Filling in missing sea-surface temperature satellite data over the Eastern Mediterranean Sea using the DINEOF algorithm

Abstract

The Data Interpolating Empirical Orthogonal Functions method is a special technique based on EmpiricalOrthogonal Functions and developed to reconstruct missing data from satellite images, which is especially useful for filling in missing data from geophysical fields. Successful experiments in the Western Mediterranean encouraged extension of the application eastwards using a similar experimental implementation. The present study summarizes the experimental work done, the implementation of the method and its ability to reconstruct the sea-surface temperature fields over the Eastern Mediterranean basin, and specifically in the Levantine Sea. L3 type Satellite Sea-surface Temperature data has been used and reprocessed in order to recover missing information from cloudy images. Data reconstruction with this method proved to be extremely effective, even when using a relatively small number of time steps, and markedly accelerated the procedure. A detailed comparison with the two oceanographic models proves the accuracy of the method and the validity of the reconstructed fields.

A method to generate fully multi-scale optimal interpolation by combining efficient single process analyses, illustrated by a DINEOF analysis spiced with a local optimal interpolation

“…Another approach for analyzing a set of satellite images (data interpolating empirical orthogonal functions -DINEOF) uses the data to create a truncated empirical orthogonal function (EOF) representation of the data set to fill in missing data (e.g., Beckers and Rixen, 2003;AlveraAzcárate et al, 2005AlveraAzcárate et al, , 2007. The latter method has been favorably compared to OI and has been exploited in a series of applications (e.g., Sheng et al, 2009;Ganzedo et al, 2011;Nikolaidis et al, 2014;Wang and Liu, 2014), including operational setups (e.g., Volpe et al, 2012). In some situations, however, the truncation of the EOFs series can reject some interesting small-scale features that only give a small contribution to the total variance, and that can often be split into several modes (Sirjacobs et al, 2008).…”

Implementation of Pattern Recognition Algorithms in Processing Incomplete Wind Speed Data for Energy Assessment of Offshore Wind Turbines

“…In [24], the authors deal with wave data and the data filling takes place in three-hour intervals. In [25], the method is used for recovering images from a satellite; however, no evaluation metrics are used for the method's performance.…”

A Multivariate Empirical Orthogonal Function Method to Construct Nitrate Maps in the Southern Ocean

“…The Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) program recently reported that 31 profiling floats carrying nitrate sensors have successfully transmitted 40 complete nitrate annual cycles (Johnson et al 2017), and additional float deployments are being carried out. Multiple methods and techniques have been developed to reconstruct fields in regions with sparse observational data, such as optimal interpolation (Reynolds and Smith 1994;Schneider 2001), model-based gapfilling techniques (e.g., data assimilation; Stammer et al 2002;Wunsch and Heimbach 2007;Mazloff et al 2010;Verdy and Mazloff 2017), and empirical orthogonal function (EOF)-based methods (e.g., Smith et al 1996;Kaplan et al 1997;Beckers and Rixen 2003;Alvera-Azcárate et al 2005;Kondrashov and Ghil 2006;Alvera-Azcárate et al 2007;Alvera-Azcárate et al 2011;Nikolaidis et al 2014;Alvera-Azcárate et al 2016). The EOF-based methods, in particular, show advantages over the other methods in terms of ease of implementation and accuracy relative to computational costs (Alvera-Azcárate et al 2005).…”