The Mediterranean Ocean Colour Observing System – system development and product validation

Abstract

Abstract. This paper presents the Mediterranean Ocean Colour Observing System in the framework of the growing demand of near real-time data emerging within the operational oceanography international context. The main issues related to the satellite operational oceanography are tied to the following: (1) the near real-time ability to track data flow uncertainty sources; (2) in case of failure, to provide backup solutions to end-users; and (3) to scientifically assess the product quality. We describe the major scientific and technological steps made to develop, maintain and improve the operational system and its products. A method for assessing the near real-time product quality is developed and its limitation discussed. Main results are concerned with the degradation, starting from mid-2010, of the MODIS Aqua channel at 443 nm with its successive recovery thanks to the new calibration scheme implemented in the recently released SeaDAS version 6.4. The product validation analysis highlights that SeaWiFS chlorophyll product over the Mediterranean Sea is the best performing in comparison with those of MODIS and MERIS. Despite their general good agreement with in situ observations, MODIS-and MERIS-derived chlorophyll present a slight and systematic underestimation of the in situ counter part. The most relevant implications induced by these results are discussed from an operational point of view.

Regional Empirical Algorithms for an Improved Identification of Phytoplankton Functional Types and Size Classes in the Mediterranean Sea Using Satellite Data

“…For more details on the processing adopted by the data producers and the quality product assessment see Volpe et al (2012) and http://marine.copernicus.eu/documents/QUID/ CMEMS-OC-QUID-009-038to045-071-073-078-079-095-096. pdf.…”

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).…”

MERIS Phytoplankton Time Series Products from the SW Iberian Peninsula (Sagres) Using Seasonal-Trend Decomposition Based on Loess

“…There are studies (e.g., Vantrepotte and Mélin [3], Vantrepotte and Mélin [4], Vantrepotte and Mélin (2009) [46], and Volpe et al (2012) [10]) that remove these observations from the data, whereas STL solve this situation without reducing the dataset. Moreover, an analysis has been performed that detects the dominant components in the time series, using a statistical dispersion measure that is not influenced by outliers.…”

“…However, these classical methodologies do not allow for a flexible specification of the seasonal component, whilst the trend component is represented by a deterministic function of time that is easily affected by atypical outlying observations. Vantrepotte and Mélin (2010) [3], Vantrepotte and Mélin (2011) [4] and Volpe et al (2012) [10] have avoided these limitations to their studies of time series for remote sensing data by removing atypical observations. The ideal method should allow for variations in the seasonal pattern and should be robust in the presence of outlying observations.…”