|Analysis of high frequency geostationary ocean colour data using DINEOF|Alvera-Azcárate, A.; Vanhellemont, Q.; Ruddick, K.; Barth, A.; Beckers, J.-M. (2015). Analysis of high frequency geostationary ocean colour data using DINEOF. Est., Coast. and Shelf Sci. 159: 28-36. dx.doi.org/10.1016/j.ecss.2015.03.026
In: Estuarine, Coastal and Shelf Science. Academic Press: London; New York. ISSN 0272-7714, more
geostationary satellite data; turbidity; reconstruction of missing data;outlier detection; DINEOF; southern North Sea
|Authors|| || Top |
- Alvera-Azcárate, A., more
- Vanhellemont, Q., more
- Ruddick, K., more
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 (15 min) 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.