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Alvera-Azcarate A.,University of Liege | Vanhellemont Q.,Royal Belgian Institute Of Natural Sciences | Ruddick K.,Royal Belgian Institute Of Natural Sciences | Barth A.,University of Liege | And 2 more authors.
Estuarine, Coastal and Shelf Science | Year: 2015

DINEOF (Data Interpolating Empirical Orthogonal Functions), a technique to reconstruct missing data, is applied to turbidity data obtained through the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat Second Generation 2. The aim of this work is to assess if the tidal variability of the southern North Sea in 2008 can be accurately reproduced in the reconstructed dataset. Such high frequency data have not previously been analysed with DINEOF and present new challenges, like a strong tidal signal and long night-time gaps. An outlier detection approach that exploits the high temporal resolution (15min) of the SEVIRI dataset is developed. After removal of outliers, the turbidity dataset is reconstructed with DINEOF. In situ Smartbuoy data are used to assess the accuracy of the reconstruction. Then, a series of tidal cycles are examined at various positions over the southern North Sea. These examples demonstrate the capability of DINEOF to reproduce tidal variability in the reconstructed dataset, and show the high temporal and spatial variability of turbidity in the southern North Sea. An analysis of the main harmonic constituents (annual cycle, daily cycle, M2 and S2 tidal components) is performed, to assess the contribution of each of these modes to the total variability of turbidity. The variability not explained by the harmonic fit, due to the natural processes and satellite processing errors as noise, is also assessed. © 2015 Elsevier Ltd. Source


Alvera-Azcarate A.,University of Liege | Barth A.,University of Liege | Barth A.,Frs Fnrs National Fund For Scientific Research | Parard G.,University of Liege | Beckers J.-M.,University of Liege
Remote Sensing of Environment | Year: 2016

An analysis of daily Sea Surface Salinity (SSS) at 0.15 °. ×. 0.15° spatial resolution from the Soil Moisture and Ocean Salinity (SMOS) satellite mission using DINEOF (Data Interpolating Empirical Orthogonal Functions) is presented. DINEOF allows reconstructing missing data using a truncated EOF basis, while reducing the amount of noise and errors in geophysical datasets. This work represents a first application of DINEOF to SMOS SSS. Results show that a reduction of the error and the amount of noise is obtained in the DINEOF SSS data compared to the initial SMOS SSS data. Errors associated to the edge of the swath are detected in 2 EOFs and effectively removed from the final data, avoiding removing the data at the edges of the swath in the initial dataset. The final dataset presents a centered root mean square error of 0.2 in open waters when comparing with thermosalinograph data at their original spatial and temporal resolution. Constant biases present near land masses, large scale biases and latitudinal biases cannot be corrected with DINEOF because persistent signals are retained in high order EOFs, and therefore these need to be corrected separately. The signature of the Douro and Gironde rivers is detected in the DINEOF SSS. The minimum SSS observed in the Gironde plume corresponds to a flood event in June 2013, and the shape and size of the Douro river shows a good agreement with chlorophyll-a satellite data. These examples show the capacity of DINEOF to remove noise and provide a full SSS dataset at a high temporal and spatial resolution with reduced error, and the possibility to retrieve physical signals in zones with high initial errors. © 2016 Elsevier Inc. Source


Alvera-Azcarate A.,University of Liege | Alvera-Azcarate A.,Frs Fnrs National Fund For Scientific Research | Troupin C.,University of Liege | Barth A.,University of Liege | And 2 more authors.
Ocean Dynamics | Year: 2011

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, MEDAR/Medatlas, Coriolis Data Center, International Council for the Exploration of the Sea and International Comprehensive Ocean-Atmosphere Data Set). The in situ data are classified into different platforms or sensors (conductivity-temperature-depth, expendable bathythermographs, drifters, bottles, and ships), in order to assess the relative accuracy of these type of data with respect to 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. © 2011 Springer-Verlag. Source

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