field of the western Mediterranean SST. The results are validated with independent data. [less ▲]

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See detailReconstruction of Total Suspended Matter data over the North Sea using DINEOF: use of the Gaussian anamorphosis transformation
Alvera Azcarate, Aïda ULg; Neukermans, Griet; Barth, Alexander ULg et al

Conference (2012, May 10)

Total Suspended Matter (TSM) from the SEVIRI sensor in the North Sea will be analysed using DINEOF (Data INterpolating Empirical Orthogonal Functions), an EOFbased technique to reconstruct missing data ... [more ▼]

Total Suspended Matter (TSM) from the SEVIRI sensor in the North Sea will be analysed using DINEOF (Data INterpolating Empirical Orthogonal Functions), an EOFbased technique to reconstruct missing data. The information needed to reconstruct the missing data is computed internally based on a truncated EOF basis, so no assumptions about the statistics of the data have to be made. DINEOF uses the mean and covariance of the original data to calculate the EOF basis. If the data are normally distributed, then the probability density distribution can be completely described by their mean and the eigenvectors of the covariance matrix (the EOFs). Variables such as TSM, however, do not have a Gaussian distribution, since TSM is never smaller than zero. DINEOF typically does not take this into account. To overcome this, a logarithmic transformation is usually performed to non-Gaussian variables, although the exponential transformation needed to retrieve the original variable units after using DINEOF leads sometimes to unrealistic high values in the reconstruction. An empirical transformation, which allows to obtain a normally distributed variable based solely on the data themselves, will be applied. This procedure, called Gaussian anamorphosis, is sometimes used in data assimilation. A Gaussian anamorphosis transformation will be applied to the TSM data of the North Sea prior to their reconstruction. The high spatial and temporal dynamics of the gapfree geostationary TSM data set will be analysed, focusing on tidal dynamics and sub-daily variability. [less ▲]

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See detailInterannual variability of Black Sea’s hydrodynamics and connection to atmospheric patterns
Capet, Arthur ULg; Barth, Alexander ULg; Beckers, Jean-Marie ULg et al

Conference (2012, May 09)

The long term variability (1962–2000) of the Black Sea physical processes (e.g. temperature, main circulation, cold intermediate layer, sea level) and its relation to atmospheric conditions and large ... [more ▼]

The long term variability (1962–2000) of the Black Sea physical processes (e.g. temperature, main circulation, cold intermediate layer, sea level) and its relation to atmospheric conditions and large scale climate patterns are investigated using an eddy-resolving tridimensional model in combination with statistical tools (e.g. Empirical Orthogonal Functions, Self Organizing Maps). First, the ability of the model to represent the interannual dynamics of the system is assessed by comparing the modeled and satellite sea surface temperature (SST) and sea level anomaly (SLA) decomposed into their dominant Empirical Orthogonal Functions (EOFs). The correlation between the spatial and temporal EOFs modes derived from model and satellite data is usually satisfactory and this gives some confidence in using the model as a tool to investigate not only the SST and SLA dynamics but also the dynamics of connected variables. Then, the long term variability (1962–2000) of the Black Sea hydrodynamics is assessed by decomposing into their dominant EOFs modeled SST, SLA and selected key hydrodynamical variables associated to the main circulation and vertical structure of the water column. Significant correlations between the EOFs associated to these variables are investigated in order to link the variability of surface fields and the internal dynamics of the system. In particular, the intensity of the general cyclonic circulation (the Rim Current) is shown to impact strongly (1) the mean sea level, (2) the SST response to air temperature (AT), (3) the formation of the cold intermediate layer, (4) the meridional repartition of the SST anomaly and (5) the exchanges of heat between the north-western shelf and the open basin. In order to appraise the variability of atmospheric conditions over the Black Sea during 1962–2000 and their role in driving the hydrodynamics, a self-organizing maps technique is used to identify spatial recurrent patterns of atmospheric fields (i.e., AT, wind stress and curl). The impact on these patterns of large scale climatic variability over the north Atlantic and Eurasia (estimated by respectively the north Atlantic oscillation (NAO) and the east Atlantic/west Russia oscillation (EA/WR) indexes) is assessed. Distinct time scales of influence of the large scale teleconnection patterns on the AT are identified: EA/WR drives the short scale (1–5 years) variations of SST, while the long term (4-5 years) trends of the NAO drive the long term SST trends. The drastic changes that have occurred in the Black Sea deep sea ecosystem at the end of the 80s are connected to an intensification of the general circulation that has promoted an export of riverine materials from the eutrophicated north-western shelf to the deep sea. Finally, in the last two decades, we find an increased duration of persistent atmospheric anomalies regime that has the potential to drive the system away from its average state as occurred in the late 80s. If persistent in the future, such long lasting atmospheric anomalies may have a significant impact on the ecosystem functioning. [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 ULg; Alvera Azcarate, Aïda ULg; Barth, Alexander ULg 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 ▲]

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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 detailInterannual variability of Black Sea’s hydrodynamics and connection to atmospheric patterns
Capet, Arthur ULg; Barth, Alexander ULg; Beckers, Jean-Marie ULg et al

in Deep-Sea Research Part II, Topical Studies in Oceanography (2012)

The long term variability (1962–2000) of the Black Sea physical processes (e.g. temperature, main circulation, cold intermediate layer, sea level) and its relation to atmospheric conditions and large ... [more ▼]

The long term variability (1962–2000) of the Black Sea physical processes (e.g. temperature, main circulation, cold intermediate layer, sea level) and its relation to atmospheric conditions and large scale climate patterns are investigated using an eddy-resolving tridimensional model in ombination with statistical tools (e.g. Empirical Orthogonal Functions, Self Organizing Maps). First, the ability of the model to represent the interannual dynamics of the system is assessed by comparing the modeled and satellite sea surface temperature (SST) and sea level anomaly (SLA) decomposed into their dominant Empirical Orthogonal Functions (EOFs). The correlation between the spatial and temporal EOFs modes derived from model and satellite data is usually satisfactory and this gives some confidence in using the model as a tool to investigate not only the SST and SLA dynamics but also the dynamics of connected variables. Then, the long term variability (1962–2000) of the Black Sea hydrodynamics is assessed by decomposing into their dominant EOFs modeled SST, SLA and selected key hydrodynamical variables associated to the main circulation and vertical structure of the water column. Significant correlations between the EOFs associated to these variables are investigated in order to link the variability of surface fields and the internal dynamics of the system. In particular, the intensity of the general cyclonic circulation (the Rim Current) is shown to impact strongly (1) the mean sea level, (2) the SST response to air temperature (AT), (3) the formation of the cold intermediate layer, (4) the meridional repartition of the SST anomaly and (5) the exchanges of heat between the north-western shelf and the open basin. In order to appraise the variability of atmospheric conditions over the Black Sea during 1962–2000 and their role in driving the hydrodynamics, a self-organizing maps technique is used to identify spatial recurrent patterns of atmospheric fields (i.e., AT, wind stress and curl). The impact on these patterns of large scale climatic variability over the north Atlantic, Eurasia and the Pacific Ocean (estimated by respectively the north Atlantic oscillation (NAO), the east Atlantic/west ̃Russia oscillation (EA/WR) and the El Nino southern oscillation (ENSO) indexes) is assessed. Distinct time scales of influence of the large scale teleconnection patterns on the AT are identified: EA/WR drives the short scale (1–5 years) variations of SST, while the long term (4-5 years) trends of the NAO drive the long term SST trends. The drastic changes that have occurred in the Black Sea deep sea ecosystem at the end of the 80s are connected to an intensification of the general circulation that has promoted an export of riverine materials from the eutrophicated north-western shelf to the deep sea. Finally, in the last two decades, we find an increased duration of persistent atmospheric anomalies regime that has the potential to drive the system away from its average state as occurred in the late 80s. If persistent in the future, such long lasting atmospheric anomalies may have a significant impact on the ecosystem functioning. [less ▲]

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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 detailScience based management of coastal waters
Delhez, Eric ULg; Barth, Alexander ULg

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

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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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